Today, it may seem that software development is going through something similar to what happened to web publishing after content management systems appeared. AI can already write code, find bugs, build interfaces, connect APIs, and accomplish in a few hours what would have taken a team of developers days or even weeks only a few years ago. From a business perspective, this leads to an entirely reasonable question: why pay expensive, experienced developers if a machine can already do a significant part of their work?<\/p>\n
At the early stages, the argument looks convincing. With AI, it is possible to quickly build a prototype, an MVP<\/a>, or even a fully functional product \u2014 and to do it faster, cheaper, and with a much smaller team. This is precisely where a dangerous illusion begins to emerge: if the product works today, then it must have been built well.<\/p>\n But a software product is not truly tested at launch. It is tested one, two, or five years later \u2014 when new features accumulate, requirements change, traffic grows, security issues emerge, old decisions begin to conflict with new ones, and the codebase becomes so large that nobody remembers why certain decisions were made in the first place.<\/p>\n That is probably when businesses will begin to rediscover a simple truth: writing code and building a reliable software system are not the same thing<\/strong>.<\/p>\n So the real question for the coming years is not whether AI can replace part of a developer\u2019s work<\/a>. It already does. The more interesting question is: how long will it take companies to realize that faster code generation cannot replace engineering experience?<\/strong><\/p>\n It would be a mistake to underestimate what AI can already do<\/a>. Modern AI tools have genuinely transformed software development. They can generate standard business logic in minutes, write APIs, build interfaces, prepare tests, identify obvious bugs, and suggest ready-made solutions to tasks that used to take developers hours.<\/p>\n AI is particularly effective when dealing with problems that have already been solved thousands of times before. User authentication, forms, dashboards, database operations, standard integrations, and admin panels can all be built much faster than they could just a few years ago.<\/p>\n That is why today\u2019s business optimism about AI has a very real foundation.<\/p>\n A small team can now accomplish what previously required several times as many people. In some cases, a startup founder with only basic technical skills can use AI to build the first version of a product almost entirely on their own.<\/p>\n And the problem is not that this approach does not work. Quite the opposite: it often works surprisingly well.<\/strong><\/p>\n The prototype launches, users can register, data is stored in the database, payments go through, the interface looks professional. From the business owner\u2019s perspective, the product exists and does what it is supposed to do. At that point, it is easy to make the next assumption: if AI helped build 80% of the product without a large development team, then the remaining 20% must simply be a little more of the same work. But in software engineering, those last 20% can easily turn out to be more difficult than the first 80%.<\/p>\n That is because the first 80% answers the question: \u201cCan we make this work?\u201d The remaining 20% has to answer very different questions: \u201cWill it keep working reliably? Can we change it safely? Will it scale? And will someone still be able to maintain it three years from now?\u201d<\/p>\n This is the point where the speed of code generation gradually stops being the main advantage.<\/p>\n The main problem with poorly built software is that, at first, it can work almost as well as a well-engineered product. The difference becomes visible only over time.<\/p>\n As long as the product is small, the number of users is limited, and the functionality remains relatively simple, many architectural weaknesses simply do not surface. The system works, the server handles the load, the database responds quickly enough, and new features can still be added without much trouble.<\/p>\n Then the product begins to grow. New modules appear, more integrations are added, user roles become more complex, business requirements change. The system starts processing more data, more transactions, and more edge cases. What began as a relatively simple application can turn into a network of hundreds of interconnected decisions. This is where choices that looked perfectly acceptable during the MVP stage begin to become expensive.<\/p>\n Adding one feature unexpectedly breaks another. A small change to the database requires a complicated migration. A new customer creates a level of load the system was never designed to handle. A third-party API changes, and suddenly an important integration stops working. A security weakness turns out to have been built into the product from the very beginning.<\/p>\n Gradually, the team spends less time building new things and more time dealing with the consequences of old decisions. This is the point where the difference between simply producing working code and doing software engineering becomes obvious.<\/p>\n An experienced developer will not necessarily build the first version of a feature faster than AI. Sometimes the opposite is true. They may spend more time asking questions, thinking about data structures, dependencies, security, scalability, and how the same part of the system may need to change two or three years later.<\/p>\n At the beginning, that caution can look like unnecessary complexity. A few years later, it may be exactly what allows the product to keep evolving instead of being partially or completely rebuilt.<\/p>\n That is why the real consequences of today\u2019s wave of AI-assisted development will not necessarily appear immediately. Thousands of AI-built products may look perfectly successful at launch. The real test begins when they have been in use long enough to accumulate history, dependencies, technical debt, and change.<\/p>\n One of the biggest mistakes in the debate about AI replacing developers is reducing software development to the act of writing code. If that is the definition of the job, then replacement does indeed look increasingly realistic. AI can already generate code faster than a human, and every new generation of models is getting better at it.<\/p>\n But the value of an experienced developer has never been just about how quickly they can produce code. A senior developer is valuable because they have already seen what bad decisions look like after six months, two years, or five years. They know that the easiest solution today can become a serious limitation later. They understand where technical debt is acceptable and where saving a few days of development can eventually cost months of work. Experience means seeing a problem before it becomes a problem.<\/p>\n This is also one of the hardest things for a business to measure. If an experienced engineer prevents an architectural mistake, nobody sees the failure that never happened. If they insist on structuring the data differently, separating responsibilities more carefully, or designing for future growth, the immediate result may simply look like extra time and extra cost. Only later does the value become visible.<\/p>\n AI is very good at answering a clearly formulated question. But software engineering often depends on something more difficult: knowing which question should be asked in the first place.<\/p>\n You can ask an AI model to design an architecture, choose a technology, or suggest a scaling strategy. It can provide several plausible options and explain their advantages. But someone still has to judge which option fits the actual product, its business model, its budget, its team, its users, and its future development. And as AI tools become more powerful, this role may become even more important.<\/p>\n The experienced developer of the near future may spend less time writing routine code and more time defining architecture, reviewing AI-generated solutions, identifying risks, and taking responsibility for the system as a whole.<\/p>\n In other words, AI may take over a growing share of programming work. That does not mean it takes over the role of the engineer.<\/p>\nAI Is Excellent at Building the First 70\u201380% of a Product<\/h2>\n
Problems Don\u2019t Start in the First Month<\/h2>\n
Experience Is Not About Writing Code Faster<\/h2>\n
When an AI-Built Product Meets Reality<\/h2>\n