As the former PM for consumer trading at Coinbase and a long time points maximizer, I've spent hundreds of hours thinking about incentives. This is a short post introducing a heuristic framework to help us graduate toward more potent and durable incentive systems that will be helpful for consumer adoption.

The Problem: The current incentive systems for many applications and ecosystems in crypto, specifically governance token rewards, mining, and airdrops, are naive and will not extend to mainstream consumer adoption.

They are inefficient because they employ low power utilitarian incentive mechanisms and low levels of abstraction.

They may have worked in a bull cycle but a speculative environment masks the inefficiency (specifically by shifting everything up and to the right in incentive framework below).

If your need is for only thousands of users to adopt (for example, to operate an L1), this model may be perfectly sufficient.

But if your goal is to sustainably motivate millions or billions of users on web2 scale, you'll need to draw from a more comprehensive and potent psychological framework.

Luckily, businesses have been experimenting with how to tap into psychological incentives for hundreds, if not thousands of years. We can learn a lot from studying web2 loyalty programs which are among the most highly evolved at-scale incentive systems in the modern world.

Here is a quick framework. The basic model, which I’ve thought about for a while, is to distinguish incentive mechanisms from lower strength utilitarian incentives like utility and money to higher strength experiential and social incentives like curiosity, access, and status.

But it doesn’t end there. High value people tend to also play games at high levels of abstraction. So in thinking about how to replicate the best of the loyalty programs from web2, we should also consider abstraction as a semi-independent dynamic.

In the sole context of gambling at a casino, consider the difference between slot machines, table games, and poker tournaments.

• Slots = everything denominated in money. Most direct.

• Table games = chips used as direct proxies for money. Some abstraction.

• Poker tournaments = chips used as abstract proxies for money. High abstraction.

You can also use this model to consider the evolution of a particular trend. For example, Bored Apes.

• May’21: Relatively inexpensive and fun

• Sep’21: Status signal for “iykyk”

• May’22: The celebrities have them

• May’23: Still cool (in our opinions), but no longer carry “iykyk” abstract signaling

One of the reasons NFT communities have been so successful is because, unlike fungible tokens (with a few exceptions), they’ve managed to tap into the higher order experiential and social motivators, even if much of this was propelled by speculation in the last cycle.

However, these experiential and social motivators are real and durable, just as people continue to join private social and country clubs in the physical world.

As with all models, this one is incomplete, but hopefully offers insights into how to promote incentive designs that move beyond direct, utilitarian models in web3.

As we build in consumer, we need sustainable incentive systems in web3 that work outside of bull markets, and for these to work, they need to move upstream in the hierarchy of psychological motivators.

This also means we in web3 need to up our game to design products and experiences that are actually delightful and useful. Token games, while effective in driving behaviors, often drive the wrong behavior, and so should come much later in a project’s evolution: only after it has demonstrated real value.

I recently spoke with a former colleague who was previously an exec for one of the largest crypto projects that you’ve definitely heard of and probably used. He put it well: “The element that interests me the most is your pursuit of a consumer use case for crypto/blockchain that does not involve financial speculation.”

All of us in web3 will benefit from his advice.

As the former PM for consumer trading at Coinbase and a long time points maximizer, I've spent hundreds of hours thinking about incentives. This is a short post introducing a heuristic framework to help us graduate toward more potent and durable incentive systems that will be helpful for consumer adoption.

The Problem: The current incentive systems for many applications and ecosystems in crypto, specifically governance token rewards, mining, and airdrops, are naive and will not extend to mainstream consumer adoption.

They are inefficient because they employ low power utilitarian incentive mechanisms and low levels of abstraction.

They may have worked in a bull cycle but a speculative environment masks the inefficiency (specifically by shifting everything up and to the right in incentive framework below).

If your need is for only thousands of users to adopt (for example, to operate an L1), this model may be perfectly sufficient.

But if your goal is to sustainably motivate millions or billions of users on web2 scale, you'll need to draw from a more comprehensive and potent psychological framework.

Luckily, businesses have been experimenting with how to tap into psychological incentives for hundreds, if not thousands of years. We can learn a lot from studying web2 loyalty programs which are among the most highly evolved at-scale incentive systems in the modern world.

Here is a quick framework. The basic model, which I’ve thought about for a while, is to distinguish incentive mechanisms from lower strength utilitarian incentives like utility and money to higher strength experiential and social incentives like curiosity, access, and status.

But it doesn’t end there. High value people tend to also play games at high levels of abstraction. So in thinking about how to replicate the best of the loyalty programs from web2, we should also consider abstraction as a semi-independent dynamic.

In the sole context of gambling at a casino, consider the difference between slot machines, table games, and poker tournaments.

• Slots = everything denominated in money. Most direct.

• Table games = chips used as direct proxies for money. Some abstraction.

• Poker tournaments = chips used as abstract proxies for money. High abstraction.

You can also use this model to consider the evolution of a particular trend. For example, Bored Apes.

• May’21: Relatively inexpensive and fun

• Sep’21: Status signal for “iykyk”

• May’22: The celebrities have them

• May’23: Still cool (in our opinions), but no longer carry “iykyk” abstract signaling

One of the reasons NFT communities have been so successful is because, unlike fungible tokens (with a few exceptions), they’ve managed to tap into the higher order experiential and social motivators, even if much of this was propelled by speculation in the last cycle.

However, these experiential and social motivators are real and durable, just as people continue to join private social and country clubs in the physical world.

As with all models, this one is incomplete, but hopefully offers insights into how to promote incentive designs that move beyond direct, utilitarian models in web3.

As we build in consumer, we need sustainable incentive systems in web3 that work outside of bull markets, and for these to work, they need to move upstream in the hierarchy of psychological motivators.

This also means we in web3 need to up our game to design products and experiences that are actually delightful and useful. Token games, while effective in driving behaviors, often drive the wrong behavior, and so should come much later in a project’s evolution: only after it has demonstrated real value.

I recently spoke with a former colleague who was previously an exec for one of the largest crypto projects that you’ve definitely heard of and probably used. He put it well: “The element that interests me the most is your pursuit of a consumer use case for crypto/blockchain that does not involve financial speculation.”

All of us in web3 will benefit from his advice.