Artificial Intelligence vs Human Intelligence: A Simplified Perspective
Classic AI: The Foundation of Artificial Intelligence
Classic AI, also known as rule-based AI, is the earliest form of artificial intelligence. It functions based on predefined rules and logic, making it highly effective in structured settings where data is well-defined. This type of AI follows a fixed set of instructions, ensuring accuracy and reliability.
It is widely used in fields that require precision, such as machine learning models for classification and regression, search algorithms, and pattern recognition in image and voice processing. Classic AI demands significant software engineering expertise to develop and maintain its frameworks.
Generative AI: The New Age of AI
Generative AI represents a significant shift from traditional AI by focusing on prediction and content generation rather than computation. Unlike Classic AI, which calculates outputs based on defined formulas, Generative AI works by identifying and learning patterns from vast datasets, allowing it to create original content such as text, images, music, and videos.
A prime example of Generative AI is GPT models, such as ChatGPT and DeepSeek, which generate text-based responses. DALL•E, on the other hand, creates images from textual prompts. However, this AI does not perform calculations. For instance, if you ask a Generative AI model for the result of 5 x 5, it will rely on existing data to retrieve an answer rather than computing it. If most datasets mistakenly record the answer as 24, the model may provide that incorrect response, as it lacks the ability to perform calculations like Classic AI does.
Agentic AI: The Future of AI
Agentic AI is an advanced form of artificial intelligence designed to operate autonomously. Unlike Classic and Generative AI, Agentic AI does not just process information or generate content; it interacts with its environment, analyzes data, makes decisions, and takes actions accordingly.
This type of AI is at the forefront of technological evolution, with applications in autonomous vehicles, AI-driven virtual assistants, and robotics. It has the potential to revolutionize industries by acting as an independent digital worker that not only executes tasks but also adapts and iterates based on real-world feedback.
The Impact of AI on Industries
Since the 1990s, India has been a major player in IT outsourcing, benefitting significantly from the global demand for software development and business process outsourcing (BPO). However, outsourcing has always faced challenges such as compliance issues, cybersecurity concerns, and data privacy risks.
With the rise of Generative AI, a substantial portion of repetitive human tasks can now be automated. According to McKinsey & Co, AI and automation technologies could take over 60-70% of an employee’s workload in many industries. Presently, 65% of the code on GitHub is AI-generated, indicating the rapid transformation of the software development sector.
The IT outsourcing market is estimated to be worth around USD 500 billion, while the insourcing market is valued at approximately USD 1 trillion. Similarly, the global BPO outsourcing market is worth another USD 500 billion. If AI adoption eliminates 40-50% of these industries, nearly USD 1 trillion in workforce-related costs could disappear, significantly reshaping the job market. Even if only 50% of these savings translate into consumer benefits, AI-based companies would still capture a USD 500 billion market that is only expected to grow.
Our Investment Perspective
For investors, the AI revolution presents both opportunities and risks. The space is highly competitive, and with technology becoming increasingly democratized, many companies are jumping on the AI bandwagon without truly innovating.
When evaluating AI-driven companies, it is crucial to determine whether they are genuine leaders in the field or simply leveraging AI as a marketing strategy. Investors must analyze whether a company is using AI to create a sustainable competitive advantage or if it is just adopting AI for the sake of appearances.
A real-world example highlights the unpredictability of AI investments. One of our portfolio companies placed a bid for a rapidly growing AI firm. However, after a routine Windows software update, the AI firm’s application became obsolete overnight, rendering its technology useless.
The pace of AI evolution is staggering, and anchoring to a single AI model or approach can prove disastrous. Adaptability is key to survival in this ever-changing landscape.
