Does the software developer role still have a future?

Introduction

I ask myself this very simply because this is my official job. I am a software developer. So I am not talking about an abstract topic. I am talking about my everyday work. Since AI tools really settled into work, one thing has become very clear to me: some tasks move much faster. Things that used to take days, weeks or months can now get underway much more quickly. On some projects, I feel as though I am saving an enormous amount of time. At times, one person can already go a very long way where it once took a whole team and a much bigger budget. I am thinking in particular of projects where you need to structure information, prepare logic, organise data or build a solid first base. On a subject like a Wikidata-related application, for example, you can see very quickly that AI helps get things moving, test an idea, sketch out a plan or propose a first version. It is not magic. But it is already huge. So when you look at that, the question comes straight away: does the software developer role still have a future? My answer is simple: yes, but not in the same form as before. The role is changing. It is shifting. It is becoming more strategic, more demanding in its understanding of context, more attentive to quality, and more human in what it really asks of us. AI speeds up the work, but it does not replace judgement, method or responsibility.

Artificial intelligence changes the speed, not everything else

The first visible effect of AI is speed. Today, we can prototype an idea more quickly. We can launch an app base, a website, a blog, a business tool, a small feature, documentation, or even a set of presentation materials. What used to take weeks can sometimes be started in hours. That is not trivial. When you work in IT, it completely changes how you organise yourself. I see it as a change of pace. Before, we spent a lot of time laying the foundations. Now, part of that work can be assisted, accelerated, or even prepared by a tool. We no longer start from zero with the same workload. We start with a base, a proposal, a first draft. And that, frankly, changes everything. But I want to be clear: moving fast does not mean trying to understand everything. A tool can produce text, code, a mock-up or an idea for a structure. It does not automatically understand why we are doing things, the context in which we are doing them, or what we want to avoid. The tool does not see team constraints, working habits, technical debt or the effects of a poor architectural choice on its own. There again, I see an important difference between speed and relevance. AI can propose. It can accelerate. It can even impress. But it does not decide for me. It does not carry the responsibility for the final product on my behalf. In practice, that means something very simple: the more powerful the tool becomes, the more solid the human judgement has to be.

Developing is not just writing code

I think there is a fairly common misunderstanding about what a developer is. People often imagine someone spending the day typing lines of code. In reality, that is only part of the job. The real work often starts before the first line is written. You have to understand the need. You have to ask the right questions. You have to turn an intention into a technical solution. You have to choose between several paths. You have to think ahead about how the system will live over time. A good developer does not just make something work once. They also think about what happens afterwards: maintenance, updates, tests, readability, security, robustness, and the ability to fix things later without breaking everything. That is where the real value lies. Not just in production speed. And the more tools automate the visible part of the work, the more important that invisible part becomes. Understanding a need, keeping the overall shape coherent, avoiding muddle, explaining clearly what we are doing: all of that becomes more valuable. I would say that the useful developer is not the one who produces the fastest, but the one who understands needs most accurately. When I work on a concrete subject, I can see that code is only part of the problem, simplified by AI. There is always business logic behind it. There are constraints. There are trade-offs. There are people who will use what is produced. And if we forget that, we end up with something technical, but not necessarily something valuable.

Consolidating the foundations

There are fundamentals we cannot throw away. Understanding logic, data structures, the way an application is organised, tests, maintainability and security: all of that remains essential. AI can help. It does not replace solid foundations. When I talk about foundations, I am also talking about intellectual discipline. You have to know how to read a problem, break it into parts, check it, then build it. That sounds simple when put like that. In reality, that is often where the difference lies between quality work and work that is of little use.

Checking systematically

The faster a tool gets, the more we need to check behind it. The time saved in generation can disappear very quickly if we do not organise ourselves. A response that looks correct can hide an error, an approximation or a poor choice. That is why the profession still has a control dimension. We cannot hand everything over to the machine. I even think that management is becoming a real skill in its own right. It is no longer enough to have a tool that suggests something. You have to know how to compare, test, correct and decide. It is another form of work, not a disappearance of work.

Keeping the business context

Code never exists on its own. It serves a client, a team, a use, an audience. It answers a real problem. If you forget the context, you produce something technical, but not necessarily something useful. I think that is where the developer still has a real edge: they know how to connect the technical side to the real need. And that applies in many situations. The same tool can look brilliant on paper and become bad the moment you have to use it in the real world. Context is often what changes everything.

The real issue is vocabulary and clarity

I see it more and more: vocabulary makes a huge difference. When you know how to name what you want properly, you get better results. When you know how to explain a problem, understand a request, specify the tone, format, constraint and objective, you save time. That is true with AI. It is true in a team as well. In my view, this is almost a central skill today: being able to think clearly. The clearer you are, the more useful the tools are in response. The vaguer you are, the vaguer the answer you get. You can see a simplified example. If I just ask, “Make me a presentation website for my XYZ activity”, I will get an average result. If I say, “Make me a simple homepage with this objective, this audience, this tone and this technical constraint”, the result will be much closer to what I expect. It is the same logic for an article, a technical sheet, an application or an automation. And this is not only a question of technique. It is also a question of everyday language. The simpler we can say what we want, the faster we move. The more we get lost in vague formulations, the more we go round in circles. I also think of another point: vocabulary helps us think. When we have the right words, we see things more clearly. We see the steps better. We see the traps better. We see the structure better. That is why I think the software developer role will not disappear. It is becoming more demanding in terms of formulation, structure and the ability to turn a fuzzy idea into a clear product. And that goes further than a simple prompt. We can structure a book, a dossier, documentation, a presentation, or a set of content with a logic of agents and subtasks. But here again, the essential point remains the same: the tool does not replace the original objective. It sets it up according to our skills and our means. Ultimately, the more I look at these AI tools, the more I think the real advantage does not come only from the machine. It comes from the person who knows how to give it the right instructions.

IT is above all information

At the end of the day, being a computer professional is not only about knowing a computer or software. It is mainly about knowing how to organise information. That is why I think the term software developer can be misleading if we reduce it to pure technique. A developer does not work only on machines. They work on the way information flows, appears, is understood and changes. Today, you can even do part of the work from a smartphone. That is true. But personally, I still prefer a computer with proper screens. When you need to think, compare, reread and keep an overview, the workstation matters a lot. The best tool is not always the simplest or the fastest. It is often the one that makes the technical side easier to manage. Over time, I also notice that we become more effective when our equipment is well set up. A large screen, a good file structure, simple tools, regular habits: these are very concrete things. You can do a great deal with relatively little nowadays. That idea seems important to me. Because if you understand that IT is about structuring information, then you also understand why AI changes the context so much. It makes structuring information more accessible. AI makes technical barriers easier to overcome. People who were not computer specialists can already launch a project, make a mock-up, create a first application, produce content, test a concept or prepare a working base. That does not mean they become experts overnight. But it does mean they can move much faster than before. And that, for the profession, is a major evolution.

What AI really democratises

I think one of the big changes in this period is democratisation. Twenty years ago, creating something cost a lot or took a lot of time. You needed a team, rare skills, sometimes a lot of money. Today, part of that work is becoming more accessible. Not free, not effortless. But more accessible. We can create websites, blogs, business tools, images, texts, presentations, videos, animations, games or prototypes. We can also prepare a service idea, a product sheet, a commercial document, training material or a project plan more quickly. The list is long. Whether we are talking about OpenAI, Claude, Gemini or other tools, the real issue is not only the name of the platform. The real issue is how we use it. A good tool asked badly gives an average result. A simple tool used well can already go a very long way. I find that remarkable because we are moving towards a logic of more democratised access to capabilities and knowledge. What used to require a big budget can sometimes be tested more quickly, with fewer means, and therefore by more people. That does not mean everything becomes easy. It means that management outweighs technique. And that is already a lot.

Knowledge for more people

I also believe that knowledge should remain accessible. Knowledge should not be reserved for an elite. When knowledge is easier to understand and easier to obtain, more people have the chance to progress, produce and solve problems. For me, that is very important. AI can help people learn faster, write more quickly, organise an idea, check a point, or imagine a project structure. If used well, it can genuinely provide essential support to people who have very limited means. I am not only talking about a spectacular effect. When you understand better, organise better, and move faster on certain tasks, you keep energy for what matters more.

What changes for freelancers and teams

I work alone in some contexts, like many freelancers or small businesses. And I can see that AI changes the game for that kind of profile. When you are on your own, you can move faster than before. You can test more ideas. You can prepare content, mock-ups, code bases, documents and variations without having to wait for an entire chain of contributors every time. That is very powerful. But I do not think it removes the value of teams at all. On the contrary, as soon as a project grows, the collective becomes central again. You need people who coordinate, people who test, people who design, people who sell, people who document, people who manage. AI can help each of those roles. It does not remove their necessity. In fact, it should make teamwork more necessary. Because when everything moves faster, we have to speak better, understand each other better and make better decisions. We can no longer hide behind technical slowness. I also think about what this changes in the way we work with contractors, agencies or partners. A solo entrepreneur can do much more than before. But they should not try to do everything alone. AI helps, yes. It does not replace professional support. And then there is another reality: in a company, in a project, in a society, the goal is not only to produce work. The goal is also to build links, create a result and learn how to do things together. Here again, technology accelerates. It does not replace relationships. I would even say that the more powerful tools become, the more we need people who can keep a course, divide up tasks and give a shared meaning to what we are doing.

What people say about AI, and what I actually see

We hear a great deal about artificial intelligence. Often, the debate goes in two extreme directions. Either we are told everything will become magical. Or we are told a total catastrophe is coming. In reality, I think the real movement is more nuanced than that. Yes, there can be bubbles. Yes, there can be excesses. We have already seen that with the internet. At the beginning, a lot of websites were overvalued. Some companies went bust through overreach. Others disappeared. But the fundamental principles stayed the same. The internet stayed. It even changed many lives. For AI, I see a fairly similar logic. There may be surges, huge investments, business models still looking for their balance. But that does not mean the technology has no future. On the contrary. It simply means we are still in a strong phase of evolution. I also find it interesting to look at what AI already does outside code. It already helps us write, summarise, structure, imagine, create, prepare content and speed up reflection. It can help write a book, make an action plan, build an article, or produce information. We also see uses in marketing, video, image creation, document formatting or the preparation of communication materials. All of that is happening at once. But we need to stay clear-eyed: just because a tool generates something quickly does not mean the result is good. We still have to know what to ask for, what to keep, what to clean up and what to correct. That is where professional skill remains essential. You could almost sum it up like this: we often overestimate what technology will do tomorrow morning, and underestimate what it will change in ten years.

The real limits to keep in mind

I am not the kind of person who believes everything is possible, right now, without limits. Tools have compute limits. They have cost limits. They have language limits. They have context limits. They also have privacy limits. That is not a minor point. When you work seriously, you have to know the context, what you are sharing, how, and why. There is also the question of quality. A tool can give the impression that it is doing well. But if we do not check, we can easily miss an error or an approximation. That is why I always come back to the same idea: the software developer profession, like many knowledge professions, is still a profession with a future. People also talk about quantum computing. It is an interesting subject. Very promising. It is another step, not a magic wand. We easily mix research, communication, futurism and science fiction. And science fiction is a good example of what we need to distinguish. There are films that imagine the cybernetic enhancement of the human body, chips, prostheses, technical extensions, machines merging with people. Some things already exist, at least in medical form. Others remain highly speculative or forbidden. We need to make those distinctions. I think we have to stay realistic. We can admire technology without telling ourselves stories. We can recognise progress without fantasising about everything or becoming frightened by it. That is how we keep our feet on the ground. There is also a simple point not to forget: privacy. When you use an intelligent tool for work, you need a minimum of caution. Not everything should be put anywhere. Not everything should be copied without thought. Here again, common sense matters.

What I hope this transformation will bring

At heart, what interests me most is not just the technology. It is the impact technology can have on the lives of people who have access to AI. If AI can save time, then perhaps it can also allow us to spend more time with our family, friends, colleagues, work, health, or simply our quality of life. That, for me, is essential. Saving time so that we can work more is not the only goal. Saving time so that we can live better is much more interesting. I also think about those who have very limited means. If knowledge becomes affordable, if tools become more accessible, if a modest budget is enough to launch a useful project, then AI opens doors. Not all of them. Not for everyone, not instantly. We often forget that many people live with very real constraints: a roof, bills, food, health, transport, lack of time, lack of energy. AI does not solve all of that. But it can help people organise themselves better, learn better, produce better and decide better. And there is something else I notice: a person often changes roles several times in a lifetime. It is not just about changing companies. Sometimes it means changing profession, changing how you work, changing tools, changing work pace. The more the world moves forward, the more this ability to adapt matters. I do not think we should be afraid of that movement. I think we should accompany it. Keep our foundations. Learn how to use the new tools. Accept that we need to adapt. And above all, do not let ourselves get trapped in a single way of doing things.

So, does the software developer role still have a future?

Yes, clearly, it has a future, but it is evolving. Tomorrow’s developer will not simply be the person who types code. They will be the person who understands the need, keeps the context in mind, asks the right questions, checks the results, knows how to speak plainly and knows how to use tools intelligently. I even think that the more powerful tools become, the more important the quality of thought becomes. The ability to explain, choose, simplify, reread, correct and decide becomes central. AI can produce quickly. It does not replace the ability to manage. If you already work in IT, I think the right response is not to panic. The right response is to learn how to use these tools well, without losing your foundations. If you do not work in IT, then take this away: understanding AI tools, learning how to use them and organising yourself well is becoming a major skill in many jobs. I would even say that the real profession of tomorrow, in many fields, will be to connect a market need with ever-growing technical power. That link needs a team to build it. And that team needs to be built through relationships. For me, the software developer role therefore has a future. Not as before. Not in the same form. But it has a future, because there is still a need to understand, organise, verify and build useful projects. The real difference tomorrow will be between those who use AI and those who do not. It will not replace the importance of human relationships.