Estimating the number of real crypto users

As part of our 2024 State of Crypto report, our team has spent a lot of time trying to size up the crypto industry. With the industry maturing and more applications coming online, we wanted to understand how many people are actually using crypto. This is a nuanced question because the most obvious and easily quantifiable usage metric — active addresses — is so easily gamed. 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 the quality of a user — in fact, a whole field of growth analytics is devoted to this topic — but at its most basic, users can be aggregated into “daily active users” (DAUs), “monthly active users” (MAUs), and so on.

In crypto, things are trickier. This is because on blockchains, user identities are pseudonymous. It’s easy for one person to create and control a so-called sybil — a group of different identities, called “public addresses” — on a blockchain. (There are plenty of perfectly legitimate reasons for doing so, such as for privacy, security, or other purposes.) So it’s hard to know how many addresses a single person may use. (And vice versa, since multiple people can use a single address through multisigs, omnibus accounts, and various account abstraction protocols.) 

Until recently, the most popular blockchains had very limited capacity, which resulted in high transaction fees. This created a natural barrier to spinning up and using hundreds or thousands of addresses, since doing this would cost a lot of money. But recently, crypto infrastructure has gotten more scalable — via L2 rollups and new high-throughput L1s — which has reduced the cost of transactions on many of these blockchains to near zero.

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

The crypto industry has a long history of rewarding early users of a protocol with tokens. New protocols today often bootstrap their circulating supply of tokens with an “airdrop” — a rewards campaign that provides token incentives to a predefined set of addresses. Often these lists of addresses are derived retroactively from historical onchain transaction records. Some people may try to game the system by creating many different identities and using them to transact. In the industry, this tactic is commonly known as “airdrop farming.”

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

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

One approach we took is to filter out addresses that are suspected to be controlled by a bot or part of a sybil. Using onchain analytics and forensics, there are a variety of ways to do this that we explored:

1. Filtering out addresses that received their source of funds from a dispersion contract — a smart contract whose sole purpose is to take in funds and automatically distribute them across many different addresses. While there could be some false positives, the activity implies that the destination addresses all received funds from a single source and are therefore connected in some way.
2. Filtering out addresses that have near-zero balances at both the beginning and end of a given period. For example, if you’re looking for real monthly active users in September 2024 — you can try culling addresses that have near-zero balances on both September 1st and September 30th. This criteria implies that the addresses were transient in nature. While bots and sybils will likely seek to “clean up” balances after taking their actions, real human users will usually want to keep some balance in their wallet to cover future transaction fees.
3. Analyzing the distribution of addresses with one, two, three, four, five, or more transactions during the period. Addresses with just one or two transactions during the period are at best low quality users and at worst they are bots or sybils. This approach works best when aggregating over longer periods.
4. Filtering out addresses with many transactions over a very short period of time. Humans using a wallet or application interface can only reasonably process a certain number of transactions in a given period of time, whereas bots can transact at greater frequencies.
5. Optimistically including addresses that are tied to an identity protocol that requires some setup cost. For instance, addresses with ENS names, Farcaster IDs, and other linked social identities are likely to be real human users.

These are just some of the patterns onchain that may indicate bot-like behavior. By no means is this an exhaustive list, and we welcome suggestions that build on the above.

Another approach to estimating monthly active users is to look at offchain data sources. The most obvious place to start is with wallet users.

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

Assuming that we are looking to estimate transacting users, the next step would be to determine what percentage of Metamask’s users actually end up transacting. In 2019, Metamask reported that on a given day, about 30% of active users confirm an onchain transaction. (This is the latest available estimate.) If we apply this ratio to MAUs, we get about 9 million users transacting monthly via MetaMask wallet products.

Next we need to understand MetaMask’s total wallet market share across all blockchains. While this exact data is not readily available, we can make some educated guesses based on what we know. For example, we have a good estimate of MetaMask’s market share on the mobile wallet side based on data from the mobile analytics firm Sensor Tower. (Due to commercial service agreements, we are unable to disclose specific numbers here.)

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

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

Other considerations

It’s important to consider that some people use and transact with multiple addresses and wallets. This is unlikely to drastically inflate the numbers (since unlike bots and sybils, there is some upper limit on how many wallets a person can reasonably use), but it could be worth further de-duplicating based on some reasonable assumption.

On the flip side, there are also scenarios where a single address can be associated with multiple human users. Exchange omnibus accounts are one example. And by the way, this is all going to get even more complicated as account abstraction protocols and smart contract wallets proliferate. We left these considerations out of our analysis.

Based on our analysis using many of the approaches described above, we estimate there are 30–60 million real monthly crypto users today. This is a wide range, obviously, but it’s our best ballpark range based on the available data.

Note that this is just 14–27% of the 220 million monthly active addresses we measured in September. It’s also just 5–10% of the 617 million global crypto owners reported by Crypto.com in June. (Global crypto owners refer to people who own crypto, but do not necessarily transact onchain.) This differential suggests there is a huge opportunity to convert existing crypto owners — who are mostly passive holders — into active users. As major infrastructure improvements make brand new, compelling apps and consumer experiences possible, crypto holders who are lying dormant could become re-engaged onchain users.

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Measuring the number of active crypto users is difficult, but by using some of the methodologies detailed here, one can begin to arrive at a reasonable estimate. This post represents our attempt to share our thinking and the work that factored into our calculations. The approaches will continue to evolve over time, and as they do we’ll continue to share our latest thinking. If you’re interested in exploring this topic further or have ideas that might help refine these estimates, we would love to collaborate or hear from you: @DarenMatsuoka @eddylazzarin. 

See the a16z crypto 2024 State of Crypto report for more data and insights on the latest industry trends.

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