Let's Be Honest. Isn't "Generative AI" Just A Better "Automation"?
By Saeed Mirshekari

April 1, 2024

Debunking the Myth: Why Gen AI is Just a Better Automation with Absolutely Zero "Creativity"

In the realm of artificial intelligence, the term "Generative AI" (Gen AI) has gained traction, representing a new wave of AI models capable of creating content autonomously. However, amidst the hype surrounding Gen AI, there's a prevailing argument that it is merely a form of automation, rather than a groundbreaking leap in AI capabilities. In this blog, we'll delve deeper into why Gen AI can be viewed as just automation, debunking some of the myths surrounding its perceived novelty.

Understanding Generative AI

Generative AI refers to AI models that have been trained to generate new content, such as images, text, or music, that closely resembles content created by humans. These models, often based on deep learning techniques like neural networks, have demonstrated remarkable proficiency in various tasks, from generating lifelike images to composing music compositions.

The Automation Argument

Critics of Gen AI argue that it is fundamentally a form of automation, akin to traditional automation tools used in manufacturing or data processing. Just as automation tools streamline repetitive tasks by replacing human labor with machines, Gen AI automates the process of content creation. Instead of humans manually generating content, AI models do it automatically based on input data and predefined parameters.

Debunking the Myths

Myth 1: Gen AI is Revolutionary

Reality: While Generative AI has undoubtedly made significant strides, it builds upon existing technologies and techniques in machine learning and deep learning. For example, GPT-3, one of the most advanced Generative AI models, is built upon the foundation of earlier models like GPT-2. Rather than representing a revolutionary breakthrough, Gen AI is an evolutionary step forward in AI capabilities.

Myth 2: Gen AI is Creative

Reality: Generative AI can produce content that is remarkably similar to human-created content, but it lacks true creativity and understanding. AI models operate based on patterns and statistical analysis of existing data, rather than genuine creativity or intuition. For example, an AI-generated piece of music may sound pleasing to the human ear, but it lacks the emotional depth and complexity of a composition by a human musician.

Myth 3: Gen AI is Autonomous

Reality: Despite its impressive capabilities, Generative AI still requires human intervention and oversight. AI models must be trained on vast amounts of data and fine-tuned by human experts to achieve desired results. Additionally, humans play a crucial role in providing context, creativity, and critical thinking that AI lacks. For example, while a Generative AI model can generate text based on a prompt, it's up to humans to interpret and validate the results.

The Fallacy of True Creativity

Reality: One crucial aspect often overlooked is that Generative AI relies heavily on the input data it receives. Without a diverse and creative dataset, AI models struggle to generate truly novel or creative output. In essence, there is nothing inherently creative about Generative AI itself—it merely synthesizes existing patterns and data to produce new content. Without constant exposure to fresh and innovative ideas, Generative AI can quickly become stale and repetitive, failing to deliver the creative spark that humans bring to the table.

The Role of Human Expertise

Human expertise remains essential in the development and deployment of Generative AI. For example, in the field of design, AI-powered tools can assist graphic designers in generating visual assets quickly and efficiently. However, human designers are still needed to provide creative direction, refine the output, and ensure that the final product aligns with the client's vision.

Practical Applications of Gen AI

Generative AI has numerous practical applications across various industries. For example:

  • Content Creation: AI-generated articles or videos can supplement human-created content, especially in scenarios where scale and speed are essential. News organizations may use AI to generate news articles based on raw data, freeing up journalists to focus on investigative reporting and analysis.

  • Design: AI-powered tools can assist graphic designers in generating visual assets quickly and efficiently. For example, Adobe's Project Scribbler uses AI to turn simple sketches into photorealistic images, helping designers explore ideas more rapidly.

  • Entertainment: AI-generated content has also made its way into the entertainment industry. For example, AI-generated music compositions can be used in video games or film soundtracks, providing composers with a wealth of creative possibilities.

The Ethical Considerations

As with any form of automation, Generative AI raises important ethical considerations. Issues such as data privacy, bias in AI algorithms, and the impact on employment must be carefully addressed to ensure that AI technologies benefit society as a whole.


In conclusion, while Generative AI may be hailed as a revolutionary advancement in artificial intelligence, it can be more accurately described as a form of automation. By debunking the myths surrounding Gen AI and understanding its role in automating content creation, we can better appreciate its practical applications and ethical implications. Ultimately, Gen AI represents a valuable tool that, when used responsibly and in conjunction with human expertise, has the potential to enhance productivity and creativity across various domains. However, it's essential to recognize that true creativity stems from human ingenuity and innovation, and Generative AI should be viewed as a tool to augment, rather than replace, human creativity.

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In O'Fallon Labs we help recent graduates and professionals to get started and thrive in their Data Science careers via 1:1 mentoring and more.


Saeed Mirshekari

Saeed is currently a Director of Data Science in Mastercard and the Founder & Director of OFallon Labs LLC. He is a former research scholar at LIGO team (Physics Nobel Prize of 2017).

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