Is it worth learning to code in 2026? This is a question many beginners ask themselves today, as they watch the rapid development of artificial intelligence (AI) and its impact on the IT industry. In recent years, tools like GitHub Copilot and ChatGPT have begun writing a significant portion of code — according to Google and Microsoft executives, AI already generates around 20–30% of the code in these companies. This raises concerns that AI might soon replace programmers. On the other hand, the demand for technology continues to grow, and companies still need specialists to build software. In Poland, in the first half of 2025, the number of IT job offers increased by about 68% year-over-year — mainly thanks to the boom in AI projects and data analytics. But does the classic advice — “learn to code, it’s a secure future” — still hold true in the age of AI? Let’s examine three possible scenarios for AI development and their impact on whether learning to code still makes sense for beginners in the Polish job market.
Current state: programming in the age of AI (global and Polish perspective)
The rise of generative AI is already reshaping how programmers work around the world. Large corporations are automating part of their tasks — for example, over 30% of code at Google is written by AI, and Microsoft uses AI agents not only to generate but also to review code. Some voices have even begun questioning the old dogma that everyone should learn to code. Okta’s CEO called this idea “ridiculous,” arguing that coding is worth learning only if you have an aptitude for it — not everyone needs to become a coder. Meanwhile, Google’s head of research believes the opposite — in his view, basic programming skills are more important than ever in the AI era, and “everyone should learn to program,” just as we all learn mathematics. Even industry leaders are divided about the future of coding.
In the Polish IT market, we can also see dynamic changes. After a slowdown in 2022–2023, 2025 brought a rebound — the number of job offers for programmers increased by nearly 70% compared to the previous year. “These increases stem from the fact that clients realized that AI won’t write all the code for them, and they still need a lot of people to build software,” comments Piotr Nowosielski, CEO of Just Join IT. The most in-demand specialists include data experts, AI and machine learning engineers, and advanced programmers (Java, JavaScript, Python). This shows that AI development is driving the demand for new experts — paradoxically, AI itself is creating new programming jobs, for example for AI engineers, data scientists, and MLOps professionals.
However, life isn’t easy for juniors. More than half of all offers (around 52%) target seniors, while juniors account for less than 6% of job ads. Yet beginners remain the most active group — they apply twice as often to each listing. Companies have raised the bar significantly: today’s juniors are expected to know not only the basics of programming but also AI tools, maintain an up-to-date project portfolio, and work quickly. A new phenomenon has emerged — “unemployable juniors” — people coming from bootcamps and courses for whom there are no roles, because companies no longer look for beginners to handle simple tasks now performed by AI. Data confirms this: although the total number of programming job offers rose by 68% in H1 2025, junior-level offers increased by only about 20%. It is now easier to start learning to code (the entry barrier is lower thanks to online courses and AI assistants), but harder to land that first job — competition is growing, and basic tasks for junior staff are increasingly automated. Experts point out that there is still room for new talent, but only the most committed will break through.
Current IT workers also feel the pressure of accelerating change. According to a 2025 study, nearly 70% of Polish IT specialists expect some of their current skills to become obsolete within a few years. Even now, up to 30% of code in many companies is written with the help of AI, and the programmer’s role is shifting from code creator to quality controller. No wonder 40% of developers fear their skills will soon become outdated. At the same time, adoption of new tools is high — 20% of developers use AI daily, and another 36% regularly. Most always verify AI-generated output and understand the limits of the technology, but 42% admit they can’t keep up with the pace of change, and 38% feel overwhelmed by the flood of AI-related information. These statistics illustrate the core dilemma: AI is changing the rules of the game in the labor market for programmers, while becoming a tool that programmers must master to stay relevant.
Three AI development scenarios and the future of learning to code
What might the situation look like in a few years? Let’s consider three hypothetical scenarios for AI development and their impact on the programming profession — from stagnant AI progress, through sudden breakthroughs, to gradual automation — with a special focus on the reality facing beginner programmers in Poland.
Scenario 1: AI stagnation — progress slows or stalls
In the first scenario, the current wave of AI innovation slows down. Perhaps we’ll hit the limits of current models or regulatory barriers, and in a few years AI will be only slightly better than it is today. What would this mean? Even if progress slows, the impact of current AI achievements will still be enormous. Experts emphasize that “no further breakthroughs” doesn’t mean stagnation — rather a multi-year phase of integrating and scaling the tools we already have. In other words, models like today’s ChatGPT/Copilot will become ubiquitous across the industry, increasing productivity and changing work culture, but not reaching full autonomy.
In practice, programmer productivity could increase severalfold over the next decade thanks to widespread adoption of code assistants. Writing code from descriptions (“vibe coding”) will become standard — even today, you can prototype small app elements in natural language, and within a few years such “descriptive coding” will enter the mainstream. However, this won’t be magical autopilot engineering — human oversight and expert work will remain necessary. A stagnating AI still makes mistakes, needs guidance, and can’t replace creativity or systems thinking. Therefore, companies will still need programmers, though their roles will evolve.
The biggest shift in this scenario affects junior roles. We already see that juniors are the first to feel the impact of automating routine tasks. When AI can generate template code, test it, or write documentation, many simple tasks once handled by interns and junior devs disappear or shrink. As a result, companies may hire fewer entry-level workers, relying instead on smaller teams of experienced developers equipped with AI. Analyses predict that the industry may ultimately employ fewer programmers overall, focusing on experts in architecture, verification, and AI orchestration — fewer “coders,” more “architects” and “code reviewers.” This is already happening: Polish companies are limiting junior recruitment (only ~5–6% of offers), because basic work is automated with AI tools.
Scenario 2: A Sudden Breakthrough – AI Takes Over Most Programming Work
The second scenario is a more catastrophic vision (for programmers): imagine that within the next year or two we see a breakthrough comparable to artificial general intelligence, or at least a major leap in the capabilities of code-generating models. Such an AI could independently—based on a requirements description—design a system architecture, write code, test it, and deploy it with minimal human involvement. Sounds futuristic? In March 2025, Anthropic’s CEO Dario Amodei predicted that within 3–6 months AI would be writing 90% of all code. The forecast proved exaggerated—by late 2025 estimates suggested around 41% of code being AI-generated—but the scenario of a sudden technological leap remains possible. If it were to happen, say in 2026, it could shake the programming job market.
With a sudden breakthrough, many programming tasks would become automated virtually overnight. Companies could drastically reduce hiring—if a single AI can do the work of an entire development team in a fraction of the time, why maintain large IT departments? In an extreme scenario, we might witness mass layoffs of programmers, especially in execution-level roles. Only a handful of “human AI guardians” would remain—experts overseeing algorithms, verifying results, and ensuring requirement correctness. The human role could shrink to defining what needs to be done, while AI decides how to implement it. Even today there are projects like GPT-4 as an autonomous agent, capable of creating entire applications based on a description. If such autonomy became reliable, traditional coding could be pushed to the margins.
From a beginner’s perspective, this would drastically worsen employment prospects in programming. The time invested in learning might not pay off if in 2–3 years there simply aren’t enough junior positions, or even mid-level ones. Everyone except the very top specialists would have to retrain or find their place in the new landscape. New job types could emerge—such as “AI trainer” (teaching models project specifics), “prompt analyst” (crafting queries for AI), or “AI auditor” (checking ethics and quality). But the number of such roles would be limited. Domain knowledge might also become valuable—for example, a programmer with financial expertise could work as a bridge between the business side and an autonomous system generating financial software.
So is it worth starting to learn programming in the face of such a revolution? It depends on your goals and time horizon. If you dream of becoming a “typical” developer writing applications in five years, a sudden AI breakthrough could make that path much harder. In that case, it might be better to consider other IT career paths that are less susceptible to full automation (more on that later), or assume that programming knowledge will serve you in a different role. Still, even in this bleak scenario for programmers, knowing how to code does not instantly become useless. Quite the opposite—people who understand code and the software development process would be needed to supervise and evaluate AI. Jay Graber, CEO of Bluesky, points out that we cannot delegate all thinking to AI. People should still learn skills like programming, because if you don’t know what good code looks like or how to build a system, you can’t assess whether AI’s output makes sense. In a world where AI writes most code, someone still has to judge whether that code is valid and meets the requirements.
It’s also important to remember that technology rarely wipes out an entire sector overnight. Even the most advanced AI will initially be costly, limited, and will require humans for training and maintenance. More likely, we would see a shift in focus: e.g., most simple front-end code would be generated by AI, while demand for specialists in infrastructure and integration with legacy systems might increase. For beginners, this means it is wise to watch trends closely. If a breakthrough seems imminent, you can adjust your learning path (e.g., focus more on understanding AI algorithms, math, and data—rather than learning yet another front-end framework that AI will handle easily anyway). Soft skills and uniquely human capabilities—creativity, business thinking, technical leadership—might also become more important, as AI won’t replace them easily.
In a sudden-breakthrough scenario, Poland would not remain a “green island.” Our IT market is deeply tied to global trends—many Polish developers work for foreign clients or multinational companies. If demand drops globally, we will feel it here too (for instance, via fewer outsourcing contracts). On the other hand, Polish companies are often not the earliest adopters of cutting-edge technologies—this delay could give local specialists some breathing room before AI fully replaces them. Government, education, and the public sector certainly won’t automate everything immediately. But in the long run, under this scenario, the traditional programming career path would shrink significantly.
To sum up: if you fear a sudden AI breakthrough, the decision to start learning programming in 2026 carries more risk. You must be prepared for the possibility of needing to retrain along the way. However, the knowledge gained from learning to code will not be wasted—even if you don’t become a developer, technological literacy is a huge asset in many other careers. And if the worst-case scenario doesn’t materialize (or is delayed), you will have skills that may again become highly valuable. After all, the tech industry often shows that the hype exceeds reality—today people warn that ChatGPT will take everyone’s job, yet companies continue to hire developers en masse (as job-posting data shows). Even if Amodei was wrong about the timeline for 90% code automation, it’s still wise to be cautious—while learning programming, develop skills that AI will not surpass anytime soon (more on that later).
Scenario 3: Gradual Automation – AI Slowly but Steadily Transforms the Market
The third scenario seems the most likely—and, in fact, is already happening. It assumes that AI will continue to improve steadily over the next 5–10 years, without a single dramatic jump in quality. Instead of a revolution—an evolution. Each year, tools become a bit better, able to take over new aspects of programming work. But the change is spread over time, allowing the industry and workers to adapt step by step.
In this scenario, the programmer’s role transforms. Today’s industry reports clearly indicate that AI is “shifting the programmer’s role from narrowly delivering code to broadly delivering value.” This means the developer of the future spends less time hand-writing code and more time understanding business needs, designing solutions, and coordinating the work of various tools (including AI). The programmer is no longer a lone creator of functionality but an orchestrator—a kind of manager who oversees the cooperation of many agents: the “AI programmer,” “AI tester,” and other automated assistants. As one report put it vividly, the developer evolves from an individual contributor to a manager of multiple AI agents. Less craft coding, more systems engineering, decision-making, and ensuring the created solutions make sense and meet goals.
Imagine a typical project in 2030: a single person plans which modules AI should create (and how they should integrate), gives AI the necessary instructions (prompts), reviews the generated code, tests it using additional AI tools, measures outcomes, and fine-tunes everything to meet business objectives. This is a completely different work model from classical hand-coding. Meta-skills become crucial: understanding the whole system, architectural thinking, defining requirements (what exactly needs to be done and how we’ll recognize success), and close collaboration with other departments (product, security, business). The programmer of the future is someone who combines technical and soft skills, can quickly learn new tools, and coordinates the work of various “agents”—both human and non-human.
Poland is already seeing early signs of this change. In the earlier-cited survey, 72% of developers said they understand AI’s limitations, and 88% always verify AI outputs. This shows that a supervisory role and critical thinking are becoming the norm. Meanwhile, most IT specialists indicate that to stay in the profession, they must continually improve their skills—nearly half study in their free time every month. Required competencies are also shifting: employers expect young candidates not only to code but also to use AI tools, think analytically, be creative, and learn new things quickly. Soft skills are gaining importance—surveys highlight creativity (71%), the ability to learn (68%), and critical thinking (61%) as key for the future. Technical know-how remains the foundation, but it must be constantly updated.
For a beginner starting in 2026, the gradual-automation scenario is, in some sense, optimistic. Why? Because it gives you time to enter the industry and grow your career alongside the transformation. Someone who will be a senior developer ten years from now likely won’t spend most of the day writing code line by line—but if they start as a junior now, they will gain experience along the way, learn to work with AI, and progress as the profession evolves. Over those ten years, programmers are unlikely to be pushed out entirely—instead, they will be gradually re-skilled into system engineers, analysts, AI integrators, etc. The job title “programmer” won’t disappear; it will simply mean something slightly different than it does today. It’s similar to the evolution of car mechanics: once they repaired mostly mechanical parts, today they must understand digital diagnostics and electronics in EVs. It’s still a “mechanic,” but the knowledge and tools have changed.
It’s important to note that under the gradual scenario, new programming jobs will continue to appear, though with different specializations. AI will paradoxically create demand for roles related to itself—for example, developing and maintaining AI systems, integrating them with existing software, and ensuring security (new risks like automated AI-driven attacks will emerge). Polish companies already report that AI adoption and business-process automation are creating demand for specialists in these areas, and categories like AI/ML were among the fastest-growing job segments in 2025. We can expect that the junior of the future may assist in training AI models, build tools for monitoring AI behavior, or connect various AI services into cohesive systems. So knowing the basics of programming remains crucial, because it is the starting point for all these roles.
In summary across the scenarios: The most realistic outlook is a continuation of the acceleration we are already seeing. AI will become more and more embedded in programmers’ work, but it will not eliminate their jobs overnight. Instead, it will change the nature of the job. For aspiring developers, this means that learning to program still makes sense, but you can no longer assume that “a bootcamp + one language = an easy career.” You must prepare for a profession that requires constant learning, flexibility, and broad horizons. If someone is ready for that, the prospects remain good—because the world will need people who understand technology and can guide it. But if someone viewed programming only as a quick path to a high salary and isn’t genuinely interested in IT, they should think carefully (as the Okta CEO said, not everyone must code). In such a case, another career path might be a better choice.
Programming Compared to Other IT Paths (and Beyond IT)
It’s worth taking a broader view: do other IT roles offer more stability today than programming? And how does the IT sector compare to non-tech professions in the era of expanding AI?
Other IT paths. The truth is that artificial intelligence affects most technology-related professions—not just programmers. Take software testers, for example: tools that automate testing are already widespread, and AI can generate test cases and detect bugs. Routine testing may soon be just as automated as writing code. System administrators/DevOps? AI supports infrastructure management (so-called AIOps), though these roles will always require oversight of physical systems and architecture—so the work is likely to shift toward more complex tasks rather than disappear. UX/UI specialists can sleep more peacefully: while AI can already generate simple designs, it still lacks human intuition around usability and aesthetics. Designers will likely use AI as an assistant (e.g., for prototyping), but the creative human will continue to shape the final look and feel of products.
Meanwhile, there are areas of IT that AI actually fuels. For instance, data science and data analytics—growing datasets and AI modeling require more specialists to prepare data, interpret results, and explain model behavior. AI is unlikely to replace data scientists; instead, it will make their work more efficient (automating steps like data cleaning), making this path look promising. Cybersecurity is another domain where workload will likely increase rather than decrease—experts note that AI adoption introduces new attack vectors, and companies must invest in automated defense as well as security professionals. So if someone is deciding between programming and, say, cybersecurity, the latter may be worth considering—though programming skills (e.g., for scripts and tools) are highly useful there too.
Roles closer to the business side in IT—such as product manager, business analyst, consultant—are also evolving, but here AI is more of a support tool than a replacement. A PM can use AI for market analysis or summaries, but human judgment, feature prioritization, and understanding customer needs remain essential. A business analyst may use AI to generate a report, but it’s still the human who asks the right questions and interprets the results. So if someone is technically inclined but doesn’t want to spend entire days coding, a path combining IT and business (the tech-biz area) may be a strong option—where programming knowledge remains an advantage, helping you understand the development team and assess the feasibility of plans. (Cloudflare’s CEO has said that knowing how to code makes him a better leader because he understands engineers’ work.) At the same time, conceptual tasks and interaction with clients or leadership are domains where empathy and communication—purely human traits—dominate.
Or maybe something outside IT? It is increasingly argued that humanities-based or craft professions may be the future in the age of AI. Professor Baobao Zhang, an AI policy expert, notes that the safest jobs are those requiring empathy or manual skills. Paradoxically, roles like electrician, plumber, mechanic, nurse, caregiver—not typically associated with modernity—are less vulnerable to automation than many office jobs. Robots and algorithms still struggle with the unpredictable physical world and with building human relationships. For the next 10 years, it is unlikely we’ll see a robot fully replacing a bedside nurse or performing a complex electrical renovation in an old apartment building. If someone’s goal is maximum resistance to AI, then indeed, a non-IT profession might be the better choice. However, many of these “safe” careers don’t offer the same pay or remote-work flexibility as IT—there are trade-offs. And the world still needs people to develop AI—so escaping technology entirely won’t stop the changes (it’s a bit like refusing to go into medicine because new diagnostic devices appear—better to learn how to use them).
Ultimately, the choice of path should depend on individual aptitude and passion. If you genuinely enjoy programming, like solving problems, and love learning new things, AI shouldn’t discourage you—it should become your ally. All signs point to programmers working differently, but still being needed. If someone is hesitating because they’re equally interested in data analysis or server administration, it’s worth tracking trends in those fields, but the general rule is that IT fundamentals are valuable in every tech specialization. Coding skills are often called the “new literacy” in the digital world. Even if you never become a full-time software engineer, understanding code gives you a competitive edge in many technical roles.
Benefits of Learning to Code (Regardless of Employment)
Finally, it’s important to emphasize that learning to code offers many benefits beyond securing a full-time job. Here are some key ones:
Technological literacy: We live in a software-driven world. Knowing the basics of coding helps you understand the apps and devices around you. Even if you don’t become a developer, this knowledge helps you use technology consciously. A real-world example: Dropbox’s CEO, Morgan Brown, admitted that although he doesn’t do advanced coding in his role, he learned SQL to understand his team’s data and queries. The ability to “speak the language of technology” will always matter—without it, you may miss the opportunities and limitations of the tools you work with.
Improved logical thinking and problem-solving: Programming teaches structured thinking, breaking down complex problems, and analyzing causes and effects. This problem-solving mindset transfers to many areas of life—from project planning to personal productivity. Cisco points out that certain fundamental elements of programming shape how we approach tasks. In short, “coding teaches combinatorics”—and that’s useful everywhere.
Automating your own work: Even if you work in another field, knowing how to code allows you to streamline many tasks yourself. A finance worker can write a script to generate reports, a biologist can integrate tools for analyzing research results, and a marketer can automate the collection of social-media data. In the age of AI, this becomes even more powerful—someone who can write code and also knows how to use AI model APIs can automate tons of tedious tasks, gaining an advantage over others. Hence the popular saying: “learn to code, if only to make your own life easier.”
Hobby projects and your own business: Being able to create a working application or website opens huge opportunities to bring your ideas to life. You can build a simple game, mobile app, or blog—either for fun or as the seed of a startup. What’s more, thanks to AI, a single developer can now achieve more than ever. There are real accounts of one person building a game in a year using AI where previously it took 10 developers three years (because AI helps write code, generate graphics, etc.). Learning to code gives you the creative power to build things—and AI enhances this power instead of taking it away. Many successful tech entrepreneurs began by coding the first prototype of their product themselves. With this skill, you can eventually launch your own project without immediately hiring an entire development team (which is both expensive and hard to retain).
Freelancing and remote work: Programming has long been a profession well-suited for remote and contract work. The freelance IT market thrives in Poland and globally—companies willingly hire specialists for individual tasks. For someone who values freedom and project diversity, coding opens the door to a career as an independent contractor. You can work from anywhere in the world for clients ranging from the U.S. to Australia. And with today’s freelance platforms and asynchronous work culture, the entry barrier is lower—beginners can start with small gigs (e.g., WordPress sites, simple scripts) and gradually build a portfolio. Even if the full-time job market tightens, there will always be demand for capable people doing ad-hoc tasks in side projects, legacy maintenance, system migrations, etc. Global competition grows, yes—but access to opportunities is far wider than the number of full-time positions in any single country.
To sum up: programming is not just a profession but a universal 21st-century competency. It teaches you how to think, gives you independence in a tech-driven world, and lets you create something from nothing—even if only for personal satisfaction. Even if you don’t become a full-time developer, the skill can be an ace up your sleeve in many situations. And if you do choose it as a career path, the advantages above will only strengthen your position.
Conclusion: Is It Worth Learning to Code in 2026?
Yes—but with your eyes open. The IT industry—especially software development—is on the brink of major changes due to AI. Yet the history of technology shows that new inventions tend to reshape the job market rather than destroy it. When ATMs appeared, bank tellers didn’t disappear overnight; when spreadsheets became popular, accountants weren’t replaced—they simply shifted to more complex work. Similarly, AI will not suddenly erase the need for software, because the demand for digital solutions keeps rising. Gartner projected that by 2025, AI would create more programming jobs than it eliminates—thanks to new projects in AI, data science, and automation. We’re already seeing this: companies adopting AI need people who can manage it.
From the perspective of a beginner in Poland, it’s worth learning to code—provided you treat it not as a quick course guaranteeing a stable job, but as the start of a long learning journey. You must be prepared for the possibility that entering the job market may take more time (due to competition and rising expectations). Your first job might not be “Junior Java Developer in a corporation,” but perhaps as an automation tester, junior data analyst, or AI project intern—and only later you might transition into a pure development role. Flexibility is valuable, and you should not dismiss adjacent roles—they also provide valuable experience. Remember: programming is a tool for solving problems, and if you can apply that skill, you’ll find ways to make use of it—even if your job title doesn’t explicitly say “developer.”
The three scenarios analyzed earlier show that the darkest variant (full automation of the profession) is unlikely to happen overnight, while the most realistic—gradual progress—offers solid opportunities for those who start learning now and adapt alongside the market. In the stagnation scenario, programming remains as necessary as it is today (augmented by AI). In the gradual-automation scenario, the nature of work changes, but demand for technically sharp people doesn’t disappear. And in the sudden-breakthrough scenario—well, even then it’s better to understand technology than to be merely a passive user.
To conclude, here are the words of Google’s Yossi Matias: “The fundamentals of programming may be more important today than ever in the age of AI.” By learning them, you invest not only in a potential career but also in your overall intellectual development in a digitized world. So if you have the motivation and curiosity to dive into coding, 2026 is still a great time to begin. Just do it wisely: follow trends, learn to use AI instead of fearing it, and also build soft skills. In a few years, it may be you—a new specialist comfortable with AI—who has the advantage over “old-school programmers” stuck in outdated habits. The future will belong to those who can collaborate with AI to deliver value—regardless of the formal job title.
Should you start learning to code in such a world? Yes, but… you need to be aware of higher competition and new expectations. Basic coding skills may no longer be enough — additional competencies will matter. Experts recommend combining coding with broader understanding: security fundamentals, familiarity with AI tools, analytical and business thinking. Cisco’s Liz Centoni emphasizes that programming remains a fundamental skill that teaches problem-solving, but the engineer of the future must know when to use technologies like ML or generative AI for real-world tasks. In other words, systems thinking and the ability to connect tech and business will matter more than narrow specialization in writing code.
A beginner entering the market in 2026 in an AI stagnation scenario will still find job offers — programming won’t disappear as a profession. However, the path from learning to landing a first job may be longer and more challenging than a decade ago. In Poland, the oversupply of juniors relative to demand may persist, forcing continuous improvement. Flexibility will be key: mastering not only one technology but also support tools (like Copilot), continuously learning, and staying ready to retrain if needed. In an AI stagnation scenario, we have time to adapt — but it’s not worth postponing learning, because even AI that “maintains its current level” will gradually change work standards. In summary: learn to code if you’re passionate about it and have talent — but assume that coding alone is not enough. Those who combine skills will do better. As Matthew Prince (CEO of Cloudflare) said, understanding how software is built is extremely useful — even if you’re not coding daily, that knowledge makes you a better specialist or manager. And even in security, no line of code will reach production without human review, so the engineer’s role remains indispensable.