What Skills Should Developers Learn in the AI Era?

While AI technology automates simple coding tasks, the role of developers remains crucial and is evolving rather than disappearing.

Today, Artificial Intelligence (AI) has evolved into programming-capable agents that can assist or automate many development tasks from code generation to debugging. According to a 2023 survey, 76% of developers are already using or considering adopting AI coding tools (like GitHub Copilot), and 65% of companies are utilizing generative AI for one or more business functions.

Bottom line: AI is changing the types of roles and competencies required of developers, but it’s not eliminating developer jobs. However, since AI is replacing simple and repetitive tasks that humans used to handle, future developers must focus on work that creates higher added value.

How Developer Roles Are Changing: “Evolution” Not “Replacement”

The emergence of AI has indeed brought significant changes to development work. AI tools now quickly handle tasks that developers used to do manually: automatic code generation, refactoring, bug fixes, and test case generation.

Google’s Principal Scientist Jeff Dean predicted that “AI will soon be able to perform coding at the level of a junior developer.” This means the environment is becoming more challenging for recognition with only average entry-level development skills.

However, many experts propose a new direction of collaboration between AI and developers. Microsoft CEO Satya Nadella mentioned that “AI will not replace programmers but will become their tool, helping humans achieve more.”

AI Limitations and Human Developer Strengths

Current AI coding tools have limitations:

Cannot fully understand context
Limited in creative thinking and generating new ideas
Insufficient complex and long-term problem-solving abilities

Three areas where AI tools are vulnerable: (1) deep contextual understanding, (2) creative thinking, (3) complex problem-solving abilities.

Since there are clearly areas where AI cannot excel, future developers will be responsible for filling the gaps that AI cannot address.

Human Developer Strengths and Required Competencies in the AI Era

① Technical Competencies: Becoming Engineers with Solid Fundamentals and Integrative Skills

Strong CS Fundamentals

Traditional computer science knowledge like data structures, algorithms, and computer architecture remains absolutely crucial in the AI era. Even if AI tools can quickly generate code, properly understanding the time complexity and memory usage of that code is the developer’s responsibility.

Large Language Models (LLMs) have learned from vast datasets, but they don’t truly “understand” programming concepts like humans do, sometimes producing inefficient or error-prone code. To filter these out and select optimized solutions, fundamental knowledge of algorithmic complexity and data structure principles is essential.

Software Architecture Design Skills

While AI can write code, it cannot autonomously decide what system structure to design. The ability to envision the big picture, design inter-module relationships, and organize architecture considering scalability and maintainability requires the judgment of experienced developers.

Future developers will increasingly take on “director” roles that assemble AI-generated code fragments and provide direction. Therefore, understanding and applying object-oriented design, design patterns, and system architecture principles will become a differentiating capability.

Testing and Debugging Capabilities

Test writing and debugging abilities that ensure software reliability are also core roles of human developers. While AI can generate basic test code, determining what boundary conditions to additionally verify and whether there are logical flaws remains human work.

Google’s Jeff Dean emphasized that “for AI to truly reach human developer level, it must also be able to run tests and debug performance issues.” Ultimately, tracing root causes of bugs and finding performance bottlenecks through profilers are advanced tasks that depend on developer expertise.

AI/Data Utilization Skills

In the AI era, developers also need basic understanding of data and machine learning. Even if not all developers become AI specialists, having knowledge of fundamental machine learning principles (based on linear algebra and probability/statistics) and data processing capabilities (SQL, etc.) provides significant advantages.

For example, extracting insights from large datasets or applying simple machine learning models are capabilities that may be required in almost all future software. Developers who effectively utilize AI understand how to collect and preprocess necessary data and apply models to solve problems.

Security and Quality Insights

As software becomes more complex and AI-generated code increases, Security and Quality assurance have become even more important. Since AI generates code based on vast training data, it can sometimes indiscriminately suggest code with licensing issues or security vulnerabilities.

Therefore, developers who understand security principles (e.g., input validation, SQL injection prevention, memory safety) and software testing techniques are highly valued in the AI era. For AI-generated code especially, we must thoroughly examine “Does this code work as intended? Is there potential for abuse?”

② Soft Skills: Leveraging Human Capabilities in the AI Era

Creative Problem-Solving

Creativity is one of the greatest strengths of human developers. Programming goes beyond simply implementing given problems in code; it involves defining problems and devising novel solutions. AI only suggests code in familiar patterns learned from past data; it cannot generate completely new ideas or innovative designs.

Ultimately, “innovative thinking” remains the domain of human engineers. Develop creative thinking skills to identify user pain points for improvement or conceive entirely new services.

Critical Thinking and Ethical Awareness

The attitude of verifying rather than blindly trusting AI outputs, and ethical consciousness considering technology’s social impact, are also important. In the AI era, humans must ultimately filter and take responsibility for errors caused by incorrect AI outputs or biased data.

Therefore, critical thinking skills that question “Why did this result occur?” are essential. For example, you must be able to thoroughly examine and question whether AI-recommended code has errors or security issues.

Communication and Collaboration Skills

Teamwork and communication abilities are increasingly important strengths of human developers in the AI era. While AI might be good at coding alone, it cannot communicate with people. Ultimately, extracting user requirements and collaborating across departments to create products remains human work.

Developers must constantly communicate with fellow developers, designers, planners, and even clients. Developers who excel at persuasively conveying their ideas and listening to others’ opinions for coordination are indispensable in organizations.

Domain Knowledge and Business Acumen

Future developers who are multi-players with domain expertise and business understanding, rather than just technical knowledge, will become increasingly valuable. In the AI era, developers with deep knowledge in specific fields become more important because even AI-powered solutions ultimately require humans to understand the context and apply them appropriately.

For example, developing medical AI software requires developers who understand medical data characteristics, regulations, and doctors’ needs. AI only performs general learning; it cannot fully grasp the subtle differences or specialized knowledge of each industry.

How to Use AI as a Tool: Become a Smart Pilot

Treat AI as a “Smart Junior Developer”

AI coding tools are more like “very diligent junior developers who work quickly but also make mistakes” rather than experienced senior developers. Therefore, rather than blindly trusting and delegating, you always need an attitude of supervising and mentoring.

When reviewing AI-suggested code or solutions, first examine whether there are any incomprehensible or suspicious parts. Like reviewing code written by a newcomer, you should identify bugs or inefficiencies and think of improvements. Since AI cannot fully understand context, we must properly guide the direction.

Prompt Engineering: Questions Determine Quality

How you ask (prompts) is very important to get desired results from AI. The same problem can yield vastly different answer quality with slight prompt modifications. Therefore, practice asking clearly and specifically.

The more detailed you provide about problem situations, requirements, and constraints, the higher the accuracy of AI responses. Also, it’s normal if perfect responses don’t come in one try - trying multiple times and improving is part of the process.

“Trust but Verify”

This is the golden rule of AI utilization. If you’ve quickly generated code with AI’s help, always go through a verification process. Using AI code without verification can lead to unexpected bugs or security issues later.

Specifically, you should directly run tests on AI-written code and manually check edge cases. According to GitHub’s recommendations, AI-generated test code should also be understandable and analyzable by developers to be trustworthy.

What to Learn and How to Utilize Undergraduate Curriculum

The theories and principles learned in undergraduate studies serve as the foundation for higher-level problem-solving in the AI era. The more AI automates, the more valuable people who understand “why things work that way” become.

▶ Theoretical and Mathematical Foundation Courses: Logical Thinking and AI’s Language

Discrete Mathematics

Forms the logical backbone of computer science. Learning propositional logic, sets, graphs, and combinatorics develops logical thinking and proof abilities. This helps understand the correctness of complex algorithms and verify AI logic.

For example, knowing graph theory is essential to properly solve social network path recommendation or network optimization problems. To judge whether AI-proposed solutions are logically sound, such rigorous thinking must be the foundation.

Linear Algebra and Probability & Statistics

These are the mathematical foundations of modern AI algorithms. Linear algebra is virtually the basic language of deep learning, with matrix operations and vector space concepts being the key to understanding neural network operating principles. Probability and statistics help understand how machine learning models make probabilistic predictions and inferences.

Studying these subjects diligently allows you to go beyond simply using AI to understand why it works that way, and when errors occur, approach and diagnose mathematically.

Numerical Analysis

Deals with solving mathematical problems by computer, studying floating-point errors, approximation methods, etc. This knowledge applies directly to scientific computing and simulation, and also adds depth of understanding when dealing with numerical stability issues like gradient vanishing/exploding problems in deep learning.

▶ Programming and Algorithm Courses: Honing Problem-Solving Skills

Object-Oriented Programming (OOP)

Learning concepts like encapsulation, inheritance, polymorphism teaches how to structure large programs. This improves code reusability and maintainability. Even when AI writes code, knowing OOP principles allows us to properly structure AI-generated code and refactor when necessary.

For example, even with Copilot-generated code fragments, deciding how to divide them into classes and modules, or applying design patterns for generalization, remains the developer’s responsibility.

Data Structures and Algorithms

Provide tools for efficient problem-solving. Learning data structures like arrays, linked lists, trees, hashes, graphs, and algorithms for sorting, searching, pathfinding develops the ability to judge which approach is appropriate for which problem.

This process also teaches time/space complexity analysis. This knowledge remains important in the AI era because even when AI writes code, humans must judge whether it’s an O(N²) or O(N log N) algorithm.

Programming Language Theory

Studies programming language design principles and various paradigms (imperative, functional, logical, etc.). While this might seem abstract, this knowledge enables quickly grasping core concepts when learning new languages and develops insight for language selection.

In the AI era, using appropriate tools for different situations is important. Understanding language theory helps you understand questions like “Why is Python easy to learn but slow?” or “What advantages do functional languages have in the multicore era?” enabling optimal technology stack selection.

▶ Systems and Networking Courses: Understanding Computing Internals

Operating Systems

Covers computer resource management principles like CPU scheduling, memory management, file systems, and process synchronization. Learning OS helps understand how programs receive memory allocation, threads execute, and interact with hardware through system calls.

This knowledge greatly helps in writing efficient code. For example, it strengthens performance optimization and problem-solving abilities like avoiding deadlocks when using multithreading or coding in a memory cache-friendly manner.

Computer Architecture

Covers CPU and memory principles, instruction pipelines, caches, and parallel processing. This teaches how hardware executes code. This knowledge is essential for system optimization. Understanding computer architecture helps you know why certain code patterns cause cache misses and performance degradation, and consider operation optimization at the assembly level.

Multiprocessor Computing

Deals with parallel processing and concurrent programming essential today. To extract performance in multicore CPU and distributed computing environments, you need to understand concepts like locks/mutexes, parallel algorithms, and distributed systems.

Learning to handle concurrency issues (data races, race conditions) through this course enables you to identify and fix risks in AI-generated multithreaded code.

Data Communications/Computer Communications (Networking)

Teaches principles of internet and network protocols (TCP/IP, HTTP, etc.). This helps learn how data flows between computers and network performance and security concepts. Almost all modern software is connected to networks, making network knowledge essential especially for cloud services, web development, and distributed AI services.

▶ Software Engineering and Development Methodologies

Software Engineering

Covers the overall development cycle including requirements analysis, design (UML, etc.), development processes (agile, waterfall, etc.), project management, and configuration management (git). This is a meta-ability different from simply coding well - it’s methodology for leading large projects to success.

Even when AI writes code, deciding what features to implement must be human-led. Understanding user requirements, writing them as specifications, and systematically managing development schedules are high-level capabilities.

Software Testing

Covers various testing techniques (unit testing, integration testing, regression testing, etc.) and test automation. This course teaches systematic methods for ensuring software quality. Testing becomes even more important in the AI era because AI-written code may have unexpected malfunctions.

Unix Programming

Teaches system calls, shell scripting, and system tool usage in Linux/Unix environments. This develops efficient development environment utilization skills for developers. Learning CLI (Command Line Interface) familiarity and automation script writing can later help with AI development environment setup and build/deployment automation.

▶ Data and AI Courses

Database

Teaches efficient data storage/retrieval methods through SQL usage, normalization, transactions, and indexing. Since almost all modern applications handle data, DB knowledge is essential. Data modeling ability remains very important in the AI era.

AI doesn’t magically utilize data. Humans must design what data to collect in what structure for meaningful analysis or learning.

AI Introduction

Covers classical search algorithms, knowledge representation, reasoning, and machine learning fundamentals. This helps understand various approaches to problem-solving in AI’s broad context. Learning state space search or A* algorithms develops thinking processes for modeling problems and finding solution strategies beyond simple coding.

Deep Learning Practice

Literally involves implementing and applying deep learning models. You’ll likely use Python’s TensorFlow or PyTorch and work with various deep learning models like CNN/RNN and reinforcement learning. This experience helps understand how modern AI is actually built and operates.

You’ll observe model learning curves and experience the difficulties of hyperparameter tuning. This practical experience develops an eye for AI even if you don’t directly develop deep learning models after graduation.

▶ Security and Quality Courses

Computer Security and Network Security

Covers cryptography basics, authentication, access control, malware, system security concepts, and attack and defense techniques for vulnerabilities. This knowledge instills security-conscious coding habits when developing.

For example, thorough input validation, encrypting/hashing important data, and avoiding sensitive information logs are areas that developers with security knowledge naturally consider. AI-generated code doesn’t automatically guarantee security.

Career Strategy: Learning in Companies vs. Entrepreneurship

Learning from Large Companies/Existing IT Companies

A few years at a good company provides opportunities to learn not just technology but project management, large-scale system operations, team collaboration, and business domain knowledge. Well-established companies especially have systematized efficient development processes, code review culture, test automation, and DevOps pipelines, naturally teaching industry standards and best practices.

This experience becomes a great asset when starting a business or forming new teams later. For example, seeing directly how systems used by millions of users are structured and how service failures are handled is dimensionally different from learning through books.

Job Preparation Points (AI Era Version)

Job preparation processes are also changing slightly with AI’s emergence. Many companies still value data structures/algorithms coding tests and basic CS knowledge interviews - AI can’t take these for you.

However, recently, demonstrating AI utilization skills in addition can be a plus factor. For example, showing that you’ve used Copilot in personal projects or created simple ML models as side projects - demonstrating that “you’re prepared to develop efficiently in the AI era”.

Also, creating AI-related projects (e.g., adding chatbots to simple web apps) in your portfolio helps with differentiation. Companies inevitably prefer candidates who understand AI trends.

Entrepreneurship Perspective

Entrepreneurship is the ultimate comprehensive art. Technology is basic, but various abilities like market research, product planning, fundraising, and marketing are needed. Fortunately, from a technical perspective, AI has made it possible for small teams to accomplish much more.

For example, prototypes that used to take 10 people 6 months can now potentially be created by 2-3 people in a short time with AI coding assistants. Therefore, if you dream of entrepreneurship, you can adopt strategies to develop products efficiently by maximally utilizing AI.

The path of gaining experience in companies before entrepreneurship is especially recommended. Know-how gained from developing/operating large-scale user services in companies becomes a great strength when starting businesses. Also, industry networks and domain knowledge gained through work can lead to new business ideas.

If you decide on entrepreneurship, what problem the business solves is important in the AI era. Rather than developing AI itself, ideas that innovatively solve specific field problems using AI will be promising. Since there are many AI application opportunities in medical, education, environment, transportation, and other fields, pay attention to trends and problems in various fields.

Promising Roles in the AI Era

ML Engineers/Data Scientists: Obviously popular positions
MLOps Engineers: Model deployment/operations specialists with expected increased demand
Application developers who integrate AI into products: App developers with AI features, etc.
Developers with AI ethics/regulatory expertise: Designing AI utilization while complying with data regulations like GDPR
Product Managers (PM) and Tech Leads: Abilities to establish AI utilization strategies are required

Ultimately, rather than development positions decreasing in the AI era, new specialized fields and convergence areas are emerging, so rather than focusing on just one thing, trying various experiences to find what suits you is good.

Conclusion

The anxiety you feel now can be a good catalyst for preparing for the future. Having this kind of problem awareness itself means you’re prepared to move ahead. Don’t fear AI; actively utilize it while developing uniquely human strengths.

As NVIDIA CEO Jensen Huang said, “People who use AI as a tool will replace those who don’t.” This means those who collaborate with AI rather than compete with it will be the winners.

Continue your studies by absorbing theory as much as possible and validating your skills through practice and projects. Using AI alongside will show tremendous growth by graduation compared to now. Your perspective on AI will also change - from “AI does many things better than me” to “I can do better thanks to AI”.

“AI will fundamentally change the meaning of software development, but human creativity and problem-solving abilities will become even more important. Conceptualizing complex problems and designing for special requirements remains the domain of human experts, and AI will only make such experts more powerful.”

Referenced Materials:

Verdi, S. (2024). Hard and soft skills for developers coding in the age of AI. GitHub Blog
Burak, A. (2023). Will AI Replace Programmers? The Real Impact on Coding Jobs. Relevant Software Blog
Brainhub. (2025). Is There a Future for Software Engineers? The Impact of AI. Brainhub.eu Report
Business Insider. (2025). Google chief scientist predicts AI could perform at the level of a junior coder in a year. (Interview with Jeff Dean)
Korostelev, F. (2023). Surviving and thriving as a software developer in the age of AI. Pragmatic Engineer Newsletter