a16z: How many real crypto users are there at present?

Author: Maren Matsuoka, Eddy Lazzarin, a16z Crypto; Compiler: 0xxz@黄金财经

As part of the a16z 2024 Crypto Industry Status Report, our team spent a lot of time trying to assess the crypto industry. As the industry matures and more applications come online, we want to understand how many users are actually using cryptocurrencies.

This is a tricky question because the most obvious and easily quantifiable usage metric - active addresses - can be easily manipulated. So below we share our thoughts.

In traditional software, the concept of a "user" is well understood. Of course, there are many ways to measure user quality - in fact, there is an entire field of growth analytics dedicated to this - but at the most basic level, users can be aggregated into "daily active users" (DAU), "monthly active users" (MAU), etc.

In the crypto space, things are trickier. This is because on the blockchain, user identities are anonymous. It’s easy for a single person to create and control what’s called a sybil — a collection of different identities called “public addresses” — on a blockchain. (There are many perfectly legitimate reasons to do this, such as for privacy, security, or other purposes.) It’s therefore hard to know how many addresses a single person can use. (And vice versa, since multiple people can use a single address through multisig, omnibus accounts, and various account abstraction protocols.) Until recently, the most popular blockchains had very limited capacity, which led to high transaction fees. This created a natural barrier to starting and using hundreds or thousands of addresses, because it would have cost a lot of money to do so. But recently, crypto infrastructure has become more scalable — through L2 rollups and new high-throughput L1s — which has driven transaction costs down to near zero on many blockchains.

But isn’t the cost of creating multiple identities also near zero for traditional internet applications? In most cases, this is true. For example, it’s very easy for a single person to create and use multiple email addresses. But the key difference is that in crypto, there are strong incentives for this behavior.

The crypto industry has long rewarded early adopters of protocols with tokens. Today, new protocols often launch their circulating token supply with an “airdrop” — a bounty event that provides token incentives to a predefined set of addresses. These lists of addresses are often derived retroactively from historical on-chain transaction records. Some may try to game the system by creating many different identities and using them to transact. In the industry, this strategy is often referred to as “airdropping.”

Given these behaviors, it’s clear that the 220 million unique monthly active addresses we measured in September 2024 do not directly translate to 220 million individuals or users. (Note that an address active on multiple EVM chains only contributes once to the 220 million total.)

So how many active users are there? 10 million? 50 million? 100 million? That’s the question we set out to answer. Here’s more on our approach.

Approach 1: Filtering Active Addresses

One approach we took was to filter out addresses that were suspected of being controlled by bots or sybils. Using on-chain analysis and forensics, we explored multiple ways to do this:

1. Filter out addresses that received funds from a dispersion contract source - a dispersion contract is a smart contract whose sole purpose is to receive funds and automatically distribute them to many different addresses. While there may be some false positives, this activity means that the target addresses all received funds from a single source and are therefore related to each other in some way.

2. Filter out addresses that have a balance close to zero at the beginning and end of a given time period. For example, if you are looking for real monthly active users in September 2024 - you can try to eliminate addresses that have a balance close to zero on both September 1st and September 30th. This criterion means that these addresses are temporary in nature. While bots and Sybils may seek to "clean up" balances after an action, real human users often want to keep some balance in their wallets to pay for future transaction fees.

3. The distribution of addresses that had one, two, three, four, five, or more transactions during the analysis period. Addresses that have only one or two transactions in a period are at best low-quality users and at worst bots or Sybils. This approach works best when aggregated over longer periods of time.

4. Filter out addresses that have made a large number of transactions in a very short period of time. A human using a wallet or API can only reasonably handle a certain number of transactions in a given period of time, while bots can make transactions at a much higher frequency.

5. Optimistically include addresses tied to identity protocols that require some setup cost. For example, addresses with ENS names, Farcaster IDs, and other linked social identities are likely real human users.

These are just some of the patterns on the chain that may indicate bot-like behavior. This is by no means an exhaustive list, and we welcome your suggestions based on the above.

Method 2: Inferring from Wallet Users

Another way to estimate monthly active users is to look at off-chain data sources. The most obvious starting point is wallet users.

In February 2024, the popular crypto wallet MetaMask reported 30 million monthly active users. They define a monthly active user as “someone who loads a page in the MetaMask extension or opens the mobile app at least once in any consecutive 30-day period.”

Assuming we want to estimate the number of transacting users, the next step is to determine what percentage of Metamask’s users actually transact. In 2019, Metamask reported that on a given day, about 30% of active users confirmed an on-chain transaction. (This is the latest available estimate.) If we apply this ratio to MAU, we find that about 9 million users transact through the MetaMask wallet product each month.

Next, we need to understand MetaMask’s total wallet market share across all blockchains. While these exact numbers aren’t easy to come by, we can make some educated guesses based on what we do know. For example, we have a good estimate of MetaMask’s market share for mobile wallets based on data from mobile analytics firm Sensor Tower. (We can’t reveal the exact numbers here due to commercial service agreements.)

Once we have MetaMask’s estimated market share, we can simply extrapolate an estimate of total cryptocurrency users based on the 9 million monthly active transacting users figure we derived earlier. We can then compare that to the results from Method 1 to see if it’s at least in the same ballpark.

We can further refine our estimates by analyzing data from other wallets and infrastructure providers who are willing to share their proprietary metrics with us, and then cross-check that with the numbers derived above.

Other Considerations

It’s important to consider that some people use multiple addresses and wallets to transact. This is unlikely to significantly increase the numbers (since, unlike bots and sybils, there is a certain upper limit to the number of wallets a person can reasonably use), but it may be worthwhile to further deduce based on some reasonable assumptions.

On the other hand, there are also cases where a single address can be associated with multiple human users. Omnibus accounts on exchanges are one example. This is all going to get more complicated, by the way, with the proliferation of account abstraction protocols and smart contract wallets. We didn’t account for these in our analysis.

Final Estimate: 30-60M Actual Monthly Transacting Users

Based on our analysis using the multiple methods described above, we estimate that there are currently 30-60M actual monthly crypto users. Obviously, this is a large range, but it’s the best range we can estimate based on the data we have.

Note that this only accounts for 14-27% of the 220M monthly active addresses we measured in September. This is also only 5-10% of the 617M global crypto holders reported by Crypto.com in June. (Global crypto holders are people who own crypto but don’t necessarily transact on-chain.) This discrepancy suggests that there is a huge opportunity to convert existing (mostly passive) crypto holders into active users. As major improvements in infrastructure enable new and compelling applications and consumer experiences, dormant cryptocurrency holders may re-emerge as on-chain users.

Measuring the number of active crypto users is difficult, but by using some of the methods detailed in this article, one can begin to arrive at reasonable estimates.