The crypto ecosystem has been talking about DAOs since 2014. DAOs are even highlighted in the Ethereum whitepaper. But how will we know when they are ready for prime time?

I think the most important dynamic to look at is how efficient DAOs are at accomplishing their goals in comparison to traditional organizations (TradOrgs). Secondarily, I would look at the Learning Rate for each of these types of organizations.

There are three properties of DAOs that stand out to me as I reason about their Learning Rate:

1. Experimentation Rate: Following lots of activity in DeFi and NFTs, there’s a lot of new capital in crypto that people are excited to put to work. DAO experimentation is attracting that capital. Simultaneously, the tooling and infrastructure to launch DAOs (as mentioned in the piece from The Defiant above) is becoming increasingly robust, making DAOs less expensive to launch.

2. Propagation of Successful Experiments via Forking: The ecosystem of crypto protocols is largely open-source, both technologically and philosophically. Across currencies and DeFi, we have already seen forks of massive projects (e.g. Bitcoin → Bitcoin Cash; Uniswap → Sushiswap). These forks have been able to take what works, make opinionated changes, then quickly attract capital and usership. The same dynamic will play out in DAOs for governance and compensation structures. This means successful DAO experiments will spread across the ecosystem quickly, and fewer projects have to fail in order to achieve a high Learning Rate.

3. DAO Ecosystem Network Effects: Much like a Web2 marketplace or platform, DAOs have internal network effects. But unlike Web2, interoperability is a core philosophy of many DAOs. For example, there are a number of DAOs that allow you to gain membership either as an individual or as another DAO. Today, DAOs are holding each other’s tokens, working on each other’s infrastructure, and partnering to build projects. This leads me to believe that the network effects of the DAO ecosystem will be even more robust than what we’ve seen in Web2.

These three factors add up to an exponential Learning Rate for DAOs. The DAO Ecosystem is a network of networks that is highly capable of propagating and utilizing the latest knowledge created anywhere in the network.

In contrast, the Learning Rate for TradOrgs seems to be linear growth at best. Sure, companies today learn quickly about how to operate their particular business. But there isn’t rapid iteration in these TradOrgs in organizational design, decision-making, or compensation. The Learning Rate for TradOrgs is more like the pace of organizations undergoing Agile Transformations (years), or slowly cycling in and out of the S&P500 (decades).

The organizational efficiency of linear growth TradOrgs will be overcome (quickly) by exponential growth DAOs. I call the moment when this happens “Efficiency Parity.”

This is why it’s interesting to dig in and learn about DAOs now — we are still pre-Efficiency Parity.

Before Efficiency Parity, there is an opportunity cost associated with choosing to operate as a DAO instead of a TradOrg. As such, the people starting DAOs now are either: 1) philosophically aligned with DAOs, 2) betting on the future of DAOs, or 3) enabled by DAOs to do something they otherwise couldn’t.

However, as we approach Efficiency Parity, it will make more sense for more organizations to be DAOs. And my guess is that soon after, it will make very little sense to be anything but a DAO, for most organizations.

It’s hard to say how long this will take, but it seems to me like we’re on the precipice of a Cambrian explosion of organizational experiments. And I’m very excited for what’s to come.

The crypto ecosystem has been talking about DAOs since 2014. DAOs are even highlighted in the Ethereum whitepaper. But how will we know when they are ready for prime time?

I think the most important dynamic to look at is how efficient DAOs are at accomplishing their goals in comparison to traditional organizations (TradOrgs). Secondarily, I would look at the Learning Rate for each of these types of organizations.

There are three properties of DAOs that stand out to me as I reason about their Learning Rate:

1. Experimentation Rate: Following lots of activity in DeFi and NFTs, there’s a lot of new capital in crypto that people are excited to put to work. DAO experimentation is attracting that capital. Simultaneously, the tooling and infrastructure to launch DAOs (as mentioned in the piece from The Defiant above) is becoming increasingly robust, making DAOs less expensive to launch.

2. Propagation of Successful Experiments via Forking: The ecosystem of crypto protocols is largely open-source, both technologically and philosophically. Across currencies and DeFi, we have already seen forks of massive projects (e.g. Bitcoin → Bitcoin Cash; Uniswap → Sushiswap). These forks have been able to take what works, make opinionated changes, then quickly attract capital and usership. The same dynamic will play out in DAOs for governance and compensation structures. This means successful DAO experiments will spread across the ecosystem quickly, and fewer projects have to fail in order to achieve a high Learning Rate.

3. DAO Ecosystem Network Effects: Much like a Web2 marketplace or platform, DAOs have internal network effects. But unlike Web2, interoperability is a core philosophy of many DAOs. For example, there are a number of DAOs that allow you to gain membership either as an individual or as another DAO. Today, DAOs are holding each other’s tokens, working on each other’s infrastructure, and partnering to build projects. This leads me to believe that the network effects of the DAO ecosystem will be even more robust than what we’ve seen in Web2.

These three factors add up to an exponential Learning Rate for DAOs. The DAO Ecosystem is a network of networks that is highly capable of propagating and utilizing the latest knowledge created anywhere in the network.

In contrast, the Learning Rate for TradOrgs seems to be linear growth at best. Sure, companies today learn quickly about how to operate their particular business. But there isn’t rapid iteration in these TradOrgs in organizational design, decision-making, or compensation. The Learning Rate for TradOrgs is more like the pace of organizations undergoing Agile Transformations (years), or slowly cycling in and out of the S&P500 (decades).

The organizational efficiency of linear growth TradOrgs will be overcome (quickly) by exponential growth DAOs. I call the moment when this happens “Efficiency Parity.”

This is why it’s interesting to dig in and learn about DAOs now — we are still pre-Efficiency Parity.

Before Efficiency Parity, there is an opportunity cost associated with choosing to operate as a DAO instead of a TradOrg. As such, the people starting DAOs now are either: 1) philosophically aligned with DAOs, 2) betting on the future of DAOs, or 3) enabled by DAOs to do something they otherwise couldn’t.

However, as we approach Efficiency Parity, it will make more sense for more organizations to be DAOs. And my guess is that soon after, it will make very little sense to be anything but a DAO, for most organizations.

It’s hard to say how long this will take, but it seems to me like we’re on the precipice of a Cambrian explosion of organizational experiments. And I’m very excited for what’s to come.