Note to the image of the post AI vs Traditional Software: It was difficult for AI to generate Venn diagrams with much text. Grok 3 made the best attempt as seen in the image. The prompt included the following:
AI: “Learns from data”, “Adaptable”, “Complex”, “May be susceptible to bias”, “Can be difficult to interpret”, “Democratizes creative output”, “Raises ethical questions”.
Traditional software: “Rule-based”, “Predictable”, “Explicitly coded”, “Greater control over logic”, “Clearer documentation”, “Emphasizes user experience”.
Overlapping section: “Problem-solving”, “Automation”.
Introduction
Artificial intelligence (AI) is rapidly changing the world as we know it. From self-driving cars to facial recognition software, AI is already having a major impact on our lives. But what exactly is AI, and how does it differ from traditional software? In this blog post, we’ll explore the key differences between AI and traditional software, based on an article by Cliff Berg and a report on the topic by Gemini 1.5 Deep Research. We’ll also discuss some of the challenges and opportunities that AI presents. By the end of this post, you’ll have a better understanding of what AI is and how it’s shaping the future of technology.
What is Traditional Software?
Traditional software, also known as conventional software, is a set of instructions or programs that tell a computer what to do. It operates based on predefined rules and algorithms explicitly coded by developers. These instructions are written in a programming language that the computer can understand and execute. Traditional software is designed to perform specific tasks, such as word processing, data analysis, or gaming. It is typically created using a linear and predictable development process, where developers write code to execute specific instructions.
What is AI?
AI, or artificial intelligence, involves creating models that learn from data and adapt their behavior based on patterns and insights derived from that data. This involves tasks such as learning, reasoning, and natural communication. AI models are not explicitly programmed with rules but instead learn from data and adapt their behavior based on patterns and insights derived from that data. This makes AI systems well-suited for dynamic environments and problems with evolving requirements.
Key Differences Between AI and Traditional Software
While both AI and traditional software aim to solve problems, their approaches differ significantly. Traditional software development follows a more linear and predictable path, with developers writing code to execute specific instructions. AI development, on the other hand, is more iterative and data-driven. It involves training models on large datasets, evaluating their performance, and refining them to improve accuracy and adaptability. This process often requires close collaboration between data scientists, subject matter experts, and software engineers.
AI excels in handling complex tasks, analyzing vast amounts of data, and making predictions based on patterns. Its ability to learn and adapt makes it well-suited for dynamic environments and problems with evolving requirements. However, AI systems can be susceptible to bias if the training data reflects existing prejudices. They can also be difficult to interpret and debug, leading to challenges in ensuring transparency and accountability.
AI has also democratized creative output by removing the need for natural creative talent. This raises ethical questions about intellectual property and ownership, which will be discussed later in this report. Traditional software, while less adaptable, offers greater control over logic and decision-making processes. This allows for clearer documentation and reasoning, resulting in higher trust and accountability. Traditional software development also emphasizes user experience and interface design, ensuring applications are user-friendly and accessible.
Conclusion
In conclusion, AI and traditional software are distinct technologies with their own strengths and limitations. AI’s ability to learn and adapt makes it well-suited for dynamic environments and problems with evolving requirements. However, AI systems can be susceptible to bias and can be difficult to interpret and debug. Traditional software, while less adaptable, offers greater control over logic and decision-making processes.
As Cliff Berg points out in his article, AI software is simulated neural networks, and the future of AI hardware may lie in neuromorphic systems, which could be much more energy-efficient than current processors. Understanding these differences is crucial for navigating the evolving landscape of technology and harnessing the transformative power of AI to benefit society and drive innovation across various industries.
Sources
- I first saw Dylan Curious YouTube video about Berg’s article
- Berg, C. (2025, February 9). Why AI is not software. Medium. Retrieved from https://cliffberg.medium.com/why-ai-is-not-software-07a2188e3c13
- Gemini 1.5 Deep Research. (2025, February 26). Why AI Is Not Software: A Deep Dive
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