The CEO of Stability AI, Emad Mostaque, recently sparked debate across the internet by predicting that human programmers will no longer exist within five years. He took it a step further with a bolder and more alarming forecast, suggesting that outsourced coding jobs in India would vanish in just two years! This statement prompted many online commentators and social media critics to question the "stability" of his claim. Given that his company is named "Stability AI," people were surprised by this seemingly "unstable diffusion" of information. However, as Steve Jobs famously reminded us, we should be kinder to those who believe they can change the world—those "square pegs in round holes."
In reality, long before GenAI emerged, scientists and engineers were hard at work developing low-code/no-code platforms, particularly in the fields of Data Science and Machine Learning. A quick look at the steady rise of these platforms in Gartner’s Magic Quadrant for Data Science, starting from 2017, reveals how these platforms have steadily "taken over the quadrant." In fact, over the past five years, low-code platforms like Dataiku, Alteryx, KNIME, and RapidMiner have dominated the top quadrant.
As Simon Sinek famously taught us, to inspire action, one must begin with "why." Just as great leaders inspire through this approach, groundbreaking innovations also fundamentally transform human life and progress. So let's start there.
Why do we need Low-Coding/No-Coding platforms?
The "why" can be broken down into three key aspects: 1) Democratization 2) Industrialization 3) Efficiency
#1. Democratization – We can define it as “AI/ML and data science for all”. Data science offers companies a competitive advantage, yet there remains a significant shortage of readily available data science talent. Forward-thinking organizations have therefore extended data science and AI/ML responsibilities to non-traditional roles like “Citizen Data Scientists”. Gartner coined this term to describe individuals whose primary role isn't in analytics or statistics but who can be trained to perform predictive and prescriptive analytics. Low-code/no-code platforms empower these roles, enabling the broader adoption and application of AI/ML throughout the business.
#2. Industrialization – We can express this as the “Rapid scaling of data science and AI/ML” across the organization. The drag-and-drop and plug-and-play features of these platforms, which use reusable components (or "accelerators"), allow new solutions to be developed in roughly half the time compared to traditional coding methods (SAS/R/Python + analytics databases). For instance, while the "coding world" might produce a few models within a certain timeframe, the "low-code world" can generate thousands within the same period.
#3. Efficiency – This refers to the development of data science solutions “With half the people, in half the time”. If 20 data science tasks took 20 people 20 days in a traditional coding setup, the same tasks could be completed by 10 people in 10 days in a low-code environment.
How are Low-Coding Platforms changing the way we work? How is working on a typical data science project different?
Teams utilizing low-coding platforms can now conduct real-time analytics during meetings. This represents a transformative shift, eliminating the traditional weeks or months-long wait for business questions to be answered. Ad hoc requests can now be addressed in real-time, immediately upon being raised.
In traditional coding environments, analysts must query databases to retrieve data (requiring SQL knowledge), then analyze it using tools like SAS/R/Python, and finally present findings using visualization tools like PowerBI, Tableau, or Qlik. In contrast, the "low-coding world" enables all these tasks to be performed on a single platform without requiring a team with multiple skill sets. Additionally, these platforms allow coders to integrate R/Python scripts directly into workflows, enabling them to focus more on analysis, insight generation, and model accuracy rather than switching between platforms and perfecting syntax. This is a completely new approach to Data Science projects.
What are the benefits of using a “Low-Coding Platform”?
The benefits are twofold – impacting both the "top line" and the "bottom line." More data-driven decisions can be made, and AI/ML-driven intelligent decision-making systems can be scaled across the business, driving top-line growth. At the same time, companies can reduce their data science teams by half while maintaining the same output, directly affecting the bottom line through cost savings and achieving economies of scale.
Finally, coming back to the core question of “whether GenAI will signal the end of low-code/no-code platforms”. In my opinion, the answer is a clear “no.” Even if GenAI generates code, understanding how to build analytics and AI/ML solutions will remain essential for using that code effectively. The code generated by large language models (LLMs) will still be complex and unreadable for most non-coders. This is where low-coding platforms hold a distinct advantage—their visual coding interfaces provide a level of simplicity and transparency unmatched by current technology. Perhaps, in the future, LLMs will be able to generate workflows similar to these platforms, just as they currently produce code. The ultimate outcome would remain the same: a global shift toward adopting low-coding