Every solo founder lives with a quiet, unsettling presence in the room: the specter of failure. While I was building my productivity system to help students overcome app-hopping, I was constantly haunted by a mathematical reality.
Statistics indicate that 90% of startups fail—not necessarily because the idea is bad, but often because the market doesn’t need the product or because the market was too small, along with other reasons.
I knew well that passion alone does not guarantee income, so I needed a Plan B — to gain deep, specialized expertise in another field, so that if my project failed or succeeded and was acquired, I could continue directly as an entrepreneur, without a long period of hesitation trying to figure out the next step.
For that, I chose the field of bionics, specifically bionic hands, semi-active knees (also known as microprocessor knees), and ESR (Energy Storage and Return) feet.
This choice was not random; I was seeking a field that would challenge my mind to grow, confronting real engineering problems that required patience, discipline, and deep thought.
But there was another reason as well.
Before I began studying advanced prosthetics, I noticed a clear gap between the global demand and what the market currently offers. Millions of people need advanced prosthetic devices, yet this technology remains scarce and prohibitively expensive.
In one interview, the founder of a bionic hand company mentioned that the cost to manufacture a single hand was $1,800, while it sold for $15,000. When I read that, I didn’t just think about the numbers—I thought about the opportunity behind them.
I saw a different possibility: building prosthetic devices that are technically solid but selling them at a lower cost, making this technology accessible to many more people.
For me, it wasn’t just an intellectual challenge; it was an opportunity to create something that could eventually become a real commercial venture while also benefiting people.
What followed was an intensive 14-month deep dive into academic work: 200 scientific publications, 15 theses (mostly PhDs), and a complete rethinking of how the human brain learns complex systems.
Here’s the deep-dive protocol I used to escape the trap of horizontal distraction and turn academic noise into three clear engineering design documents.
The Horizontal Distraction Trap
When I began my research, the mistake wasn’t that I wanted to learn everything at once — it was my excessive enthusiasm. Discovering the world of prosthetics opened up an incredible engineering domain, and driven by passion, I wanted to understand every part of it quickly.
I started by reading academic theses: I completed an entire thesis on designing a smart hand, then moved on to the microprocessor knee, and then the ESR foot. I jumped between topics because each part seemed exciting to explore.
But the result was relatively shallow knowledge. I knew the terms and concepts — far more than the average person — but I didn’t have the deep understanding needed to design a real product.
The outcome was a knowledge disaster: I was “learning,” but I wasn’t “gaining true understanding.” A mind full of terminology couldn’t help me draft a single complete design — and this is what I call horizontal distraction: the illusion of progress through diversity.
In the startup world, we sometimes call this “Pivot-itis” — the tendency to constantly shift project directions until the team loses momentum and can’t execute effectively. In academic research, it can lead to intellectual paralysis: you gather knowledge everywhere, yet it doesn’t add up to something you can actually create.
Vertical Mastery Transformation
After two months of spinning in a loop, I realized the human brain isn’t designed for multi-track learning — it’s built for focus. So I started working as a sequential specialist, dividing 14 months into three vertical sprints:
Phase 1: Bionic Hand
I focused entirely on designing the hand, reading scientific papers and theses on EMG signal processing and mechanical designs. The result: a complete design document for a commercially viable smart hand.
Phase 2: ESR Foot
I moved on to the foot, applying a new model from precise motion control to dynamic energy storage. I dove into biomechanics and the use of carbon and glass fibers in building these medical devices. Thanks to the discipline developed during the hand phase, I completed a development-ready design document in about four months.
Phase 3: Semi-Active Knee
I tackled the most complex of all: the knee, using the finite state machine (FSM) method to cover walking, standing, stair ascent/descent, and more.
In this phase, I developed an experience template that allowed me to extract solutions directly from lengthy theses, adding my own logic to ensure user comfort — leveraging the accumulated knowledge from the previous phases.
The Math of 215 Scientific Sources
Reading 200 scientific publications and 15 theses over 14 months, while building a startup at the same time, wasn’t possible through hard work alone. It required what I call an Information Consumption Pipeline.
I wasn’t reading academic papers just for knowledge. I was reading with a clear objective: to add a new line to my design document. So I would start by reading the introduction and abstract, quickly scanning the paper.
If I found that it contributed to solving an engineering problem or added useful information to my design specifications, I would read it in depth. If it had no direct impact on the design, I would skip it and move on to the next source.
Building My Startup vs. Studying Bionics: A Synergy
You might be wondering: “Didn’t this distract you from your startup?” If I had to give a direct and honest answer: studying bionics wasn’t a distraction — it was part of my strategy for managing time intelligently.
Before starting my research on prosthetics, I had already hired a freelance developer and clearly defined what I needed to be built.
Although the development of my app took longer than expected due to technical issues I previously wrote about in a HackerNoon article titled “Perfection Is the Enemy of Launch: Why Your Tech Stack Might Be Killing Your Startup,” I used that time to study bionics after first establishing the business and marketing groundwork for the company.
In this way, learning a new field didn’t compete with the project. Instead, it helped sharpen my intellectual discipline and turned that time into real value for both sides: my core startup and my engineering expertise.
Lessons for the Founder–Researcher
- Master one field before moving to the next: Sequential learning allows you to build deep expertise. Multitasking scatters focus and kills depth.
- Hedge with practical skills: If your project is software-based, learn the fundamentals of hardware or low-level programming to gain a real advantage.
- The design document is the real benchmark: Don’t measure progress by the number of papers you’ve read, but by the number of specifications and structures you’ve created for a practical implementation.
- Use management to buy focus: Direct your team and distribute routine and cognitive tasks so you can dedicate your mental energy to deep research and analysis.
- Turn fear into learning fuel: Use the question “What if the project fails?” as motivation to go deeper and plan better — this is one of the most productive forms of anxiety.
Conclusion
Today, my application has hit the market, and my full focus is now on making it successful. But more importantly, I am no longer just a software founder; I now possess three comprehensive design documents for the next generation of bionic prosthetics. Through this journey, I realized that the ultimate fail-safe for a founder is not primarily a bank account, but the ability to push your mind into a state of vertical mastery.
If you are building a startup today, don’t just build a product—build deep, specialized technical expertise in your field to the point where building the product feels like the easiest part of the journey. Don’t learn horizontally across dozens of scattered topics; dive vertically into one domain until you reach its depths, because real value is often hidden at the bottom.