When we talk about tokenomics we usually start with ideas. People say that incentives help get everyone on the page keep the network safe and make it grow. We draw pictures that show how value and responsibility move around. If everything looks balanced and makes sense we think the plan is good. There is an idea that if something looks good on paper it will work well in life. Tokenomics is, about how these incentives and networks work together and tokenomics is what we are trying to understand. Tokenomics is a topic but we often think that if we can just get the basics right the rest of the tokenomics will fall into place.
I have found that when we make that assumption things start to get really complicated. That assumption is where the complexity starts to come in. It is, like that assumption is the beginning of all the things that happen with that assumption.
Systems that are used every day do not always work the way they were planned to. People who use these systems do not always react the way to things that are meant to motivate them. Some people try hard to get the best results others do not try at all and a few people can change the outcome in big ways that you do not expect. Things that are supposed to happen rarely start to happen a lot. Things that happen outside of the system like changes in the market or new rules interact with the system in ways that the people who designed it did not think about.
Tokenomics, which is a system that is controlled by feedback has to deal with delays, mistakes and unexpected problems. Systems, like this can be very hard to predict and control. Those effects do not show up away when we do simulations. The effects of the system come out slowly once the system has been running for a time and has built up a real history. The system effects take time to appear.
When we look at software and infrastructure systems that have been around for a time we learn something important. It is not about how Token-based systems work when everything is perfect. What is really important is how Token-based systems behave when things start to go. How do Token-based systems fail? How are Token-based systems maintained? How well do Token-based systems work when they are, under a lot of stress?
Some Token-based systems look great when everything is just right. They can become fragile when things do not go as planned. On the hand Token-based systems that are more simple often work better over time. This is not because they are super clever. Because it is easier to understand what is going on with Token-based systems when something goes wrong. The real test is not whether things that motivate people work when everything is going well. What happens when people start to focus on their own interests when people do things that help them right now or when something outside of the situation makes people do things they normally would not do.
The real test is about how these motivators work when people are trying to get an advantage over others or when there is a lot of pressure from outside to do something. The real test is about what happens to these incentives when things are not going smoothly like when people're only thinking about what is good for them or when something, from outside is forcing them to behave in a certain way that is not what we expected from these motivators.
Early design decisions in a system can have an impact later on. The choices people make about things like issuance and reward schedules and how the system is governed do not just affect one part of the system. These choices spread out. Affect the whole system. Some things might seem like details when the system is first set up but they become really important once they are part of the contracts and the tools people use and what people expect from the system. It is very hard to change these things on because it can cause a lot of problems.
When people try to make changes it can be very complicated. Can introduce new problems. Over time the system can get weighed down by problems that are not just technical, but also problems with the incentives that were set up early on. The system can get stuck with obligations that were created by promises and assumptions that were made a time ago. Early design decisions, like these can really add up. Cause a lot of trouble.
There is also a difference between systems that change over time and those that are made for a specific job. Neither way is always right. Systems that adapt to the world around them get to learn from what happens but they can also get complicated because of old decisions. Systems that are made for one thing can be more straightforward. They can also be too tied to ideas that may not work when things change. Adaptive systems, like these can be really good.
They can also be bad. Purpose-built systems are the kind and they can be good too but they have their own problems. Adaptive systems and purpose-built systems are both. They both have good and bad points. In tokenomics you see a difference in how incentives are handled. Sometimes people keep making changes to incentives as they see how people behave. Times incentives are a fundamental part of the system from the very beginning. This difference is important in tokenomics because it affects how the system works. Tokenomics is, about understanding how incentives work in a system and this difference shows up in the way incentives are used in tokenomics.
What really matters are the tradeoffs, not the features of something. If people get incentives to participate quickly it can actually make things in the long run. When you closely tie activity to rewards it can make things happen faster. It can also make things more unpredictable. Rules that seem unnecessary, at first might actually help keep things stable when things get tough. The thing is, you do not usually see these kinds of effects when you first launch something or when you are trying to market it.. Over time these effects are what really determine what happens to the tradeoffs and the features.
When we look at design paths we can see that they are based on different ideas from the start. If we want a system to grow fast we will make different choices about what motivates people to use it compared to a system that is designed to work in a predictable way. A system that is set up to have a lot of control will be able to handle more risks, than a system that is designed to have very little outside control and is slow and expensive to change. Each design path has its downsides. As things get more complicated it gets harder to make changes. The limitations of a system become a part of how it works. We cannot avoid these problems they are a natural result of choosing a particular way of doing things with our system.
What I have seen times is that markets and the stories people tell about them are slow to catch up with what engineers are actually doing. People tend to focus on things that are growing fast, new and easy to understand. The harder work of making sure things do not fall apart dealing with problems that can cause issues and keeping everything working together is often overlooked at first. Usually by the time people start talking about these problems the system has already decided where it is going.
When you think about tokenomics this way it is not really about creating incentives. It is more, about making sure the system works with the real world in the long term. The big question is, can the tokenomics system still make sense when things change? Can it handle problems without needing to be fixed all the time?
Can tokenomics. Grow without losing the things that make it work in the first place? Tokenomics needs to be able to adapt to things without falling apart.Ultimately, long-term viability turns on a single question: when the original assumptions inevitably break down, does the system still behave in a way that people can reason about, maintain and sustain over time?