Artificial General Intelligence (AGI) is a hypothetical, advanced form of AI capable of learning and reasoning like a human being, and its development raises many questions about the future of the labor market. Already today, we can observe the impact of algorithms and automation on employment: according to a World Economic Forum (WEF) report, as many as 66% of companies plan to hire employees with AI skills, while at the same time 40% of businesses intend to reduce headcount as more tasks become automated. As work on AGI progresses, these trends may intensify. Some forecasts are downright alarming—experts estimate that by 2040, artificial intelligence could automate or fundamentally transform 50–60% of existing occupations, and by 2050 even more than 80% of jobs could be automated if the pace of innovation continues. On the other hand, the outlook is not uniformly pessimistic—the same WEF report predicts that although AI may eliminate around 92 million jobs, it will simultaneously create approximately 170 million new positions.
The development timeline for AGI remains uncertain and long-term. Leading technology figures disagree on when true AGI might emerge—some (such as Elon Musk) believe it could happen as early as the second half of the 2020s, while others (including leaders at Google DeepMind) point to the 2030s or later. Regardless of the exact timing, advanced AI systems are already taking over many tasks that require data analysis or repetitive work. The rapid progress of generative models (e.g., large language models) in recent years has demonstrated that algorithms can write code, generate text and graphics, and support decision-making. This raises a crucial question: which jobs and skills will remain secure in the coming AGI era?
Global Trends: Automation and Employment in Technology
The current wave of automation poses the greatest threat to jobs based on routine and repetitive activities. Structured, predictable, and data-processing tasks are the easiest for AI to take over. Even today, we see declines in roles such as administrative staff, accountants, or office assistants—activities like data entry, document processing, scheduling meetings, or basic reporting can largely be automated. For example, AI-powered tools streamline customer service (chatbots), replace portions of accountants’ work (automated bookkeeping), or substitute receptionists (online registration systems). Changes are also evident in IT: a wide range of intelligent coding assistants already exist that can generate code from natural language descriptions. As a result, routine tasks traditionally assigned to junior developers can be completed more quickly by AI, calling into question some traditional entry-level IT positions.
At the same time, new career opportunities are emerging, driven by the development of artificial intelligence. Wherever AI takes over tedious tasks, demand grows for people who supervise, improve, and ethically govern these systems. Experts predict the emergence—or rapid growth—of roles such as AI ethics specialists, data engineers, machine learning engineers, or AI solution architects—professionals who help organizations deploy and maintain advanced models safely and responsibly. Moreover, if AGI development accelerates, major infrastructure changes will be required (e.g., data centers) along with the broader technological ecosystem. This is why forecasts also point to growing importance of professions such as nuclear engineers (to supply power for energy-intensive AI systems) or data center managers.
The impact of AGI on the labor market will therefore be dual in nature: many roles will be automated, while new professions and specializations will emerge. A key trend is the shift of human work from purely technical, operational tasks toward more creative, conceptual, and supervisory responsibilities. Even in areas where AI supports work (such as software development), humans will still be needed to define problems, ensure quality control, and adapt solutions to business contexts. In the most pessimistic scenario, AGI could take over most available jobs, forcing societies to adopt new support models (e.g., universal basic income). However, most experts emphasize that the complete elimination of human labor is unlikely—instead, its nature will change. Automation will be complementary to many professions: AI will handle the repetitive 50% of tasks, while humans will focus on aspects requiring a human touch, creativity, or oversight.
Professions Resilient to Automation and Replacement by AI
Which roles are therefore most resilient to AI and future AGI? Labor market analyses clearly point to certain characteristics of future-proof professions: high creativity, intensive human interaction, complex problem-solving, adaptability, and the need for non-standard decision-making. These are precisely the areas where algorithms struggle the most, because emotional intelligence, empathy, creative thinking, and strategic vision remain fundamentally human strengths. Even advanced artificial intelligence relies on historical data and predefined patterns, which makes it less effective in novel, uncertain situations or those requiring intuition.
Professions often considered relatively safe from automation include roles in healthcare and education (e.g., nurses, psychotherapists, teachers), because they are built on human relationships and trust and require responses to unique situations. Similarly, senior management and leadership roles show strong resistance—managing people, inspiring teams, and making strategic decisions based on incomplete data are areas where soft skills and experience are irreplaceable. Analysts suggest that even by 2040, most managerial tasks will not be fully automated, and effective leadership will continue to rely on emotional intelligence and adaptability. In short, social competencies will be extremely valuable—no machine can replace a charismatic leader or a trusted mentor.
Within the technology sector, resistance to AGI is particularly evident in roles at the intersection of multiple disciplines, especially those that combine digital skills with deep understanding of human or business needs. As career advisors note, the future belongs to professionals who can work alongside AI rather than compete with it. New professions are already emerging in which humans remain essential due to empathy, creativity, strategic thinking, and adaptability. Below are some examples of careers especially resilient to automation in IT and related fields:
AI Prompt Engineer – a specialist who designs and optimizes prompts for AI models to achieve desired outcomes. This role requires understanding both technology and human linguistic intent. (An example of new technology-driven roles emerging in the labor market.)
AI Ethics Specialist / Algorithm Auditor – a professional who oversees AI systems for compliance with ethical principles, regulations, and social norms. This role requires technical knowledge as well as an understanding of the social and legal consequences of AI deployment, asking questions such as: should we deploy this at all?.
AI Systems Architect – a designer of advanced AI-driven solutions who can translate business needs into technical architecture. This role combines programming skills with domain expertise to build AI systems that solve real-world problems.
Context-aware Data Scientist – a data expert who, in addition to strong skills in statistics and machine learning, possesses industry-specific knowledge (e.g., healthcare, finance) and can interpret AI outputs in context. Such professionals are valuable because generating a prediction is not enough—it must be embedded in real-world business conditions and followed by informed decisions.
Cybersecurity Specialist – a role gaining importance in an increasingly digital world. Protecting data and systems from ever more sophisticated threats (often AI-assisted themselves) requires creative, unconventional thinking and constant adaptation. Cybersecurity relies heavily on human ingenuity, which is why security experts remain in high demand.
UX/UI Designer (User Experience/User Interface) – a creative profession in which understanding human emotions, needs, and behaviors is crucial. While AI can generate visual or textual proposals, it cannot replace human sensitivity to user experience or the ability to create a coherent product vision. A strong UX designer combines psychological insight with aesthetics and functional logic.
Digital Transformation Consultant / IT Business Analyst – a professional who helps organizations implement new technologies and optimize business processes. Success in this role requires communication skills, understanding the client’s industry, and creative problem-solving at an organizational level. Such interdisciplinary roles (bridging IT and business) are difficult to automate because they rely on tailored solutions and change management.
The common denominator across these professions is that they require more than just technical proficiency. They either integrate knowledge from multiple domains or rely on uniquely human qualities—creativity, empathy, ethics, and strategic thinking. Importantly, many of these roles are relatively new or transformed by technology. This demonstrates that instead of fearing that “AI will take our jobs,” it is more productive to ask: what new roles will emerge, and how can I prepare for them? The labor market is evolving, and while some positions will disappear, others—often more engaging and intellectually rewarding—will emerge at the intersection of humans and artificial intelligence.
Fields of Study for the Future – What to Study in the Age of AI?
Now that we understand which competencies will be in demand, it is worth translating this knowledge into educational choices. AI-resilient fields of study are those that prepare graduates for roles combining technology with other domains or that develop skills that are difficult to automate. Labor market experts recommend combining specializations—the ideal graduate of the future is someone with deep expertise in a specific domain (e.g. finance, biology, design), complemented by a solid technical foundation and ethical awareness. Supplementing a traditional degree with an additional minor or a certificate in areas such as data science, AI, or cybersecurity can significantly increase resilience to market volatility. Similarly, an increasing number of universities offer courses such as “AI in X” (e.g. AI in Healthcare, Data Analytics in Marketing, Data Ethics in Finance), which enable students to acquire unique combinations of skills.
Based on WEF reports and labor market analyses, several fields of study and educational pathways stand out as “future-oriented” and relatively resistant to automation:
Computer Science (with a specialization in AI / Machine Learning) – traditional computer science enhanced with in-depth knowledge of artificial intelligence. Graduates are able to design and improve AI systems—in other words, to build the tools of the future rather than compete with them. These skills are crucial in any industry becoming “AI-first,” from healthcare to fintech.
Data Analytics / Data Science / Statistics – programs focused on collecting, processing, and extracting insights from data. Data is the foundation of AI, and data specialists will be needed to train models, interpret outputs, and support business decisions across all sectors (from manufacturing to marketing).
Cybersecurity (Information Security) – in an increasingly digital world, protecting systems from attacks is a growing priority. The more organizations rely on AI and cloud infrastructure, the more valuable experts become who can safeguard systems and data. IT security programs teach both technical skills and analytical thinking about risk and crisis management, areas that are difficult to automate.
Software Engineering (DevOps / Cloud Engineering) – programs focused on designing, deploying, and maintaining complex software systems. Even if some code is written by AI tools, someone must oversee the entire ecosystem, integrate components, and ensure scalability and reliability in the cloud. DevOps and cloud specialists are becoming essential in all organizations building or operating AI-powered applications.
Electronics / Automation and Robotics / Computer Engineering – fields that combine hardware and software knowledge. Designing robots, embedded systems, or IoT devices requires understanding both physics/mechanics and algorithms. In the era of autonomous vehicles, smart factories, and intelligent devices, hybrid hardware-software skills will be highly sought after.
Finance and Data Analytics (FinTech) – a field blending traditional financial knowledge with the use of technology (AI, automation) in financial services. Banking and insurance are undergoing a digital transformation—FinTech programs teach financial theory alongside programming, big data analytics, and regulatory frameworks. Graduates can design risk assessment algorithms, payment systems, or scoring models, making them valuable assets to modern financial institutions.
Human–Computer Interaction (HCI) / UX Design – programs focused on user-centered technology design. Students gain knowledge in psychology, design, and computer science, learning how to create interfaces and products aligned with human needs. Because empathy and understanding human behavior are essential here, this field will remain one in which humans play a leading role, with AI serving only as a supporting creative tool.
Operations and Supply Chain Management (Data-Driven) – interdisciplinary studies at the intersection of engineering and management, focused on optimizing business processes using analytics and AI. Graduates can analyze logistics data, improve supply chains, and implement automation in factories while maintaining a holistic view of organizational operations. This systems-level thinking and coordination across many variables is difficult to encode in algorithms, ensuring ongoing demand for operational analytics specialists.
Bioinformatics / Medical Informatics / Health Informatics – interdisciplinary programs combining life sciences (biology, medicine) with computer science and data analysis. In the age of AI, the importance of genomic analysis, personalized medicine, and remote healthcare is growing. Professionals who understand biological processes and can use AI tools to interpret medical data will be indispensable in healthcare and pharmaceutical industries.
The list above is not exhaustive, but it illustrates a clear direction: invest in education that combines technical skills with domain knowledge, or that develops capabilities where technology requires human augmentation. A valuable strategy is also to build a T-shaped profile—deep specialization in one area combined with broad knowledge of related fields (especially in IT). For example, people drawn to creative careers (media, design) benefit from learning the basics of programming or data analysis, while engineers and IT professionals should understand the business context of their work and study fundamentals of economics or sociology. The future labor market will favor individuals with broad horizons and the ability to connect the dots across disciplines. As the WEF emphasizes, the most sought-after graduates are those who combine deep domain expertise with strong technological proficiency and ethical awareness in the use of AI.
Key Competencies: Soft Skills and Interdisciplinary Abilities
Not only what we study, but also which skills we develop will determine our career resilience. Soft skills—communication abilities, social intelligence, creative thinking—are becoming increasingly valuable in an algorithm-driven world. As automation reduces the importance of purely technical, repetitive tasks, demand rises for individuals who are adaptable, creative, capable of solving complex problems, and effective collaborators. According to WEF reports, by 2025 the most in-demand skills—alongside AI and data analytics—will include creative thinking, resilience and flexibility, and the ability to learn quickly. In other words, adaptability has become the new currency of the labor market.
In the workplace of the future, critical assets will include critical thinking (questioning the status quo and identifying improvements), emotional intelligence (understanding others’ emotions and motivations, the basis of teamwork and leadership), creativity (generating innovative ideas and thinking beyond established patterns), and communication and collaboration skills within diverse teams. These qualities give humans an edge over machines—algorithms can process billions of data points per second, but they do not sense social context, lack empathy, and cannot improvise effectively in unfamiliar situations.
Interdisciplinary competencies are gaining importance as well. In the face of complex challenges (such as AI ethics dilemmas, climate change, or global crises), individuals who can integrate perspectives from multiple disciplines are especially valuable. Employers increasingly seek professionals fluent in the language of technology who can also engage with business needs or user perspectives. Those with open minds, lifelong learning habits, and the ability to rapidly reskill will be particularly prized. As a result, investing in personal development should not end with a diploma—continuous upskilling, online courses, trend tracking, and conference participation will become standard elements of a career. The ability to learn how to learn—to quickly absorb new tools and knowledge—is one of the most important meta-skills of the future.
In practice, to strengthen career resilience today, it is worth: (1) becoming comfortable with AI—learning and using AI tools to support daily work (e.g. automating routine tasks); (2) developing social skills—improving communication, leadership, and empathy in professional relationships; (3) nurturing creativity—experimenting with new approaches and learning creative problem-solving methods; and (4) maintaining curiosity and a habit of lifelong learning. This combination of hard and soft skills will enable us to work effectively with machines rather than compete against them, complementing their capabilities with what is uniquely human.
Adapting in the AGI Era
AI-driven automation and the potential emergence of AGI will undoubtedly reshape the IT labor market (and many others) in the coming decades. This does not, however, imply a jobless future for humans. Roles grounded in empathy, creativity, adaptability, and an understanding of human needs will survive and thrive—qualities that algorithms cannot authentically replicate. Global forecasts suggest that many traditional roles will disappear or be transformed, while tens of millions of new positions requiring novel combinations of knowledge and skills will emerge. At a macro level, AI can increase productivity and relieve us of monotonous work, creating space to focus on more ambitious challenges.
For current and future workers, the key will be flexibility and proactivity. It is worth analyzing trends now, learning faster than technology evolves, and consciously shaping one’s education and career path. Rather than fearing competition from intelligent machines, it is more productive to treat them as tools that expand human capabilities. Strategies such as skill combination (e.g. technical with humanistic), continuous upskilling, and the development of soft skills provide the best protection against automation. As one labor market report aptly states: “The future belongs to those who can learn faster than technology changes. Instead of running from AI, we should learn to work with it.” Armed with knowledge, adaptability, and uniquely human talents, we can confidently enter the AGI era—not as victims of replacement, but as partners in technological progress, co-creating a new and innovative labor market.