Why English is the Next Programming Language You’ll Learn

he idea that English is becoming a new coding language isn’t just a theoretical concept—it’s gaining traction among tech visionaries who see it reshaping the industry. Let’s explore what some key figures are saying about this shift.

Andrej Karpathy and Vibe Coding

Andrej Karpathy, a pioneer in AI and former director of AI at Tesla, has been a vocal advocate for this evolution. In a tweet from January 25, 2023 (link), he coined the term “vibe coding” to describe a programming style where developers focus on expressing high-level ideas and intentionswhile AI handles the technical details.

This approach dovetails perfectly with the rise of English as a coding language.

Varun Mayya on Communication and Expertise

Entrepreneur Varun Mayya offers a nuanced take on this trend.

In a post on X, he wrote, “Imo most programming will be done in natural language in the future, but you still need to know what you’re doing”

Mayya’s perspective underscores a critical balance: while English and AI can simplify the act of coding, foundational knowledge remains essential. For Mayya, English isn’t just a tool for instructing machines—it’s a medium for humans to channel their expertise into technological solutions.

Sam Altman’s Prediction

OpenAI CEO Sam Altman adds a sense of urgency to this narrative. He has predicted that coding as we know it will undergo a radical transformation by the end of 2025, a shift likely fueled by advancements in AI.

This bold forecast suggests that the integration of natural language into programming isn’t a distant dream—it’s an imminent reality. Altman’s vision reinforces the idea that tools enabling English-based coding could soon dominate, fundamentally altering how we create software.

The Implications: Opportunities and Challenges

Democratizing Technology

One of the most exciting implications of this shift is how it democratizes technology. By enabling people to interact with machines using natural language, barriers to entry in the tech industry are lowered.

Imagine a designer building a website, a marketer analyzing data, or a student automating a task—all without needing to learn a programming language. This could spark innovation from diverse perspectives, as more people gain the ability to create and experiment with technology.

The Challenge of Ambiguity

However, English as a coding language isn’t without its challenges. Natural language is inherently ambiguous and context-dependent, which can lead to misunderstandings between humans and machines.

For instance, a vague prompt like “Make it better” might confuse an AI, whereas a specific instruction like “Increase the font size to 16px and change the color to blue” yields better results. This is where the art of prompt engineering becomes critical—crafting prompts that are clear, specific, and tailored to the task.

As AI improves its ability to interpret nuance and context, these challenges may diminish. But for now, effective communication with machines requires a level of skill and intentionality.

The Future of Skills and Education

This trend also raises questions about the future of programming education. If AI can handle much of the coding, what should aspiring developers focus on? The answer likely lies in a hybrid skill set:

Coding Fundamentals: Understanding languages like Python or JavaScript remains valuable for precision and control, especially when AI outputs need tweaking.

Communication Skills: Fluency in English (or another natural language) is essential for collaborating with AI tools effectively.

Problem-Solving: Defining problems and envisioning solutions will always be a human strength.

Beyond individual skills, the rise of English as a coding language has profound implications for industries and society.

Here are a few possibilities:

Healthcare: A doctor could describe a patient’s symptoms in English, and an AI could suggest diagnoses or treatment plans.

Education: Students could learn complex subjects by asking questions in natural language, with AI tutors providing tailored responses.

Business: Entrepreneurs could prototype apps or automate workflows by simply describing their ideas.

This could accelerate innovation and problem-solving across domains, but it hinges on our ability to communicate effectively with AI systems.

While the potential is immense, it’s worth keeping a critical eye on this trend. AI isn’t infallible—there are still limitations to what it can achieve with natural language alone, especially in complex or specialized domains.

Over-reliance on AI for coding could also lead to a loss of foundational skills, making it harder to troubleshoot or innovate when the AI falls short. The key is balance: leveraging AI as a tool while maintaining the human expertise that drives true breakthroughs.