Germán
Estrada Mendoza
Founders learn validated methods for finding product-market fit — then systematically fail to use them. I study why, and I teach the courses designed to close that gap.
Founders learn validated methods for finding product-market fit — then systematically fail to use them. I study why, and I teach the courses designed to close that gap.
Most founders who fail at the pre-PMF stage don't fail because they lack information. The playbooks exist. Lean Startup, The Mom Test, customer discovery frameworks — available to everyone. And still, the overwhelming majority of startups die before finding product-market fit.
I spend most of my time with founders. Watching them learn the frameworks in detail and then systematically ignore them. They fall in love with their first idea and iterate the product instead of the customer. They treat polite interest as validation.
This is not an information problem. It is a behavioral one. Founders know what they should do. They don't do it.
My work is about that gap. I combine direct operational experience — co-founding a fintech startup, leading M&A and valuation work at PwC and BBVA — with the daily practice of working alongside founders in real time.
The financial and strategic logic of a startup changes completely at the moment of product-market fit. Applying post-PMF thinking to a pre-PMF company — or vice versa — is one of the most common and most expensive mistakes.
A pre-PMF company is not optimizing for revenue, growth, or profit. It is optimizing for learning speed — how fast it can identify a customer segment with real demand.
Burn in this phase is not waste. It is the cost of running experiments. The question is never "why aren't they growing?" It is "what are they learning, and how fast?"
If product-market fit is real, efficient growth follows organically — CAC compresses, retention improves, LTV expands.
A company that has to buy customers to show growth after claiming PMF has not found it. Paid growth that stops when spending stops is not a business. It is a simulation of one.
Traditional corporate finance was built for companies with revenue, margins, and history. The founders I work with are building companies that have none of those things — yet. The frameworks still matter. But when and how you apply them depends entirely on where the company actually is.
A path to profitability is not a narrative. It is a specific set of assumptions about CAC compression, margin expansion, and volume — each of which can be tested.
A sensitivity table is not risk analysis. Risk analysis requires understanding which assumptions drive the outcome and what the company would do if those assumptions break.
A model you cannot explain is not your model. An AI-generated output you cannot defend is not your work. The tools are powerful. The thinking is yours.
At Notre Dame's ESTEEM Graduate Program, I serve as Director of Academic Curriculum. Every year I work with 50+ aspiring entrepreneurs across three connected courses — each designed around a different phase of the founder journey.