There is a better way, and the fact that you are asking the question means you already sense it. Spending eight hours building a financial model in Google Sheets for a single stock is not dedication, it is a symptom of a broken workflow. By the time you finish your projections, the market has already moved on, and the thesis that originally excited you has likely shifted. That is not a sign of poor effort; it is a sign that the tools you are using were not built for the pace of modern investing.
The real problem here is not your discipline or your analytical rigor. It is the false choice between blindly trusting analyst consensus and spending your entire weekend buried in spreadsheets. Analyst consensus is a useful starting point, but it is a blunt instrument. It averages out the opinions of dozens of analysts who may have different models, different assumptions, and different time horizons. You are right to want something more precise. But the full DIY approach, as you have discovered, is unsustainable. It consumes time you do not have and produces outputs that are stale before you can act on them.
What you need is a middle ground that respects both your need for control and your need for a life outside of spreadsheets. That middle ground exists. It involves tools that can automate the data gathering, the formula wiring, and the sensitivity analysis that currently takes you hours. Imagine being able to pull in historical financials, run projections, and test scenarios in minutes rather than days. Imagine a model that updates itself as new data becomes available, so you are always working from the most current picture. That is not a fantasy. It is what AI-native spreadsheet technology can do today.
The serious retail investors who are not burning their weekends are the ones who have stopped treating spreadsheet construction as a badge of honor. They have adopted smarter workflows that let them focus on the judgment calls, the assumptions about growth rates, margins, and competitive dynamics, rather than the mechanical drudgery. They still build models. They still challenge consensus. But they do it in a fraction of the time, and they do it with tools that keep their analysis alive and responsive to change. The question is not whether you should stop building models. It is whether you are willing to let better tools help you build them faster, so you can reclaim your weekends without compromising your conviction.