As generative AI transforms software development, natural language commands are replacing traditional programming syntax. However, experts question whether English can ever match the precision of code. The most popular programming language of 2024 was English.
That's right, plain English, or natural language, emerged as the top language last year and continues to dominate as the main language of generative AI (GenAI), according to some experts.
Andrej Karpathy, a co-founder of OpenAI who has now started a new AI and education company called Eureka Labs, highlighted this in 2023. Brad Shimmin, an analyst at Omdia, told The New Stack that Karpathy is "spot on" with his claim.
"The biggest programming language of the year has to be natural, spoken human language, with GenAI code completion and even full-stack development tools like Aider and Cline enabling developers to use, let's say, English, as a declarative programming language in its own right," Shimmin said.
He added that he believes the entire IT industry has been moving toward this idea for decades, creating higher levels of abstraction that allow developers to focus more on "what" kind of task they want a program to execute and less on "how" the program should execute that task. In fact, Shimmin told The New Stack he believes the shift to natural language programming interfaces represents a significant evolution in software development, comparable to the introduction of compilers.
Meanwhile, using English as a programming language has allowed Karpathy to introduce a new type of software development he calls “vibe coding” (see related article). Vibe coding is a high-level approach where users describe requirements from an end-user perspective rather than technical specifications.
Some major players in the GenAI space enabling English for programming include Microsoft, OpenAI, Anthropic, Google, IBM, and AWS, among others, Shimmin noted. They are developing models with better tool use and structured outputs. Key development platforms mentioned include GitHub Copilot with VS Code, Replit (an early adopter of AI integration), Aider, Cline, Cursor, and Zed.
Natural Language (English) as the Universal Programming Language “I certainly don’t think English was the most important programming language of 2024, but it is gaining prominence,” Arnal Dayaratna, an analyst at IDC, told The New Stack.
IDC predicts that by 2028, natural language will become the most widely used programming language, with developers using it to create 70% of new digital solutions. (Source: IDC FutureScape: Worldwide Developer and DevOps 2025 Predictions)
“I think the best way to phrase this prediction is to replace ‘natural language’ with ‘English’ because English is the dominant spoken and written language worldwide,” Dayaratna said.
He also believes that in four to five years, developers will increasingly use a chatbot-like interface and natural language to create digital solutions. Meanwhile, code will be used to innovate the technology that supports this kind of development.
“In other words, we’re approaching a time when commercial off-the-shelf software will decline because it will be so easy to create custom software for an organization’s business processes,” Dayaratna said.
He explained that we are witnessing the rise of what Amjad Masad, CEO of Replit, calls the era of “personal software.”
“Just as the Mac started personal computing in 1984, generative AI has begun the era of ‘personal software’ that caters to individual and organizational preferences,” Dayaratna said.
Masad told The New Stack that it is “absolutely true” that English is currently the leading programming language.
“We now have more customers building with Replit Agent using English than we have customers coding in JavaScript or Python,” he said.
Microsoft’s Mike Hulme, general manager of Azure Digital Apps and Innovation, shared the company's perspective, stating that “AI allows us to program entirely in natural language, helping every developer code faster and more accurately while connecting new streams of developer talent worldwide. By using natural language as a common model for coding, we can overcome programming skill barriers, understand and maintain existing applications more easily, and build new AI apps in a way that is more accessible and efficient for everyone.”
Developers Will Still (and Always) Write Code Programming languages are still necessary for precise operations, according to Sriram Devanathan, general manager of Amazon Q Apps and AWS App Studio. “New programming languages may emerge at higher abstraction levels. Programming languages won’t disappear, but learning methods will evolve,” he said.
Ameya Deshmukh, head of marketing programs at Tabnine, told The New Stack that it’s not surprising OpenAI’s founders see English as the “biggest programming language” due to the volume of code-related prompts being entered into ChatGPT.
“However, in our experience, comparing enterprise-grade AI code assistants to standalone LLMs shows a clear difference: enterprise AI code assistants can achieve in seconds — with a few clicks and concise prompts — what standalone LLMs often require hundreds of words to accomplish,” he said.
Yet, mature engineering teams still write significantly more lines of code than natural language prompts, he said. “AI code assistants designed for enterprise use improve workflows by integrating smoothly into existing processes, making code creation faster and more efficient while keeping engineering teams in control,” Deshmukh explained.
Enterprise Use of Natural Language and GenAI
Enterprise software vendors like Pegasystems are adopting GenAI and agentic technology. Don Schuerman, CTO at Pegasystems, told The New Stack that GenAI has made natural language a powerful starting point for developing enterprise applications. This approach significantly speeds up the transition from an idea to a working application, reducing what used to take weeks to just minutes.
“For example, tens of thousands of users engaged with Pega GenAI Blueprint to design workflow apps using only natural language, showing how plain English has evolved from merely describing requirements to actively shaping application design,” he said.
However, app development success requires more than just converting English into code. Different stakeholders can interpret the same language in various ways, and more importantly, enterprises need their apps to be maintainable and scalable over time, Schuerman said.
Is Low-Code Dead? “That's why I believe the combination of natural language prompts and visual low-code methods creates such powerful results,” Schuerman added. “When business users can express their needs in natural language and immediately see them translated into visual business models—like workflow diagrams, case lifecycles, and sample user screens—they not only get started quickly but also ensure every stakeholder sees and understands the same solution.”
This approach maintains the speed and accessibility of natural language while providing the structure and governance enterprises need for long-term success, he added.
However, Omdia’s Shimmin disagrees with part of that.
He said traditional low-code/no-code tools might be becoming less relevant.
“I feel like they’ve sort of run their course. I mean, I think there are always tools you can use, like a rules engine, that would be very useful. But you know, this idea that low code is a market on its own? I feel like that’s not what we’re really looking at anymore. Gone are the days where low code has a market on its own…”
Democratizing Development Like low-code/no-code, GenAI allows people with little or no technical training or experience to build applications, thus increasing the number of people capable of creating applications.