However, precisely because of these resource and knowledge constraints, it’s equally vital at this stage to develop solutions that are inherently flexible. The irony of the greedy approach is that while it prioritizes immediate returns, success depends on consciously avoiding rigid designs that would prematurely lock in assumptions about the problem space. By intentionally structuring early solutions for extensibility, startups create pathways for future growth and pivots, efficiently leveraging scarce resources while continuously deepening their understanding of customer needs. While decisions made early on won’t guarantee graceful scaling—especially as experimentation and complexity inevitably continue—thoughtful flexibility at the outset can greatly support the transition into later stages.
I think there are many dimensions for startups (funding, team size, micro and macroeconomics, etc) and I want to see how these fit into a a high-dimensionality model. My background is mostly in computer science, so I will need to do a bit more research in a few other domains (economics, business management) to understand what makes startups work and fail and draw on existing research.
Eventually I am hoping this model can explain what makes some startups work, and others fail in a very broad sense.