“Simple can be harder than complex: you have to work hard to get your thinking clean to make it simple. But it’s worth it in the end because once you get there, you can move mountains.”
– Steve Jobs
I named my educational platform ASimpleModel.com after an exhausting exercise.
When I landed my first M&A job, I was sent to a financial modeling bootcamp. Two days of step-by-step instruction to complete a detailed Excel template and arrive at a result I did not understand.
Back in my cubicle, I rebuilt the model from scratch using a fictional income statement and balance sheet of my creation with only the essential line items.
This step took a substantial amount of time because I didn’t understand financial statements at the time. When I was done, the goal was simple: build the model with the minimum number of formulas and links required.
I recall thinking that it was the only way to understand the process. I was unknowingly committing to a process of eliminating unnecessary complexity and building foundational knowledge, a practice I now follow deliberately.
That model became the three-statement model still available on ASM today. I built it in 2009, and at my father’s suggestion, I posted it online in 2013. I haven’t felt the need to update it since.
Once you understand the foundation of a financial model, adding complexity is easy (rows and columns that follow similar logic). Without it, complexity becomes pattern recognition without understanding.
This matters even more in the age of AI.
People ask what happens to analysts when models are built for us. We do this without acknowledging that the financial statements used to build models are already provided. They arrive as a product of accounting choices and management judgment. Uninformed analysts rarely question them and move on to the mechanical process of projecting them into the future.
The work was never solely about getting the mechanical process right. That step helps you work towards something more important: what drives cash flow.
If you do not understand the financial statements and three-statement framework, you can use the AI output, but you will not know when it is wrong. AI will create an illusion of fluency that allows error to compound for those that haven’t committed to learning.
Learning feels hard when it’s working, and the hours I spent toiling in Excel shaped my understanding. I learned on the job because that’s how it was done. Absent this process, analysts may have to build this skill sooner and independently without mechanical repetition.
Judgment and AI.
Across all investments, there is one constant theme: risk. “Risk means uncertainty about which outcome will occur and about the possibility of loss when the unfavorable ones do.” Humans have very different opinions about risk, and AI cannot remove human preference for risk. Formulating these opinions for businesses relies on a strong understanding of the three-statement model and how cash moves through it. In other words, judgment relies on structured interpretation of fundamentals under uncertainty.
Simplified: Risk analysis is science. Risk tolerance is human. AI can inform the former, but it cannot replace the latter.
The most relevant ASM series in this context is the Introduction to Financial Statements. It begins with the accounting equation and accelerates quickly. The objective is simple. Eliminate complexity by mastering what is essential.
While the original three-statement model has not required updates, I have continuously expanded this series with additional context and video. Not because the foundation changed. But because understanding it at its core is paramount.
Simplicity is a discipline. It requires doing the hard thinking so others can move faster.
That idea shapes everything we build at ASM.
If a model cannot be explained simply, the thinking is not clean. If the economic engine cannot be described in plain language, it is not yet understood.
Excellence is built on fundamentals and experience.