My approach is to "ride the wave", don't resist, just try to use it as an advantage and if not possible, adapt and look where I can keep adding value to the world.
Currently I'm trying to get my own company started because that way I can iterate the product faster using AI and if it goes well I can slowly migrate from coder to manager. I've come to terms that my days as a pure coder are coming to an end but I've been coding for at least 15 years.
The only way to be bulletproof is to be the person who takes ownership. AI can generate an app, but it can't answer to a court, clients, or the CTO when the database crashes on Black Friday.
Shift from writing code to risk management. Architecture, security, complex legacy integrations, and distributed systems debugging are zones where the cost of error is high, and where AI still operates like a random number generator. You need to be the one who knows why the system works, not just the one who writes the syntax
I don't think there truly is a way to bulletproof yourself
Anything that AI can't do today could be convincingly argued that it will be able to do in 3 years. There's no-one that can tell you what the world will look like then
If you're seriously worried and can't take that risk then maybe look into switching careers
Learn to sell yourself. Learn to adapt quickly. Learn to learn
The days of having one skillset you learn deeply and monetize for 40 years are over
> The days of having one skillset you learn deeply and monetize for 40 years are over
This is a commonly held view - particularly among employers. And certainly some people can do it.
But the problem is that it takes time to build expertise and bargain that into something you can live on. And the more times you have to do that, the more you fall behind people who found a niche or a long-term role and stayed in it.
And you eventually find yourself in your late fifties, steamrollered by yet another wave of change, and you don't have the time or resources or energy, or just plain self-belief, to adapt again.
I am 30 years in professionally in June and 10 years as a hobbyist before that. I first started programming in 86 in assembly language
I’ve kept up with every wave in technology from 1996 when I was still programming ok mainframes, through web, a brief stint on mobile. “Full Stack development” (even though I suck at the web side), and since 2018 app dev + cloud including four years working at AWS.
I had three offers within 3 weeks after being Amazoned when I was 49 in 2023 and 1 offer in two weeks in 2024.
See my other comment. One thing AI can’t do is talk to people and deal with XYProblems, organizational complexity, egos, turf wars, teasing out the “what”
Let me ask: in what way have you been proven wrong?
Have you simply just seen it build a pretty CRUD application that you feel like would take you many hours to do on your own? If that's the case, then you really shouldn't be too worried about that. Being able to build a CRUD application is not what you're getting paid for.
If you've seen something that involves genuinely emulating the essence of a software engineer (i.e., knowing what users want and need), then I'd ask you to show me that example.
Knowing what users want and need is more the essence of a product manager, not a software engineer.
Software engineering is solving problems given a set of requirements, and determining the value, need and natural constraints of those requirements in a given system. Understanding users is a task interfaces with software engineering but is more on the "find any way to get this done" axis of value rather than the "here is how we will get it done" one.
I'd say what OP is referencing is that LLM's are increasingly adept at writing software that fulfills a set of requirements, with the prompter acting as a product manager. This devalues software engineers in that many somewhat difficult technical tasks, once the sole domain of SWEs is not commodified via agentic coding tools.
That's a dangerous distinction in the AI era. If you reduce your work to solving problems given a set of requirements, you put yourself in direct competition with agents. LLMs are perfect for taking a clear spec and outputting code. A "pure" engineer who refuses to understand the product and the user risks becoming just middleware between the PM and the AI. In the future, the lines between PM and Tech Lead will blur, and the engineers who survive will be those who can not only "do as told" but propose "how to do it better for the business"
> Software engineering is solving problems given a set of requirements, and determining the value, need and natural constraints of those requirements in a given system
That’s the description of a mid level code monkey according to every tech company with leveling guidelines and easily outsourced and commoditized before the age of AI.
Great, then we can use AI to solve the problems given a set of requirements, and spend more time thinking about what the requirements are by understanding the users.
PM and software development will converge more and more as AI gets better.
The best PMs will be the ones who can understand customers and create low-fidelity prototypes or even "good enough" vibe coded solutions to customers
The best engineers will be the ones who use their fleet of subagents to work on the "correct" requirements, by understanding their customers
At the end of the day, we are using software to solve people's problems. Those who understand that, and have skills around diving in and navigating people's problems will come out ahead
(Yes I know the trend I’m about to talk about was different in BigTech and adjacent. I did an almost 4 year stint at a FAANG 2 years ago and have no need or desire to go back at 51. That’s not where most developers work)
Back around 2013-2014 well before AI, I saw the trend that it wasn’t going to take much to be a “good enough” enterprise “full stack” developer meaning the market was going to be commoditized and if i was just a “I codez good and pull tickets off the board” developer, it was going to be hard to stand out from the crowd.
I was prescient, when companies reach out to me now for standard enterprise dev jobs in Atlanta (no longer live there but a large part of my network is still there) I see the same salaries I saw in 2016.
I worked over the next six years to move “up the stack”. I learned soft skills, learned AWS and how to treat it more than just a glorified Colo, learned how to lead projects, talk to the “business”, focused on business value, started getting closer to sales and how they operate etc.
Out of everything I said, the take away should not be to “learn cloud”. Everyone and their dog knows AWS (except for one niche that has opened a few doors to me).
The take away is to incease the “scope and impact” of your work beyond just being a code monkey. Also learn how to work at an increased level of ambiguity.
AI is just another part of the commoditization and devaluation of the generic developer. There is no “moat” around being a generic full stack/mobile/web developer.
As of 2020, I work in cloud consulting specializing in app dev. I have been working full time for consulting departments (the internal one at AWS) and after being Amazoned in late 2023, it took me all of three weeks to have three offers. After I made a bad choice between the three in 2023, it again took me two weeks to have an offer.
I haven’t had to do a coding interview in over a decade because I know how to communicate my value and experience and I don’t try to compete on “I codez real gud”. I’m not a super special snowflake
The truth is that no one really knows.
My approach is to "ride the wave", don't resist, just try to use it as an advantage and if not possible, adapt and look where I can keep adding value to the world.
Currently I'm trying to get my own company started because that way I can iterate the product faster using AI and if it goes well I can slowly migrate from coder to manager. I've come to terms that my days as a pure coder are coming to an end but I've been coding for at least 15 years.
The only way to be bulletproof is to be the person who takes ownership. AI can generate an app, but it can't answer to a court, clients, or the CTO when the database crashes on Black Friday. Shift from writing code to risk management. Architecture, security, complex legacy integrations, and distributed systems debugging are zones where the cost of error is high, and where AI still operates like a random number generator. You need to be the one who knows why the system works, not just the one who writes the syntax
I don’t think anyone can be 100% AI-proof.
My plan is simple: learn the tools, but double down on fundamentals—systems thinking, testing, and owning outcomes.
I don't think there truly is a way to bulletproof yourself
Anything that AI can't do today could be convincingly argued that it will be able to do in 3 years. There's no-one that can tell you what the world will look like then
If you're seriously worried and can't take that risk then maybe look into switching careers
Learn to sell yourself. Learn to adapt quickly. Learn to learn
The days of having one skillset you learn deeply and monetize for 40 years are over
> The days of having one skillset you learn deeply and monetize for 40 years are over
This is a commonly held view - particularly among employers. And certainly some people can do it.
But the problem is that it takes time to build expertise and bargain that into something you can live on. And the more times you have to do that, the more you fall behind people who found a niche or a long-term role and stayed in it.
And you eventually find yourself in your late fifties, steamrollered by yet another wave of change, and you don't have the time or resources or energy, or just plain self-belief, to adapt again.
I am 30 years in professionally in June and 10 years as a hobbyist before that. I first started programming in 86 in assembly language
I’ve kept up with every wave in technology from 1996 when I was still programming ok mainframes, through web, a brief stint on mobile. “Full Stack development” (even though I suck at the web side), and since 2018 app dev + cloud including four years working at AWS.
I had three offers within 3 weeks after being Amazoned when I was 49 in 2023 and 1 offer in two weeks in 2024.
See my other comment. One thing AI can’t do is talk to people and deal with XYProblems, organizational complexity, egos, turf wars, teasing out the “what”
Maybe they can in 3 to 5 years. Then you talk with them in Teams Meetings.
Anything that involves 1) Physical work, 2) Legal requirements and 3) Union should keep you safe for a while.
And if the world devolves into a more chaotic one, I bet they will accelerate the advancement of AI.
Let me ask: in what way have you been proven wrong?
Have you simply just seen it build a pretty CRUD application that you feel like would take you many hours to do on your own? If that's the case, then you really shouldn't be too worried about that. Being able to build a CRUD application is not what you're getting paid for.
If you've seen something that involves genuinely emulating the essence of a software engineer (i.e., knowing what users want and need), then I'd ask you to show me that example.
Knowing what users want and need is more the essence of a product manager, not a software engineer.
Software engineering is solving problems given a set of requirements, and determining the value, need and natural constraints of those requirements in a given system. Understanding users is a task interfaces with software engineering but is more on the "find any way to get this done" axis of value rather than the "here is how we will get it done" one.
I'd say what OP is referencing is that LLM's are increasingly adept at writing software that fulfills a set of requirements, with the prompter acting as a product manager. This devalues software engineers in that many somewhat difficult technical tasks, once the sole domain of SWEs is not commodified via agentic coding tools.
That's a dangerous distinction in the AI era. If you reduce your work to solving problems given a set of requirements, you put yourself in direct competition with agents. LLMs are perfect for taking a clear spec and outputting code. A "pure" engineer who refuses to understand the product and the user risks becoming just middleware between the PM and the AI. In the future, the lines between PM and Tech Lead will blur, and the engineers who survive will be those who can not only "do as told" but propose "how to do it better for the business"
> Software engineering is solving problems given a set of requirements, and determining the value, need and natural constraints of those requirements in a given system
That’s the description of a mid level code monkey according to every tech company with leveling guidelines and easily outsourced and commoditized before the age of AI.
Great, then we can use AI to solve the problems given a set of requirements, and spend more time thinking about what the requirements are by understanding the users.
PM and software development will converge more and more as AI gets better.
The best PMs will be the ones who can understand customers and create low-fidelity prototypes or even "good enough" vibe coded solutions to customers
The best engineers will be the ones who use their fleet of subagents to work on the "correct" requirements, by understanding their customers
At the end of the day, we are using software to solve people's problems. Those who understand that, and have skills around diving in and navigating people's problems will come out ahead
As a dev engineer, I find AI incredibly valuable — vibe coding significantly ramps up my productivity.
(Yes I know the trend I’m about to talk about was different in BigTech and adjacent. I did an almost 4 year stint at a FAANG 2 years ago and have no need or desire to go back at 51. That’s not where most developers work)
Back around 2013-2014 well before AI, I saw the trend that it wasn’t going to take much to be a “good enough” enterprise “full stack” developer meaning the market was going to be commoditized and if i was just a “I codez good and pull tickets off the board” developer, it was going to be hard to stand out from the crowd.
I was prescient, when companies reach out to me now for standard enterprise dev jobs in Atlanta (no longer live there but a large part of my network is still there) I see the same salaries I saw in 2016.
I worked over the next six years to move “up the stack”. I learned soft skills, learned AWS and how to treat it more than just a glorified Colo, learned how to lead projects, talk to the “business”, focused on business value, started getting closer to sales and how they operate etc.
Out of everything I said, the take away should not be to “learn cloud”. Everyone and their dog knows AWS (except for one niche that has opened a few doors to me).
The take away is to incease the “scope and impact” of your work beyond just being a code monkey. Also learn how to work at an increased level of ambiguity.
https://www.levels.fyi/blog/swe-level-framework.html
AI is just another part of the commoditization and devaluation of the generic developer. There is no “moat” around being a generic full stack/mobile/web developer.
As of 2020, I work in cloud consulting specializing in app dev. I have been working full time for consulting departments (the internal one at AWS) and after being Amazoned in late 2023, it took me all of three weeks to have three offers. After I made a bad choice between the three in 2023, it again took me two weeks to have an offer.
I haven’t had to do a coding interview in over a decade because I know how to communicate my value and experience and I don’t try to compete on “I codez real gud”. I’m not a super special snowflake