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Joseph Barron
Joseph Barron

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How Software Development is Changing Forever, and How You'll Need to Change With It

I'm sorry, you'll probably find this is a really long article. From the title, you probably think I'm either about to about to profess the infinite glory of AI — or proclaim how it will gradually end the world and take your job.

But I promise that's not what this is. In fact, since you're reading this here on Dev.to, I promise to tell you why you're likely to do well in a post-AI world.


For as long as programming has existed, one thing has remained constant: it’s always been about writing code. Despite how computing has changed over decades, one thing has remained the same... we write code, and the machine follows our instructions.

Today, tools like Copilot, Cursor, and ChatGPT are turning that process on its head. AI doesn’t just follow instructions; it helps create them. You describe what you need, and it spits out code that gets you halfway there—sometimes further.

It feels unbelievable.

But it raises unsettling questions. If AI gets better at writing code, what’s left for developers? Is this the end of a decades-old career—or the beginning of something far greater?

The best programmers I know are not intimidated by the hallucination-riddled outputs of ChatGPT and Claude. But the history of computing suggests that these programmers shouldn't be so confident that the world won't demand that they adapt anyways.

The Next Big Abstraction in Computing

Each shift in the evolution of how we produce software has been a story of abstraction. The amount of data needed to build an application has not decreased over time, it's gotten larger. But the words we use to describe that data have become more expressive and succinct.

  • Assembly Language: No more punch card systems or painstakingly producing machine code by hand. With assembly, developers could write instructions using a more human-readable syntax, abstracting away the binary intricacies of hardware. This meant they could shift focus from managing individual bytes to designing and building more complex systems—operating systems, rudimentary user interfaces, and early software applications. It was a big first step toward programming as we know it today: enabling humans to communicate with machines without needing to speak like one.

  • Compiled Languages: Portable, high-level code revolutionized programming. Instead of writing instructions specific to a machine’s hardware, developers could write code in high-level languages like FORTRAN or C, which compilers translated into optimized machine code. This abstraction allowed developers to focus on solving complex problems rather than worrying about hardware-specific details. The result? The rise of portable, scalable applications—from enterprise systems to the software that powers our everyday lives. It wasn’t just about making programming easier; it was about opening the door to larger, more ambitious projects that transformed industries.

  • Interpreted Languages: Languages like Python and JavaScript took abstraction a step further, prioritizing ease of use, flexibility, and platform independence. With interpreted languages, developers didn’t need to compile code into machine instructions—they could write and run programs dynamically, often with instant feedback. This democratized programming, making it accessible to people who didn’t have traditional technical backgrounds. Despite being notorious for quirks and inefficiencies, these languages fueled the web and app revolutions, enabling entire ecosystems of innovation. Today, countless communities thrive around building micro-SaaS apps, games, and experimental projects that wouldn’t have been possible without these accessible tools.

If history has taught us anything, it’s that new tools don’t eliminate jobs—they create them.

A guy in 1957:

A guy in 1957: "A compiler could never outperform my hand-crafted machine code."

Programming originally meant managing every detail—manually allocating memory, writing low-level code tailored to specific hardware, and understanding every intricacy of the machine. A skillset that was incredibly niche and difficult to acquire. Today's students can effortlessly render their React pièce de résistance in any environment that can run a browser, without worrying about the nitty gritty details. Each leap in abstraction has not only made programming complex systems easier but has also unlocked countless new opportunities, expanding what’s possible with far less effort.

I believe AI-assisted coding fits neatly into this ongoing story of abstractions. Each transition in the evolution of programming has shared a central theme: that code can be represented as data. Every paradigm shift has been about abstracting how we describe and manipulate that data. Large language models take this abstraction one step further: they allow us to represent code (and anything else generative AI can produce) in the form of natural language.

  • Natural Language: Generative LLMs have unlocked a form of software development that was previously relegated to niche artisan programming languages: the ability to build applications using plain English. Well, almost. While these models can “autocomplete” their way to appearing technically proficient, achieving reliable and precise outputs still requires significant technical expertise. However, their advancing capabilities give us a preview of an inevitable future where natural language programming is the de-facto way to produce software.

What Changes for Developers?

Unlike many of the previous paradigm shifts in computing, AI is not just abstracting code... it is also abstracting effort itself. A strong developer can describe in detail what they need, and an AI can nearly handle the rest. "Agentic" tools are becoming capable of brainstorming and planning somewhat autonomously—at least, they give the appearance of doing so. The code these programs write won't necessarily pass the sniff test of the top programmers (in fact, the code might not even run), but it doesn't seem like we're far away from getting consistent and reliable outputs.

AI also isn’t a deterministic tool in the toolbox, like a compiler. There's a degree of chaos and unpredictably that, when combined with its approximate knowledge of many things, enables it to act as a pretty good collaborator. Even if you don't use the code it writes directly, you might find it's a wonderful rubber-duck, capable of bouncing ideas back and forth, willing to iterate on any ridiculous thought you have. Its ability to parse large amounts of documentation quickly is useful when researching how to go about building a system. And that means the role of the developer will evolve in a new way.

In the future, the developers with the highest job security won’t necessarily be the ones most deeply familiar with language features, APIs, and libraries. AI—even in its current impressive-but-primitive state—can already approximate much of that information with surprising accuracy.

Instead, the developers who stand to gain the most will be those who excel at the things AI will (hopefully) struggle with for a long time. AI, at least as of today, cannot hold an entire business context in its memory. It doesn’t understand the domain. It doesn’t grasp the customer, the roadmap, the budget, the politics, the culture, the positioning, the strategy—or the team’s past failures and victories. It doesn’t exist in the world, building relationships with the people navigating the ship it’s been prompted to help steer. And despite its generative capabilities, it has little meaningful awareness of the industry it’s operating in.

ChatGPT's weakness is it doesn't comment on Jeff-the-product-director's cool leather jacket to build rapport.

ChatGPT's weakness is it doesn't comment on Jeff-the-product-director's cool leather jacket to build rapport.

The developer who seeks to exploit this inherent weakness in AI's ability to build and understand complex relationships will shift their priorities. It will no longer be worth exerting so much effort to write code, because large language models will eventually sufficiently abstract the production of reliable code via natural language. From a programming perspective, it will suffice for the developer to be just expert enough within their technical domain to ensure that the machine does not make any critical technical mistakes.

More importantly, the developer who is adaptable will leverage their deep understanding of the business domain—an area where AI simply has no ability to compete, at least for the time being. The developer who has genuine interest and empathy for the end-users, the business, and the people working in it, will use AI to build the most effective solutions with 1/10th the effort compared to those who staunchly remain traditional coders—who, in the eyes of the business, will miss the forest for the trees pursuing the dopamine-rush of a puzzle well-solved by their own hands.

The developers who thrive will be the ones who learn how to collaborate with AI, taking advantage of all of its strengths and its weaknesses.

What Skills Will Developers Need?

If AI shifts the nature of programming from "writing code" to "articulating solutions," then the skills developers value today will evolve to universally encompass the soft-skills honed by those who've had time to build software engineering experience in the pre-AI corporate world.

Initially, the safest group will be senior engineers and managers already celebrated for delivering measurable business impact. They will reap the early benefits of this shift, standing just inches from the sheer edge of a suddenly expanding value chasm that separates them from the incoming junior developers who have had no opportunity to build technical experience or domain knowledge. These newcomers have the grave misfortune of trying to cross a bridge that is suddenly falling apart beneath them.

But even senior engineers won’t be safe forever. The critical move for senior and junior engineers alike will be to lean heavily on their unique strengths over AI, and offload everything else.

The ability to articulate a problem clearly and break it down into actionable logical steps will be a key differentiating skill. AI can generate code, but it can’t understand ambiguous or poorly framed requirements. Developers must be highly effective translators of business needs into prompts that AI can act on effectively. They must provide the right amount of technical context so as to not let the AI run amok by either under- or over-engineering a terrible implementation.

While AI can write individual components of a system, it can’t yet design how all those parts fit together, at least very well. In my personal experience, it loses sight of the bigger picture quickly, and is an indecisive pushover when evaluating important decisions. Developers will need to focus on architecture and integration, ensuring that the pieces generated by AI fit within the broader system and align with organizational goals.

We need to refactor this rectangle right here, or we all die instantly.

We need to refactor this rectangle right here, or we all die instantly.

Developers will need to rigorously evaluate and refine AI outputs, ensuring they are efficient, secure, and aligned with business objectives. This will involve a mix of technical expertise and gut intuition honed through experience with production systems.

The developer who builds trust with stakeholders, understands user pain points, and advocates for solutions that align with company values will have a clear edge. These are not skills an AI can replicate (yet), and they will set the best developers apart. (And arguably, they already do.)

The measure of success as a developer has never been, and still won't be, how many lines of code you can push—it will be how much impact you can drive. The winners in this new landscape will be those who:

  • Ship solutions that actually solve user problems.
  • Build resilient systems that scale with the needs of the business.
  • Use AI as a multiplier, not a crutch.

This is a shift from a craftsman mentality to a strategist mentality. The best developers will spend less time manually refactoring and testing, and more time solving meaningful problems. The skill of traditionally writing code will become less and less intrinsically valuable, except in cases where peak technical performance is vital to the business or product strategy.

AI Will Replace Developers Who Don't Adapt

Here’s the truth: the role of developers isn’t going away, but what developers do day-to-day will change dramatically. The tasks that are repetitive, time-consuming, and easily described will be automated. That’s not a threat—it’s an opportunity.

Developers who embrace this change will have more time to focus on things that truly matter. Crafting seamless experiences. Discovering innovative solutions. Solving real world problems.

But those who resist—who double down on manual processes or dismiss the potential of these tools—risk being left behind. AI isn’t waiting for anyone to catch up. If you find yourself scoffing at the imperfect code AI generates today, remember: there was once a great programmer who believed they would always write better assembly than a compiler. We know how that story ended.

The beauty of this shift is that it lowers barriers and opens the door for anyone willing to learn. Generative AI is a tool—not a replacement—and like every paradigm shift before it, those who embrace it will amplify their problem-solving capabilities. Whether you’re a junior developer just starting out or a seasoned architect, AI offers a way to do more, faster, and with greater focus on what truly matters.

But succeeding in this new era requires adaptation. You can’t approach AI as just another tool for automation. It’s a collaborator. Learn how to guide it effectively—how to frame problems, validate outputs, and iterate on solutions. Shift your focus from the mechanics of coding to the broader picture: understanding users, designing systems, and aligning with strategy.

AI is abstracting effort itself, freeing developers to put their energy where it counts: driving real impact. If you can master this transition, you won’t just keep up—you’ll be able to change the world.

AI Will Amplify Builders—But Replace Manual Work

The highest-performing developers won’t just write code; they’ll use AI to redefine how businesses operate. By leveraging their technical skills in collaboration with AI, they’ll automate the definition and translation of business and user needs, analyze unstructured data to uncover opportunities, and pinpoint inefficiencies in processes. These developers won’t just automate workflows—they’ll effectively automate the organization itself.

It’s not far-fetched to imagine Sam Altman’s prediction of a one-person unicorn becoming reality within the next decade.

For those in non-technical roles, this should be a wake-up call. Developers using AI won’t just stick to their traditional domains—they’ll expand into others, from marketing and operations to design and even product leadership. Not maliciously, and not intentionally to displace people, but because AI will give them (or the companies that employ them) the tools to radically change manual processes that were once untouchable.

What If I’m Not a Developer?

The solution is simple: learn to build. Not necessarily to code like a software engineer, but to design and build systems that solve problems. Start now, before the gap widens.

AI is transforming the nature of work. Any process that can be automated profitably, likely will be. This isn’t speculation—it’s the trajectory of technological progress. The people who understand how to create, build, and solve problems with these tools will race ahead, automating tasks, increasing productivity, and unlocking entirely new opportunities.

If you’re in a non-technical role, you might think: "I don’t need to learn to code. AI will handle it for me." That’s partially true—AI tools are lowering the barriers to entry and making it easier to get started. But the people who thrive in this era will be the ones who understand and are well-practiced in the fundamentals of how to build a solution from nothing.

AI isn’t magic. It doesn’t understand your business, your customers, or your goals. It still relies on human input—on people who can frame problems, validate outputs, and integrate solutions. Developers already know how to do this, and they’re collaborating with AI to push boundaries. You can do it too—it just takes practice and a willingness to adapt.


Thanks for reading my first article on Dev.to. I genuinely believe the future is bright for all professionals who embrace AI—not just as a tool, but as a partner in solving meaningful problems.

This paradigm shift isn’t the end of programmers, but it's likely the beginning of a new era of programming. Careers will evolve rapidly. The only constant is change. The key to thriving in this world is adaptability.

Go build something. It doesn’t matter if you’re an experienced developer or someone who’s never written a line of code. Spend an hour exploring what’s possible. Use AI to solve a problem, automate a tedious task, or create something just for fun.

The people out there experimenting, learning, and building today—whether they have technical experience or not—are the ones who will thrive tomorrow.

Be one of them.

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Top comments (13)

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brense profile image
Rense Bakker

I disagree, I think it's the other way around. AI will get better at interpreting more business data and context and hallucinate more and more when writing large pieces of code.

AI mimics an artist, not an engineer. It will generate new output everytime, even if it's nonsense (hallucination). Good engineers, given the same input parameters, will produce the same output. I think the current LLMs fundamentally lack the capability to mimic the process that an engineer goes through when writing code.

Therefore I don't see AI evolving past a companion/assistant, that will greatly increase the speed at which we write code and help us figure things out faster (due to more direct access to context). I don't think it will ever be capable of writing good software (with predictable outcome) unassisted.

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ozzythegiant profile image
Oziel Perez

I don't even trust AI to kickstart a project. Sorry but this whole AI trend is just a bubble that's going to pop if people don't stop asking it to do everything. At best it's a content generator with a glorified chat interface. We are lying to ourselves thinking that it will be able to solve complex engineering problems. Only humans have that capability

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nurhusien1970 profile image
Omar N Idris

What we need is an AI that can write instructions for your code. The problem is English language is either a barrier or lacks specifics. I once tried it to do a simple code with rectangles and circles inside it and constantly bouncing back and it was hard to tell it to position this or that and you have to tinker with the code. To use AI you need to write better English. You need to write a better code. This takes time and training to fix AI mistakes. However, I see that AI will be great if say we have context based AI. For example if you have excel data its analysis is just a click. I just analyzed a genetic data.
So for highly predictable softwares it is the way to go.

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fernal73 profile image
Linus Fernandes

I think what you're suggesting is that if developers are not building products that people or businesses use, they won't have jobs anymore. But this holds for a majority of software developers. Are they all to become product managers or open source maintainers? How realistic is that?

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orlando_villa_86bd0db6d42 profile image
orlando villa • Edited

It is all educated guesses.

  1. Today AI is just another tool with particular benefits and weaknesses
  2. It will certainly evolve/improve
  3. How much and what will be the benefit level?... no one knows for sure.
  4. Embrace the tool, learn to work with it. Worse case scenario you will have another tool under your belt.

People, learn to say "I don't know" when you don't really know. I think the future will surprise most of us.

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chris_sd_b9194652dbd4a1e profile image
Chris S-D

Having jumped into AI I can say, with some certainty, that you're absolutely right about AI not going away.

The problem with hallucinations is declining and we are seeing improvements in the way we build models.

I think people who fall into the product owner role or something similar like business analyst will likely be in the best position to harness this technology. They are typically the ones who take on the role of transforming customer requirements into meaningful deliverables, and AI will even help them do that better.

One issue, however, is that many of the people who become developers are introverts. They don't like dealing with others and frequently the sentiment is mutual (hence product owners).

These folks likely make up a majority of the software engineers and yes, I think AI might very likely put them out of a job if they can't pivot effectively. Luckily, if they can leverage AI for interaction through email and chat messages, this might not be to horrible. AI can help them write in a way that better targets their audience and may help them communicate better. Face-to-face, however, may still be a challenge (luckily we have lots more millennials and gen-z entering the workforce who primarily communicate via electronics, so maybe that won't be a big problem).

One thing that does set AI apart from previous abstractions, however, is it's ability to work autonomously.

Sure, right now it lacks lots of information and context, etc, but we'll get to past that. As AI gets more feedback loops from the world around it, it's knowledge will improve. It's ability to recognize patterns that don't work will improve. It will be able to self improve and unlike human beings, it will be able to replicate that improvement to all related AI models simultaneously.

I think we're still a year or two away from it getting to be that self-sustaining, but we're really close.

Once it does, if you think the technology is moving fast now, just wait.

The singularity is here, or at least in the driveway.

Good news. I think things like UBI will become a necessity for society. I think a lot of the things we used to have to do will be gone. We're all going into early retirement, and finally, some of us will be young enough to really enjoy it.

I think the big paradigm shift here will end up being what society looks like after this shift. We all know the problems with communism and socialism, but AI may render many of those issues moot.

Why is it so important for people to work. The idea of them simply mooching off society will be meaningless.

And some people will say, well we need work to find meaning in our lives. I say, fine, work then. There will be nothing stopping you. If you like programming, then program. Now you'll be able to program what you want, the way you want to do it and it won't cost you your job if you don't do it exactly right. If you like to make things out of wood, knock yourself out. If you love looking at art, look at art.

But, you'll be able to do things that you never could before. You'll be able to make entire movies by yourself. In short order, those movies will be immersive. You'll be able to be in those movies and maybe even interact with people.

The skies the limit and if you're the only person who cares about it, it won't even matter (well, perhaps your self esteem will take a hit, but other than that).

I'm really excited about the possibilities.

Will there be some really bad stuff that comes along with all this. ... Puhleeeease, of course there will. And we only have the slightest idea of what some of those issues will look like. Some of the issues we are seeing now will become moot in time as well. For instance, there likely won't be as much value in stealing someone's life fortune, and the chance of you getting caught doing it will be higher.

At this point, I can only say, I'm so curious where this will all go. I'm usually pretty good at identifying trends and to some extent, predicting the future. AI has made this an extreme challenge for me. At this point I have a better idea of what's not likely to happen than what will. It looks likely to be a net positive after we finally make it through the initial transition that I'm certain will be chaos.

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robert_reppel_3b0aca429d9 profile image
Robert Reppel

I agree. I think "Articulating solutions" has been the game all along. It's just that until now the process of getting that into the code was hopelessly clumsy and needed armies of coders herded by projectmanagerscrummastermiddlemanagers. Even with hallucinations it looks like lots of low hanging fruit to pick for those who adapt. Understanding the business and communicating well was always what mattered.

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bryant_93398a46c883330af1 profile image
Bryant

I think you give the best advice for existing coders to keep their jobs for the longest time possible. One very notable omission to this story though is that as AI improves it's going to lower the entry barrier to coding which means more competition with soft skills being more important than hard skills. Most coders aren't great a soft skills, so others will likely be able to outshine them. Increased competition from outside the traditional pool is going to cause salaries to decline. This effect will increase as the barriers lower until they eventually vanish entirely. In the end, coding will be not much different than going to Suno and telling it what song to make, anyone could do it.
On the bright side, this is the future of all jobs, so it's not just coders that are going to get the shaft. The bad news is that this means wage normalization to some degree across the board since pretty much anyone will be able to do any job (eventually). This also means the wealth gap will widen. When AI is able to fully replace humans then wages will go down even more as humans have to compete with AI and robotics for jobs. Tech only ever gets cheaper, faster, and more efficient, so this will happen year over year until salaries approach zero.
Obviously this isn't a sustainable model. Our economy works great in a world where human labor is necessary, but once we have sufficiently advanced AI it becomes a dystopian nightmare. Economists need to be coming up with better solutions for a post-labor economy that limits access to finite resources without incentivizing work since there will be so few jobs that the few that exist could be done by those with a passion for it.

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saileshvyas profile image
saileshvyas

Excellent insights. AI gives me hopes as someone who is a non-techie, making possible opportunities that I would have otherwise struggled with. Fear or face AI - I'm facing and embracing it.

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michaeldhead profile image
Michael Head

You are spot on. AI is here to stay. Learn to adapt or get left behind. Start now and grow with AI instead of trying to catch up down the road.

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vijay_sf_24f69e55a29344c9 profile image
Vijay sf • Edited

It's here to stay if users use it.. it learns and evolves through interactions, to slowly become what users can do and replace them, it'll not be limited to coding added with robotics, it'll be able to all jobs.. that's why the nobel prize winners and creators of neural networks expressed the thought they themselves put humanity in danger.

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