Rise of Gen-AI - Knowledge isn't enough
If you are worried that AI will take away your job, it probably will.
We often fear what we do not understand, and we often fear in anticipation of a perceived outcome. The only way to eliminate fear is by going through it rather than dancing around it.
Artificial Intelligence isn’t a new phenomenon. Alan Turing created the first workable machine to break the "Enigma" code. It was a rule-based system designed to make sequential decisions required to decode messages—and he was successful. But the 1940s were a long time ago. While significant work was done in the following decades, true advancements occurred in the 1980s when microprocessors saw their first major wave of innovation.
In the 1980s, we witnessed a huge breakthrough in microprocessors. We could now handle multiple processes using the same architecture by selectively allocating resources to different tasks. We could manage multiple computations simultaneously and control them via advanced algorithms. The concept of allocating and deallocating processing power requires some form of decision-making, which is a fundamental aspect of artificial intelligence.
I have oversimplified artificial intelligence to arrive at the central point. This may offend those who have dedicated their lives to this field, and I apologize if my gross oversimplification has done so. My intention is to make a point, as you will see.
Artificial intelligence has always been with us, but it was once beyond the reach of common people and everyday discussions. Engineers studied the processing power of computers within the architectural frameworks of microprocessors. They even knew about supercomputers. Yet, no one worried about their jobs until AI changed its shape and form. It is Generative Artificial Intelligence that has transformed the technological landscape and shocked the world.
However, AI is not just a small tool you interact with on the internet. There are four layers to it, in descending order: Artificial Intelligence → Machine Learning → Deep Learning → Large Language Models (LLMs).
Large Language Models introduced the world to AI’s contextual power, powered by neural networks. In their current form, anyone can access vast amounts of knowledge, test ideas, and validate theories through LLMs. So, where does that leave a person who once had to work hard to acquire specialized knowledge compared to someone who now has instant access to it?
The internet solved the problem of information access, allowing people to retrieve knowledge from anywhere. Social media connected individuals and gave them platforms to share ideas, which surfaced public opinions. Google revolutionized search, enabling precise information retrieval with a simple query.
However, this created a new problem—information abundance. We suddenly had so much information that we couldn’t tell what was true. With abundance comes the problem of choice, which requires authenticity, yet we had no effective way to validate information. Generative AI tackled this challenge by adding context to raw information, transforming the landscape of the internet in ways once thought impossible.
LLMs have brought the world’s encyclopedias to your fingertips. From mastering MS Excel formulas to understanding quantum mechanics, everything is available to everyone. By adding context, LLMs improved the authenticity of information to some degree. This also means that a person in a small village in India now has the same access to knowledge as the wealthiest individuals in the world.
Even before generative AI, information was accessible, and verifying its accuracy wasn’t difficult for intelligent individuals. The real challenge was expertise. Acquiring true expertise required time, effort, and structured learning—mere information wasn’t enough. Generative AI has disrupted this process.
Now, that same person in a small village in India not only has access to specialized knowledge but can also leverage expertise to build things independently. This has dismantled the monopoly over expertise—leaving only ideas as unique assets. While LLMs can generate ideas too, there is still room for originality.
So, why go through AI’s evolution? Because eliminating fear requires confronting it. I just walked you through it.
When something is abundant, it loses value. When information is abundant, it is no longer an edge. If your advantage was simply knowing obscure facts—like who the third president of Uganda was—you are not going to make it. That edge is gone.
What is the edge now? The answer lies in what we do with abundant resources. How do we shape them? How do we transform them into something new? The edge now lies in alchemy—the ability to turn raw information into valuable, actionable insights that can propel you forward or accelerate your progress.
The internet democratized education—anyone could learn anything. AI has democratized expertise—anyone can do anything.
Here’s a question for you: If you had all the resources in the world at your fingertips, what would you do? What would you build?
The internet democratized education—you could learn anything, at any pace, at any time. What did you learn?
If you don’t have an immediate answer to either question, then you already know whether AI will replace you. The world is overflowing with resources and opportunities. It’s what you do with them that determines your future. If you can no longer add value to an organization through mere information or specialized expertise, you must cultivate skills that leverage both to create something greater.
The rise of AI is a wake-up call. When computers were new, people rushed to learn them and harness their potential. They built things using the knowledge and expertise they acquired. The same principle applies to generative AI.
The only difference? The leap required this time is far greater.