Evolutionary Organizations

when you prematurely optimize DAOs, you end up with a system that’s often anticipating the wrong problems and thus creating complicated solutions to address problems the DAO does not yet have.

evolutionary organizations don’t just optimize for learning – they also optimize for flexibility. Flexible systems allow organizations to leverage learnings and make incremental improvements (constantly testing assumptions!).

So you want to build an evolutionary organization, anon? Start simple. Stop trying to design the most sophisticated governance system. Start with something simple and evolve from there. Fail intentionally. Make assumptions. Test those assumptions. Repeat. Build modular. Think about your organization as a car. You can swap out parts and it’ll still ... See more

in these systems, failure isn’t just inevitable – it’s actually helpful. By constantly testing assumptions and maximizing learning, these systems become evolutionary organizations.

When you introduce more assumptions, it becomes increasingly difficult to pinpoint why a system is failing – is it because of assumption A or assumption B? The more assumptions you have, the harder it is to figure out which assumptions are wrong and which are right.

Ultimately, it’s the constant testing of those assumptions that allow systems to evolve into sophisticated, well-oiled machines.

The key to building complex systems is not designing complexity from the start. Instead, it’s about building simple systems, allowing them to fail, and iterating on those systems based on what works and what does not work.

Unlike monolithic DAO implementations, modular DAO implementations allow for quick testing and iterations, which ultimately allow organizations to evolve quickly and efficiently.

Being wrong often is the only way to be right – especially when you’re dealing with complex systems. This – in a nutshell – is the evolutionary organization thesis.