Your personal project will probably fail—what matters is how much risk you reduce and how much you learn from it
As we face uncertainty in the job market, I’m probably one of thousands of product makers trying to use AI to turn personal projects into reality. The sad part is that most of these projects will fail once they’re published. It hurts and it can be pretty demotivating, but I don’t think failure is the end of the journey. It’s all about how we approach it and what we’re willing to learn from it.
One thing I’ve realized is that we tend to turn these personal projects into “passion projects,” but we often misunderstand where that passion really comes from. In my case, it’s not about the ideas themselves; it’s about the process. So the core goal is not just to make one idea succeed, but to become better creators over time. It’s a craftsmanship mentality.
This new power to build almost anything we want with AI is what’s fueling this wave of solo creators. But it’s also a curse, because we tend to jump into execution too fast. Speeding up development isn’t necessarily bad—I’m a big fan of quick iterations—but the inevitable side effect is that we skip the “boring” planning phase. And I don’t think planning has to be boring at all. The problem is that the current paradigm treats it like a pre-design chore instead of a real part of the creative process.
For me, planning is essentially a conversation. It follows the same rules as a good design process: ideas are shaped step by step as we test and refine our hypotheses. This is where I see a lot of value in using AI as a tool—to help us answer the questions we naturally have when we look at our own ideas with a critical eye.
Here’s a real example from a personal product I’m working on. My initial hypothesis was:
What if I use existing documentation people are already familiar with, like Google Docs or Sheets, as inputs to generate useful tools with AI?
A simple flow: you give the system a PDF or a Google Doc link, and it helps you turn that into a tool. It sounds perfect. It reuses existing knowledge and feels like a practical way to apply AI in industries that aren’t very eager to experiment with new tech.
But of course, it came with a lot of unknowns: Who would really benefit from this? What real-life use cases would it actually solve?
I asked these questions to different models (I found Claude to be more honest than ChatGPT for this kind of critical thinking), being careful not to seek validation but concrete, verifiable answers. I wanted a clearer picture of when this could be an efficient solution to a problem I still didn’t fully understand.
After a few iterations, the idea shifted to:
What if I create a tool for salespeople to quickly build high-quality sales pitches from a Google Doc?
At its core, the value and functionality are still the same, but now it’s more focused. I have a clearer audience and a problem I’m more confident I’m actually solving—one that seems more realistic to monetize, based on the data and feedback I gathered with the help of AI.
This process of planning and shaping ideas with AI didn’t just help me focus on use cases with better chances of success. It also forced me to be more realistic about scope. For me, the real enemy of personal projects is the big dream that turns what should be a one-month project into a six-month unfinished one.
I also understand that even if AI helps reduce some risks and makes it easier to build software, the failure rate will still be high. Scalability will still be a big technical barrier that coding alone can’t fix efficiently. Being aware of these limitations is important, because it reminds us why thinking small is usually the best strategy. The real value of small personal projects is learning—and the best way to maximize learning is to fail faster, learn from it, and make it better next time.