It's Not Hard, You're Just Unfamiliar With It

Have you ever stared at a problem and felt that crushing sense of impossibility? That voice in your head whispering, "This is just too hard"? I used to feel that way too. Until I discovered what might be the most liberating truth about learning anything new:

This isn't just a comforting platitude—it's a fundamental shift in how we approach challenges. I first encountered this concept through

Alex Hormozi

's learning curve analogy. He described how people start at a baseline when learning something new, then gain enough knowledge to feel optimistic. But then comes the valley—real problems emerge, and most people give up. The few who persist through this phase eventually realize something profound: the difficulty wasn't inherent to the subject; it was simply unfamiliarity.

Think of it like battling a

Hydra

. Each problem you solve might reveal two more, but your sword (your brain) gets sharper with each cut. Eventually, you've seen enough problems that you approach new challenges with confidence, knowing they'll yield to your growing experience and problem-solving abilities.

I experienced this transformation firsthand during my first website project. Initially, I struggled with the traditional tutorial approach, watching endless videos yet making minimal progress on an e-commerce site with my college mates. I was drowning in abstract concepts without context.

Then came a pivotal opportunity: someone asked me to build a landing page. Facing this real-world challenge, I realized I needed a different approach. Instead of starting from scratch, I found a static

React

template, studied its structure, and used it as a foundation to build upon. This reversed approach—seeing the finished product first and working backward—provided the context that tutorials couldn't.

What had seemed impossibly complex suddenly became a manageable set of familiar patterns. That's when it clicked: the barrier wasn't the complexity of web development; it was my unfamiliarity with its patterns. The template gave me immediate context, reducing the unfamiliarity factor dramatically.

When I first started exploring web development, I quickly ran into the traditional learning roadmap: master HTML, then CSS, followed by JavaScript, and only then consider touching frameworks like React or Angular. This step-by-step approach makes logical sense on paper, but I found it frustratingly slow and abstract.

I needed a different approach. Rather than building knowledge brick by brick with no vision of the final structure, I reversed the process. I studied complete, functioning websites and applications, deconstructing them to understand how their components worked together. This context-first method was like learning a language through conversation rather than memorizing vocabulary lists in isolation.

Within three months, I went from zero experience to successfully creating and selling end-to-end freelance projects. I built an assignment automation tool using Python and

Streamlit

that gained over 30 users (students) in just a few days. I secured a web development internship and started building projects for startups in Bangalore. I implemented

Supabase

in my first full-stack project and today work with building automations for many businesses in the medical, sales, and ecommerce industries.

Each new technology I encountered followed the same pattern: initial unfamiliarity, strategic exposure to information, and eventually, mastery through connection to existing knowledge. Knowledge acquisition is like creating a new river from scratch—the water (information) is already there in the oceans of knowledge. You just have to draw a line through the ground, keep digging until the river starts flowing, and suddenly everything clears up and water starts doing the work for us.

Let's clarify what we're really experiencing when we face a new challenge.

When we label something as "insurmountable," we're suggesting it requires rare talent or extraordinary effort—creating a perceived barrier between those who "can" and those who "cannot." This framing often masks what's really happening: our fear of the unknown.

What we typically call "hard" is actually a combination of unfamiliarity and the emotional resistance it triggers. Unfamiliarity simply means you haven't yet built neural pathways for this knowledge. The discomfort and anxiety this creates—the fear of appearing incompetent or failing—is what truly makes tasks feel "difficult."

This distinction transforms your entire learning psychology. When you label something as "impossible for someone like me," you're not just describing the task—you're defining your relationship with it. Behind this label often lurks the fear of failure, of wasted effort, of public embarrassment.

Recognizing this emotional dimension allows you to address both aspects of the challenge: the knowledge gap and the fear response it triggers. Instead of asking "Do I have what it takes?", you begin asking more productive questions: "How do I break this down into manageable pieces?" and "What specific areas trigger my resistance, and why?" This dual approach—addressing both unfamiliarity and the emotional resistance it creates—builds not just capability but confidence.

Consider my recent encounter with

Multivariate Calculus

. With an exam approaching, I had just five hours to prepare for a subject that carries an intimidating reputation. Many students freeze at the mere mention of calculus, their anxiety creating a self-fulfilling prophecy of struggle. Instead of succumbing to this collective fear narrative, I recognized two separate challenges: my knowledge gap and the emotional resistance it triggered.

By addressing both—breaking the subject into pattern-recognition tasks while consciously managing my stress response—I achieved a perfect 20/20 score. This wasn't due to mathematical brilliance but rather my willingness to confront both the unfamiliar concepts and the discomfort they initially provoked. I transformed perceived impossibility into a series of conquerable territories by refusing to let reputation dictate my relationship with the material.

This wasn't because I have some special mathematical gift. It was because I refused to be intimidated by the subject's reputation and instead focused on systematically reducing my unfamiliarity with it through formula comprehension and question solving.

We're living through a profound shift in how humans access knowledge. Throughout history, we've created tools to extend our capabilities—from books to search engines, each allowing us to transcend our individual limitations. But today's AI represents something qualitatively different.

Think about this: AI has been trained on more data than any human could process in hundreds of lifetimes. It's like having access to a being that has read virtually everything humans have written, spoken, or recorded. The only barrier between you and this vast repository of knowledge is learning how to communicate with it effectively.

I call this "God in a box"—not to be irreverent, but to capture the philosophical implications of having near-infinite knowledge available at our fingertips. Just as ancient wisdom traditions spoke of meditation, prayer, or study as paths to divine knowledge, we now have a technological path to knowledge that previous generations could only dream of.

What makes this truly revolutionary isn't just the quantity of information but how it transforms the learning process itself. Before AI, people had to find books, research on their own, derive conclusions, connect dots, and absorb information through tremendous effort. Now, much of that cognitive load can be outsourced, reducing the desire threshold for learning to simply being able to write in English and communicate effectively with AI.

A crucial distinction here: modern AI models aren't just language generators—they exhibit emergent problem-solving abilities. This means they can help you tackle challenges in ways that might not be explicitly documented anywhere.

Consider my experience with an

Arduino

project. Despite having no prior experience, I could break down the complex project into manageable steps, with AI guiding me through unfamiliar territory at each stage. The technology served as a bridge across what would have been a vast expanse of unfamiliarity.

The true value isn't in having AI do the work for you—it's in learning how to communicate with this entity to accelerate your own understanding. The barrier is no longer access to information but your ability to ask the right questions and your desire to learn.

One of the most critical factors in fast learning is finding the optimal rate of information exposure—what I call the "Goldilocks Zone." Too much information leads to overwhelm; too little leads to boredom and disengagement.

When using AI to learn, you need to monitor your understanding actively. If the explanations mostly cover what you already know, you need to increase the information density. If there are too many concepts that remain black boxes, you need to slow down and unpack them one by one.

The sweet spot is when explanations not only clarify your immediate questions but also introduce 3-4 new related concepts that intrigue you. For instance, when learning about

Physical Vapor Deposition

, an optimal explanation wouldn't just define the process—it would introduce related terms like sputtering, applications in rocket fuel tanks, or creating transparent conductors for mobile phones.

This isn't just about efficient learning; it's about building a web of understanding. When studying

Linear Regression

, learning the calculus derivations and rules for partial differentiation for the

Gradient Descent Algorithm

connects to broader machine learning concepts that will reappear in neural networks and deep learning. (It's okay if you don't understand what the examples actually mean, just catch the vibe for now hahah)

Calibrating this zone requires self-awareness and active monitoring of your comprehension. The key indicators are engagement and connection—you're learning enough to stay engaged but not so much that you feel lost, and you're seeing how each new piece connects to your existing knowledge and future learning goals.

What does all this mean for you, right now, in 2025? Simply put, the courage to say "yes" to unfamiliar challenges creates an asymmetric advantage that few are leveraging.

I've seen this in my own trajectory. I said yes to that first freelancing project that kick started my career, despite not yet having the skills. I said yes to building a physics project with Arduino even though I had no prior experience. I said yes to creating AI automations, and now I'm building

CRM

s and AI assistants taking jobs of receptionists, developing cold outreach automations for agencies, and fine-tuning image recognition models for healthcare.

The ROI of this mindset is extraordinary. While others hesitate at the edge of unfamiliarity, those who dive in—equipped with the right approach to learning—can rapidly develop capabilities that the current market values highly.

This creates market separation. Most people won't adopt this approach because it requires stepping into discomfort, admitting unfamiliarity, and developing new learning methods. But this reluctance creates opportunity for those willing to embrace the new paradigm.

The result? Accelerated career trajectories, access to higher-tier projects, and the ability to pivot into emerging fields before they become saturated.

Sam Altman

recently said in a podcast with

Varun Mayya

that "having taste is a real profession" in today's AI-saturated landscape. This points to a profound shift in how we create value professionally.

As AI becomes more capable of generating content and solving routine problems, human value shifts from production to curation. The ability to discern quality, relevance, and appropriateness becomes more valuable than the ability to produce at volume.

This creates two emerging categories of professionals: those who embrace AI as a collaborative tool and those who resist it. The former group isn't just more productive—they're developing an entirely different skill set centered around judgment, taste, and strategic direction.

Consider the parallel to spell-check and grammar tools. When first introduced, these were often dismissed as crutches that would erode writing skills. Today, professional writers and editors routinely use tools like

Grammarly

or even voice dictation to alleviate the cognitive burden of mechanical editing—not because they lack writing ability, but because these tools free their mental bandwidth to focus on higher-level aspects like narrative structure, argument coherence, and creative expression.

Similarly, AI is a tool that allows us to climb higher and cross the limits of what's possible through human ingenuity alone. The key is using AI intentionally rather than being used by it—understanding the difference between leveraging a tool and becoming dependent on it.

When viewing unfamiliar concepts as

Black Boxes

, we can make progress even without understanding every detail at first. This approach allows us to build functional knowledge while gradually deepening our understanding.

While I've focused largely on technical domains, this "unfamiliarity vs. hardness" mindset applies broadly across disciplines.

Physical challenges illustrate this principle perfectly. In fitness, we intuitively understand the interplay between capability and discomfort through progressive overload. That burning sensation in your muscles isn't a signal of incapability—it's the necessary tension between current capacity and growth potential. Your body initially resists the unfamiliar load with pain signals and fatigue, creating an emotional hurdle ("I can't do this") alongside the physical one. By embracing both aspects of the challenge, you transform resistance into adaptation.

This same pattern appears in creative endeavors. A beginning guitarist experiences both technical unfamiliarity and the emotional friction of producing imperfect sounds. The anxiety of "I sound terrible" compounds the challenge of forming chord shapes. Masters aren't just technically proficient—they've developed resilience to the vulnerability that creation demands. They've learned that discomfort isn't an indicator of inability but rather the essential companion to growth.

Even interpersonal abilities benefit from this framing. Social skills aren't inherently "hard"—they simply require exposure to unfamiliar situations, from which you learn through interaction. This parallels

Jordan Peterson

's theory in

Maps of Meaning

about the hero constantly exploring unfamiliar territory to gather resources and reincorporate them to evolve beyond their current level.

The underlying principle is the same across all these domains: systematic exposure to unfamiliarity, rather than innate capability, is what determines growth.

The distinction between "hard" and "unfamiliar" isn't just semantic—it's a fundamental shift in how we approach learning and growth. By recognizing that most perceived difficulty is simply unfamiliarity, we liberate ourselves from self-imposed limitations and open doors to capabilities we might never have explored.

In our AI-enhanced reality, the barriers to reducing unfamiliarity have never been lower. We have unprecedented tools to accelerate our journey from confusion to competence, provided we develop the right mindset and methodology.

The question isn't whether you can learn something—it's how strategically you approach the unfamiliar.

In the next installment of this series, we'll explore the cognitive framework that makes this approach work, diving into the psychological and neurological foundations that explain why reframing difficulty as unfamiliarity is so powerful. We'll examine concepts like cognitive load theory, the zone of proximal development, and how the brain physically builds connections as you become familiar with new domains.

But for now, I encourage you to identify one area where you've been thinking, "That's too hard for me," and experiment with reframing it as simply unfamiliar. What small step could you take today to begin reducing that unfamiliarity?

After all, on the other side of unfamiliarity lies capability you might not yet imagine.