Can AI Break the Straight Lines of Thought?

From Greek Geometry to Digital Reality

1. The Obsession with Simplicity: A Historical Perspective

For more than 2,000 years, humanity’s quest to understand the world has been guided by an obsession with simplicity and order. This drive hasn’t just shaped our science and mathematics—it has rippled into how we build societies, design technologies, and even structure our thinking.

The Greek Foundations

In ancient Greece, Plato (427–347 BCE) argued for the existence of perfect forms—ideal geometric shapes that, though absent in reality, defined what was true and “real.”
A few generations later, Euclid wrote Elements (c. 300 BCE), a mathematical masterpiece estimated to have been published in over 1,000 editions, making it the second most printed book after the Bible.

Greek geometry rested on clear axioms—points, lines, and planes—always seeking absolute certainty and symmetry. The result was a worldview defined by straight lines, not only in mathematics but also in our thinking and institutions.

Yet, this pursuit of clarity came at a cost: ambiguity, complexity, and the “gray areas” of life were often suppressed. Over centuries, society became trained to seek black-and-white answers, reducing complexity to simple binaries that shaped everything from morality to politics.

2. Mathematical Modeling: From Maps to Minds

As time went on, the quest for simplicity became embedded in the models we use to explain the world.

Urban Planning: Models Dictate Reality

By 2022, over 55% of the world’s population lived in urban areas. City planners, architects, and engineers increasingly rely on standardized software—used by millions worldwide—to design streets, blocks, and public spaces. The result? Cities from Los Angeles to Shanghai are starting to look uncannily similar, losing unique regional character in favor of repeatable, predictable, straight-line layouts.

This isn’t limited to urban design. Between 2000 and 2020, an astonishing 95% of scientific papers used some form of mathematical or computational modeling. These models, out of necessity, simplify reality—turning the complexity of the world into something easier to measure and manipulate.

But there’s a catch:
Simplicity is a map, not the terrain. The more we use models to define the world, the more we risk thinking with the model instead of about it. This shift impacts how we define success (test scores, GDP, social media likes), organize society (categorizing, labeling, optimizing), and even understand ourselves (identities as checkboxes or tags).

3. Does Understanding Require Sorting?

Human Minds vs. AI Algorithms

a) The Human Need for Sorting

The human brain naturally sorts, orders, and categorizes. To compare or understand anything, we instinctively line up objects, ideas, or experiences—whether mentally or physically.

Psychologists estimate that humans make hundreds of snap categorizations each minute, from recognizing faces to parsing language. But this is not a neutral act: by imposing artificial order, we risk erasing natural connections and properties.
For example, sorting wildflowers by color might obscure relationships based on habitat or genetics. Simplifying the world into neat categories can make us blind to emergent patterns—those that only appear in the original, “messy” context.

So while sorting brings clarity, it can also act as a blinder, preventing true discovery.

b) Does AI Need Sorting?

Here’s where things get interesting. Classic computer algorithms—like sorting numbers in a spreadsheet—depend on order to function.
But modern AI doesn’t operate like this.

Deep learning systems, which now power everything from image recognition to language models, actually thrive in disorder. When training a neural network to identify images, for example, millions of pixel arrays are fed into the model without sorting. The AI finds meaning in the chaos.

Language models, like GPT, process words in the order written but learn to extract context, relationships, and patterns—not from sorted data, but from messy, real-world examples.

Key statistics and evidence:

In AI, data augmentation—randomizing, shuffling, and even intentionally “messing up” input—is standard practice to improve learning.
Convolutional neural networks (used in vision) and transformer models (used in language) are designed to find order in high-dimensional, unordered data.
In 2021, DeepMind’s AlphaFold revolutionized biology by predicting protein structures from unsorted amino acid sequences—a feat thought impossible using traditional “order-based” logic.

So, AI can—and often does—understand without sorting. In fact, maintaining the natural order or even embracing chaos helps AI reveal hidden patterns that human sorting might erase.

c) The Future: New Ways of Discovery

This means AI can help us see what we’d normally miss by preserving and analyzing the chaotic, unfiltered reality. AI’s ability to handle and learn from unordered data could unlock new laws of nature, hidden social patterns, or complex relationships—precisely because it doesn’t force a “straight line” where none exists.

4. Can AI Break This Mold?

The Unique Potential of AI

Artificial Intelligence, especially today’s deep learning systems, is fundamentally different from anything that came before. With neural networks containing hundreds of millions—or even billions—of parameters, AI thrives in complex, nonlinear, high-dimensional spaces.

AI doesn’t need to reduce everything to binary choices. Instead, it can embrace ambiguity, context, and nuance.

Examples in action:

Facial recognition AIs can identify individuals among billions, even in noisy, chaotic environments.
AI-based urban design tools now simulate entire neighborhoods based on organic human behaviors, rather than rigid templates.
Personalized education platforms, used by over 200 million students globally, adapt to each learner in real-time—moving away from fixed grade levels and straight-line progressions.

With AI, the world moves from sharp lines and boundaries to gradients, probabilities, and context. “It depends” becomes the new answer, allowing more dynamic and adaptive solutions in urban planning, education, medicine, and even ethics.

5. The Crossing Line: A Paradigm Shift in Thought

Throughout history, every leap in civilization—agriculture, science, industry, information—came from breaking free of old, limiting models.
AI may be the first technology capable of showing us that not everything requires a definitive answer.

According to a 2023 McKinsey report, over 70% of global businesses are now experimenting with AI, not just for efficiency, but for innovation and creative problem-solving.

The real question isn’t “Will AI think like us?” Instead, we should ask: Are we ready to let AI teach us how to think differently—accepting ambiguity, complexity, and interconnectedness as the new normal?

Conclusion: From Straight Lines to Organic Patterns

Greek geometry gave us the straight line—a powerful metaphor for centuries of order and clarity. But human life rarely follows a straight path; it curves, twists, and blends.

AI may be the first technology that invites us to model the world not in black and white, but in the full spectrum of reality. By embracing the organic, dynamic patterns all around us, AI could reshape not just our technology or our cities, but our very understanding of what it means to be human.

By Hamid Porasl

@Bazaartoday