Designing the MVP-to-Full Path for Expert Research Tools

Founders building for researchers face a trap I've watched repeatedly: the MVP that wins a demo is the one that dies in real use. Expert users don't reward a thin, friendly slice — they reward depth. So when I help a founder scope the first version of a scientific tool, I argue against the conventional "smallest lovable product." The MVP for a power-user audience has to be narrow in surface but deep in one workflow. Pick a single high-stakes task — say, filtering and visualizing one class of experimental data — and make it genuinely better than the spreadsheet-and-scripts status quo it replaces. That earns trust. From there, the path to the full version is additive, not corrective. I plan the architecture so the data layer can already handle scale we haven't exposed yet, and I keep the interaction model consistent as we layer on queries, collaboration, and richer visualizations. The mistake is treating the MVP as disposable. With researchers, your first version sets the expectation of rigor; every later feature is judged against it. Build the foundation as if the experts are already watching, because they are.