In my last article, we discuss Tesla data as a flywheel example for Leo. Here we starting to lay the groundwork for this concept that can alter the path of any entity.
Digital platforms operate according to their own rules. It is much different than what we find in the traditional business world.
As we move into Web 3.0, it is crucial that token holders understand this concept. This is how we can accrue massive value. Since the platforms tend to have a different structure, at least in value capture, than Web 2.0, is it prudent to begin thinking in these terms.
After all, here is a core difference:
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Martec's Law Can Help Leo's Flywheel
Adopting an owner's mindset means radically altering the approach we take to the digital platform. The second one has a vested financial interest, the level of engagement should change.
This extends out to our approach to things, including the comprehension of what is taking place.
We are involved in technology. Digital platforms obviously are the epitome of this. They reside in the digital realm, introducing new principles along the way.
One thing that is beneficial to know is Martec's Law. This is one of those observations that is not a law, such as in physics, as much as something that held true over long periods of time.
Basically it says that technology operates on an exponential scale. Organizations, on the other hand, tend to operate in a logarithmic manner. Actually, there are many which proceed at a linear pace.
On a graphical basis, it looks like this:
We can see both the technological and organizational lines. The difference between the two (the white area) is where disruption takes place.
For this reason, any organization that is incorporating this understanding into its approach is going to have a leg up over its competition. There are some Web 2.0 platforms that operate in this manner. Amazon is one of those examples.
However, Web 3.0 is not run like Web 2.0. Here is where the stakeholders can make a massive difference.
Implementing enough components into the flywheel is where users-turned-owners can have an impact. Web 2.0 does this by taking what users do and having the company extract the value. They do it so well people are upset about the data they "give away".
Of course, they are not so upset as to stop what they are doing. They keep returning daily to provide the Web 2.0 platforms with more data.
Leo's Layering of the Flywheel Effects
The advantage that something like LEO has is that it can keep developing massive layers of flywheel effects. In the article linked above, we discussed data. This can, however, be broken down even further.
Each initiative within Leo can operate according to the same principles. This is how the flywheel can become extremely powerful.
Again, we have to remember what the concept is. The idea is to get things moving in a way that accelerates over time. As more is added, the impact keeps increasing, furthering the acceleration.
Overall, this means the greatest effort is required in the early stages. As the momentum gets going, incremental increases tend to have an outsized impact. Here is where exponentiality enters the picture.
It is also what moves an organization away from the logarithmic line (organizational) to the technological change one.
A basic premise here is the database. Let us look at an example:
YouTube has tens of millions of videos. Each day, more are uploaded. It also has a recommend algorithm that was honed over many years.
When someone watches a video, others are suggested. Depending upon the topic, this could result in recommendations that stretch back a number of years. It is why some videos, especially music, have millions of views. That did not happen immediately.
Over the course of time, as people watched similar videos, the recommend engine drove more traffic to other related videos.
The key is nothing new was required. Because of the library of videos, the engine just keeps feeding what is in there.
Of course, a flywheel effect is realized as more videos are added. This gives the recommend engine more data, which it feeds to the users. These recommendations are clicked upon, providing the feedback to the machine learning engine. This feedback enhances the recommend feature, offering up better suggestions.
Again, we see the circular nature. This is just one aspect of the Google ecosystem but it does show how powerful it is.
Leo can do the same thing. The process is identical even though the scope is much different.
Stakeholders have to focus upon building the database in a way that provides a similar flywheel effect. This can incorporate many facets, which will only leverages other aspects of the project.
As more do this, the shift toward exponential starts to take place. This is what Leo wants.