What happens when creating software no longer requires writing a single line of code? When junior developers with AI assistants can produce work that once required senior engineers? When 'prompt engineering' become as valuable (or perhaps even more valuable) than traditional algorithm design?

We find ourselves at a pivotal crossroads, witnessing a fundamental redefinition of what programming means as a profession. The question educators and industry leaders now face isn't whether AI will replace programmers, but rather, how AI will reshape the very identity and skillset of tomorrow’s programmers.

Introduction

The world is seeing an unprecedented growth of the capabilities of large language models (including but not limited to ChatGPT, Gemini, Claude, Grok, DeepSeek) and AI tools (GitHub Copilot, Tabnine, CodeT5, Replit AI). These models are capable of generating code, interpreting natural language, and automating code/software development. As a result, educators, technologists, and students now face a provocative question:

“Is coding still essential when AI can do it for us?”

This is more than a philosophical dilemma; it directly affects global curricula, career preparation, and the future of computational thinking. As we navigate this shift, the debate often falls between two groups: those who believe AI diminishes the need for manual coding, and those who argue that coding remains a fundamental intellectual discipline.

To further explore this debate, the table below captures diverse views from influential tech and education leaders regarding the evolving role of coding in an AI-driven landscape. On one side, advocates of "Prompt over Program" argue for an intuitive, natural language approach to computing, suggesting traditional coding might become less central. Conversely, "Code as Cognitive Tool" supporters highlight the continued importance of programming as fundamental to creative thinking, deeper understanding, and responsible AI development.

Table: Leading Voices on AI vs. Traditional Coding Paradigms

Insights from this debate could redefine current workflows across code-based industries, reshape workforce training, and spotlight the technological competencies developers must master in an AI-driven era. Advocates like Bill Gates, Satya Nadella, and Marc Andreessen anticipate that AI-driven tools will reshape interactions with technology, potentially diminishing the role of conventional programming. However, leaders such as Steve Wozniak, Paul Graham, Sebastian Thrun, and Fei-Fei Li emphasize coding’s essential cognitive value and ethical necessity in comprehending and advancing technology. Balanced perspectives from figures like Sundar Pichai, Linda Hill, and Sal Khan illustrate a multifaceted integration, suggesting that the future may require a blended skillset combining AI literacy and coding expertise.

Decoding Coding: A Writer’s Analogy

To understand the deeper value of coding, it helps to think of it the way we think about writing. Writing is not merely the act of putting words on a page; it is a cognitive process. It requires effort. When we write, we think. We translate our thoughts into words. Writing reinforces our thinking.

Coding, in many ways, is remarkably similar to writing.

As with writing, coding isn’t just about typing characters into a machine. It requires attention, intention, and precision. When we code, we do not merely issue instructions to a computer; we engage in a form of structured reasoning. We translate abstract ideas into executable logic, much like writers translate mental models into narrative flow.

Writers often discover what they really think only as they write. (Menary, 2007) Writing helps us to channel our ideas and their orderings and even stimulate creative thinking (because every re-iteration of the wording strengthens the understanding of the very core idea that the writer is trying to convey). (Emig, 1977)  The same is true for coders. Each loop, function, or condition forces coders to confront the clarity (or fuzziness) of their own logic. The process of iterating over code mirrors the writer’s act of editing a draft; each revision tightens our understanding of the problem and its solution.

This is why so many great thinkers (whether poets, professors, or researchers) are also great writers. Similarly, many of the most impactful minds in technology (engineers, scientists, and entrepreneurs) either know how to code (Thompson, 2020) or know how to think like a coder (Wing, 2006).

In both writing and coding, creativity is not in conflict with structure; it is born from it.

Redefining the Programmer: New Roles in the AI Era

As AI takes over routine coding, the programmer’s role is shifting from code generator to code thinker. Success now depends on deeper, theory-informed, and ethically aware skills.

• Code Comprehension
   Understanding and integrating AI-generated code is key to quality and security.
• Meaning-Driven Coding
   Beyond syntax, in coding, clarity, purpose, and ethical alignment matter more than ever.
• Prompt Engineering
  The ability to (first imagine and then) describe the characteristics of the end product (software/application/infographics) with the proper context is one of the most important skills. The quality of AI-generated code directly depends on the quality of prompts.
• Critical Algorithmic Thinking
   Human judgment remains vital in choosing the right data structures and algorithms (strategies). “Right” algorithms and data structures can improve the scalability and responsiveness of the systems by multifold.
• Computational Complexity
  Essential for assessing what’s efficient and feasible as systems scale. Also, this skill is essential for judging whether the AI-generated code is the best one or not for the underlying application.
• NP-Completeness Insight
  Helps programmers to avoid chasing solutions of unsolvable problems (yes, they do exist, even in the AI era); guides toward practical solutions like approximation algorithms or heuristics.
   

Modern programmers must also bring AI/ML literacy, data awareness, collaborative skills, ethical foresight, and adaptability. The future belongs to those who don’t just code, but think, connect, and create responsibly with machines.

Education Systems in Transition: What Should We Teach Now?

As AI continues to redefine who a programmer is (blending the traditional coder with those who primarily "click" or interact intuitively with digital tools), the skills needed at workplaces are changing rapidly. Consider the following analogy: Let's say you are training someone for long jumps. You would initially train them in proper body posture, running mechanics, and jumping techniques. Now, suddenly, you offer the trainee robotic help (state-of-the-art, high-tech leg devices best suited for this task). Of course, this change is for the better, and with properly modified training, the person will now be able to jump more efficiently. However, the challenge here is that only some essential aspects of their previous training remain relevant, making it necessary to retrain them thoroughly with this new, advanced tool to achieve the best results.

The same logic applies to coding. With AI's rapid integration, retraining is essential for the current workforce. But why train the younger generation or emerging coders multiple times and confuse them? Why not give them a single comprehensive training that captures essential elements of both traditional coding and the newer AI-based approaches? Today's programming learners are tomorrow's developers, making it extremely important to "develop" the right skills and AI-enabled mindset from the start. Yet, has education kept pace with these real-world transformations? Most curricula still heavily focus on teaching students how to write precise code, even though industries now place higher value on broader skills like problem-solving, understanding complex systems, and collaborating effectively with AI. If schools and colleges don't adapt soon, students might find themselves lacking the essential digital literacy required today, potentially increasing gaps in opportunity and access. To avoid this, educators need to reconsider carefully what's taught, updating their methods to prepare students not just to code, but to think critically and communicate effectively in partnership with intelligent machines.

As AI systems increasingly write the code for us, a new kind of digital literacy is emerging. A literacy that is less concerned with typing lines of syntax and more focused on understanding systems, asking the right questions, and designing ethical outcomes. In this evolving landscape, the most valued skills are not necessarily those of the best coder, but of the best thinker: The one who understands what to build, why it matters, and how to communicate that to a machine.

This shift doesn’t devalue coding; it reframes it drastically. Just as literacy expanded from handwriting to critical reading, digital fluency is expanding beyond code to include human-AI interaction, prompt formulation, computational modeling, and ethical foresight. It is high time that all the coding educators worldwide understood this paradigm shift and made the necessary drastic changes in their teaching methodologies.

Case in Point: Python Ki Pathshala (Teaching Code in the Age of AI)

Python Ki Pathshala was designed to explore a provocative question: Can AI help students learn to code better, not just faster? In an era where natural language is fast becoming the new programming interface, Python Ki Pathshala demonstrates how AI can enhance (not eliminate) the intellectual core of coding. This custom GPT-powered tutor has already transformed the learning experience for hundreds of students by combining narrative-driven explanations, bilingual accessibility, and dynamic personalization. Its core idea is simple yet profound: when learners see themselves in the examples, coding becomes not just accessible but meaningful.

Rather than viewing AI as a shortcut that bypasses understanding, Python Ki Pathshala is an assistant that deepens it. The system uses AI not just to automate responses, but to craft personalized metaphors and real-life analogies (explaining loops through Instagram reels, functions through cooking recipes, and dictionaries as contact lists). Each learner receives contextualized content that speaks their language (literally and metaphorically) whether in English, Hindi or Hinglish. Python Ki Pathshala uses AI not to trivialize programming, but to restore it as a thinking discipline, where learning syntax serves as a framework for structured reasoning. In this redefined learning environment, AI is not a replacement for human thought; it is a reflection of it. Python Ki Pathshala offers one possible roadmap for how we might teach programming in the AI age: not by removing the rigor, but by making it relatable, inclusive, and deeply human.

Conclusion: Coding Reimagined, Not Replaced

The future of programming is not about elimination but evolution. Coding is becoming less about syntax and more about thinking, reasoning, and interacting with intelligent systems. AI tools may change how we code, but not why we learn it: to solve problems, build systems, and understand the digital world.

To thrive in this new environment, educators must:

• Foster computational thinking,
• Promote prompt literacy, and
• Emphasize ethical awareness in AI usage.

Ultimately, the goal is not to choose between “code” or “clicks,” but to prepare learners to harness both; intelligently, creatively, and responsibly.

References

Borruso, M. (2023, December 11). Author talks: Dr. Fei-Fei Li sees ‘worlds’ of possibilities in a multidisciplinary approach to AI. McKinsey & Company. https://www.mckinsey.com/featured-insights/mckinsey-on-books/author-talks-dr-fei-fei-li-sees-worlds-of-possibilities-in-a-multidisciplinary-approach-to-ai

Emig, J. (1977). Writing as a mode of learning. College Composition and Communication, 28(2), 122–128. https://doi.org/10.2307/356095

Gates, B. (2023, November 9). AI-powered agents are the future of computing. GatesNotes. https://www.gatesnotes.com/ai-agents

Graham, P. (2003, May). Hackers and painters. http://paulgraham.com/hp.html

Harvard Business School. (2022, February 14). Curiosity, not coding: 6 skills leaders need in the digital era. Harvard Business School Working Knowledge. https://www.library.hbs.edu/working-knowledge/six-unexpected-traits-leaders-need-in-the-digital-era

Houser, K. (2025, January 25). Sal Khan wants to give every student on Earth a personal AI tutor. Freethink. https://www.freethink.com/consumer-tech/khanmigo-ai-tutor

Majic, J. (2025, May 22). Software ate the world, AI ate software, and now philosophy is eating AI. Forbes. https://www.forbes.com/sites/josipamajic/2025/05/22/software-ate-the-world-ai-ate-software-and-now-philosophy-is-eating-ai/

McKenna, G. (2024, October 30). Over 25% of Google’s code is now written by AI—and CEO Sundar Pichai says it’s just the start. Fortune. https://fortune.com/2024/10/30/googles-code-ai-sundar-pichai/

Menary, R. (2007). Writing as thinking. Language Sciences, 29(5), 621–632.

Nadella, S. (2024, November 15). Inside Microsoft's struggles with Copilot. Business Insider. https://www.businessinsider.com/microsoft-ai-artificial-intelligence-bet-doubts-marc-benioff-satya-nadella-2024-11

O'Flaherty, K. (2023, May 10). Apple co-founder Steve Wozniak issues stark AI warning. Forbes. https://www.forbes.com/sites/kateoflahertyuk/2023/05/10/apple-co-founder-steve-wozniak-issues-stark-ai-warning/

Thompson, C. (2020). Coders: The making of a new tribe and the remaking of the world. Penguin.

Thrun, S. (n.d.). Articles by Sebastian Thrun. Udacity Blog. https://www.udacity.com/blog/author/sebastianthrun

Watson, M. (2024, December 9). The software engineer of 2025 won't look anything like the software engineer of 2020 [Post]. LinkedIn. https://www.linkedin.com/posts/mattwatsonkc_the-software-engineer-of-2025-wont-look-activity-7271985010176344064-QIBJ/

Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33–35.

About the author: Dr. Mahipal Jadeja is an Assistant Professor at MNIT Jaipur and a leading advocate of AI-powered education. He develops custom AI tutors, leads expert sessions, and conducts global workshops on coding, AI literacy, and prompt engineering.

The content expressed in this blog is that of the author(s) and does not necessarily reflect the position of the website owner. All content provided is shared in the spirit of knowledge exchange with our AIEOU community of practice. The author(s) retains full ownership of the content, and the website owner is not responsible for any errors or omissions, nor for the ongoing availability of this information. If you wish to share or use any content you have read here, please ensure to cite the author appropriately. Thank you for respecting the author's intellectual property.