Master of Rearrangement

We can’t solve problems by using the same kind of thinking we used when we created them.—Albert Einstein

In the summer of 1975, a group of engineers at Xerox PARC had just created something incredible—the graphical user interface (GUI). It wasn’t a revolution in itself, but rather a culmination of existing innovations. They didn’t invent the mouse, the computer screen, or even the idea of interacting with digital objects through icons and pointers. What they did was combine these ideas into something more accessible, something that would later be polished into the operating systems we use today. That’s incremental innovation at its finest—a rearrangement of the known parts to create something that feels fresh but familiar.

Now, fast-forward to today, and everyone’s (including me) talking about artificial intelligence (AI), particularly large language models (LLMs), as if they’re about to rewrite the fabric of our existence. But are they really the radical force we once imagined?

Two Types of Innovation

Before diving into the world of AI, let’s get one thing straight: not all innovation is created equal. There are two main types that often come up when discussing technological advancements:

1. Radical Innovation – This is the kind of change that uproots industries and society as we know it. Think of the Internet or the iPhone. These technologies not only introduced new ways of doing things, but they also replaced existing methods and changed our behaviors.

2. Incremental Innovation – On the other hand, incremental innovation involves rearranging existing technologies to create new configurations. The result feels like a novel approach, but it’s built on the foundation of what already exists. Think of it as building a new house with old bricks.

   
   

Most people initially saw AI as the ultimate radical, imagining that it would change everything about how we work, think, and live. But in reality, AI—especially the kind powered by LLMs—falls more into the category of incremental innovation.

The Real Story Behind AI

Picture this: You’re working on a puzzle. The pieces are scattered all over the table, and each one is familiar because you’ve used these shapes and colors before. Your job is to arrange these pieces into a cohesive image. That’s essentially what AI does.

Large language models like ChatGPT, Llama, or Claude are trained on vast amounts of text, learning to recognize patterns and relationships between words. But what they’re doing is a glorified form of pattern recognition. They aren’t inventing a new language or solving an unprecedented problem. They’re taking pieces of language that already exist and rearranging them into something that makes sense.

For example, the famous AI-generated painting “Edmond de Belamy,” which sold for $432,500, wasn’t born out of pure creative inspiration. The AI behind it was trained on thousands of existing artworks, essentially remixing styles and patterns. It’s innovative, yes—but in the same way remixing a song is innovative. It doesn’t upend the world of art, but it adds a new twist to what we already know.

Incremental Steps, Giant Gaps

Here’s where the distinction becomes critical: incremental innovation, while impressive, is limited in scope. AI is great at handling tasks based on patterns, such as answering questions, creating art, or writing articles. But it falls short when faced with problems that require deep, human understanding—issues like climate change, cancer or Alzheimer’s, and other global dilemmas.

Think, for example, about healthcare. AI can analyze existing medical records, diagnose conditions, and suggest treatments. But it can’t address the bigger issue—how do we make healthcare fast and accessible to everyone? It’s one thing to diagnose a patient’s symptoms, but a whole other challenge to reform a broken healthcare system. That’s where radical innovation would come in, and AI hasn’t quite stepped up to that challenge yet.

To Sum Up…

AI, with all its brilliance, is not the radical force we once thought it was. It’s a master of rearranging the pieces we already have, making incremental improvements that can undoubtedly enhance our lives. But when it comes to the bigger picture—solving the world’s most pressing challenges—AI is still more of a puzzle solver than a problem breaker.

In the end, it’s up to humans to provide the creativity and bold thinking that will truly drive radical change. The journey continues, and AI is just one part of it—an important part, but not the whole story.

Radical innovations that will reshape the future in ways we can’t yet imagine? It is mostly about us.