Author: Joel John and Shlok Khemani; Compiled by: Felix, PANews

The past week has been camping on a hilltop in Kerala, southern India. It's a weird refuge for avoiding Trump's tariff strategy, as Kerala has always been good at making connections with the world. Thousands of years ago, the state exported peppers around the world. According to William Darymple (historian), a peppercorn from here was found in the remains of the mummy of Egyptian pharaoh thousands of years ago. But what does this have to do with today's article?

It emphasizes how the network works in a trade context. A thousand years ago, the ports here promoted the transport of gold and spices. In turn, they also become nodes of capital flow. Similar stories have been staged in London, New York, Kolkata, Hong Kong and many other trade centers. Just some do better than others, integrating themselves into the structure of global trade.

Similar situations have also occurred in the crypto venture capital sector. As an asset class, venture capital follows an extreme power law. But as it has been catching up with the latest narrative, the extent to which this happens is not studied. An internal tool has been created over the past few weeks to track the network of all crypto venture capital firms.

As the founder, knowing which venture capital investors often invest together can save time and improve their financing strategies. Every transaction is a fingerprint. Once these stories are visualized, the stories behind them can be unraveled.

In other words, the nodes responsible for most of the financing in the crypto field can be tracked and attempted to find "ports" in modern trade networks, which is no different from those of merchants a thousand years ago.

This will be an interesting experiment for two reasons:

Currently operating a venture capital network that is somewhat like a "fighting club". The venture capital network covers approximately 80 funds. And over the past year, about 240 funds have invested more than $500,000 in seed rounds. This means the network maintains direct contact with one-third of these funds.

But it is difficult to track the actual deployment of funds. Sending the latest information about the founder to each fund will only cause interference. The tracking tool is a filtering tool that lets you know which funds have invested, in which areas, and with whom.

For founders, understanding fund allocation is only the first step, and what is more valuable is understanding how these funds perform and who they usually invest with. To understand this, the historical probability of fund investment obtaining subsequent investments is calculated, but this probability becomes vague in later stages (such as Series B financing), as companies usually issue tokens instead of traditional equity financing.

The first step is to help founders identify which investors are active in the crypto venture capital space. The next step is to understand which sources of funding actually perform better. Once you have a handWith these data in mind, you can explore which funds' co-investment can bring better results. Of course, this is not a profound knowledge. No one can guarantee a Series A success just because someone wrote a check. Just like no one can guarantee that they can get married after their first date. But understanding what is coming, whether it’s dating or venture capital, is undoubtedly of great benefit.

Some basic logic can be used to identify the funds with the most subsequent financing rounds in their portfolio. If multiple companies invested in by a fund receive financing after the seed round, it is likely that the fund has done something right in some respect. When a company raises funds at a higher valuation next round, the value of venture capital in its investment increases. Therefore, subsequent financing rounds can be used as an important indicator to measure performance.

This article selects the 20 funds that received the most subsequent financings in its portfolio, and then calculates the total number of companies they invest in during the seed round. Based on this number, the probability of the founder obtaining subsequent financing can be calculated. If a fund invests in 100 companies in the seed round, 30 of which receive subsequent financing within two years, the calculated probability of “graduation” is 30%.

It should be noted that the filtering conditions set here are two years deadlines. Many times, startups may choose not to raise funds, or to raise funds after two years.

Even among the top 20 funds, the Power Law is extremely extreme. For example, getting financing from A16z means you have a 1/3 chance of financing again within two years. That said, one of every three startups supported by A16z can receive Series A financing. Considering that the probability on the other end is only 1/16, this is a pretty high graduation rate.

The closest 20 venture funds (on this list of top 20 funds that include subsequent investments) have a 7% chance to see a company continue to raise funds. These numbers look similar, but for the sake of ease of understanding, the probability of 1/3 is equivalent to the probability of rolling a dice with a number less than 3, and the probability of 1/14 is roughly equivalent to the probability of having twins. These results are very different literally and probabilistically.

Just kidding, this shows the degree of aggregation within crypto venture capital funds. Some venture capital funds can arrange follow-up financing for their portfolio companies because they also have growth funds. So they invest in seed and A-round in the same company. When a venture capital fund doubles its stake in the same company, it usually sends a positive signal to investors who participate in the round later. In other words, the existence of growth-stage funds within venture capital companies will greatly affect the chances of success in the next few years.

In the long run, venture capital funds in the crypto space will gradually evolve into private equity investments in projects that already have considerable income.

We have a theoretical basis for this transformation. But what exactly does the data reveal? To study this, the number of startups with subsequent financing in the investor community was examined. Then, the proportion of companies that the same venture fund participated in subsequent financing was calculated again.

That is, if a company receives seed rounds from A16z, how likely is it to invest in A16z in Series A?

A obvious pattern can be seen soon. Large funds with more than $1 billion in managed funds tend to participate frequently in subsequent financing. For example, in the A16z portfolio, 44% of companies receive follow-up investments from A16z. Blockchain Capital, DCG and Polychain followed up on one-quarter of their investments in refinancing projects.

In other words, in the seed round or pre-seed stage, it is much more important than expected to raise funds from whom, as these investors tend to support their projects again.

These models are the results of the subsequent summary. It is not an indication that companies that raise funds from non-top venture capital are doomed to fail. The goal of all economic activities is to grow or generate profits. A company that can achieve any of these goals will have a valuation that will increase over time. But this does help improve your chances of success. If you can’t raise funds from this group (the top 20 venture capitalists), one way to increase your chances of success is to leverage their network. Or, establish connections with these capital hubs.

The following image shows the network of all crypto venture capitalists over the past decade. There are 1,000 investors, with a total of about 22,000 connections. If one invests with another investor, a connection will be formed. This may seem crowded and may even make people feel like there are too many choices.

However, it covers funds that have gone bankrupt, never returned funds or no longer invest.

The following figure shows the future trend of the market more clearly. If you are a founder seeking a Series A funding fund, there are about 50 fund pools with investments of more than $2 million. There are about 112 funds in the network of investors participating in this type of round. Moreover, these funds are becoming increasingly concentrated and tending to invest with specific partners.

From the seed turn to round A, the investor group from which they can raise funds

Over time, the funds seem to develop a habit of co-investment. That is, funds investing in one entity often introduce another peer fund, either because of their complementary skills (such as technology, or helping with marketing), or based on partnerships. To study how these relationships work,This article explores the co-investment model among funds in the past year.

For example, in the past year:

Polychain and Nomad Capital have nine joint investments.

Bankless has 9 joint investments with Robot Ventures.

Binance and Polychain have 7 joint investments.

Binance has the same many co-investment projects as HackVC.

Again, OKX and Animoca have 7 joint investments.

The requirements for their co-investors are becoming increasingly stringent.

For example, of the 10 investments invested in Paradigm last year, Robot Ventures was involved in three of the rounds.

DragonFly participated in three rounds of investments with Robot Ventures and Founders Fund, with the three institutions making a total of 13 investments.

Similarly, Founders Fund and Dragonfly invested three times, accounting for one-third of the 9 projects invested in.

In other words, we are transforming to an era where few funds make large investments and fewer co-investors. These joint investors are often well-known institutions that have been established for a long time.

Another way to study these data is to analyze the behavior of the most active investors. The matrix above considers the funds that have invested the most since 2020 and their relationship. You will notice that accelerators (such as Y Combinator or Outlier Venture) rarely invest in joint investments with exchanges (such as Coinbase Ventures).

On the other hand, exchanges usually have their own preferences. For example, OKX Ventures has a high proportion of joint investments with Animoca Brands. Coinbase Ventures made over 30 investments with Polychain and 24 investments with Pantera.

What you can see are three structural problems:

Although investment frequency is high, accelerators tend to rarely invest in joint investments with exchanges or large funds. This may be due to stage preference. Accelerators tend to invest in the early stages, while large funds and exchanges tend to invest in the growth stages.

Large exchanges often have a strong preference for growth-stage risk funds. Currently, Pantera and Polychain dominate.

Exchanges tend to collaborate with local participantsdo. OKX Ventures and Coinbase both show different preferences when choosing co-investment objects. This precisely highlights the global characteristics of today's Web3 capital allocation.

So, if risk funds are gathering, where will the next marginal capital come from? An interesting model can be noticed: corporate capital also has its own clusters. For example, Goldman Sachs has invested in two rounds in its development history with PayPal Ventures and Kraken. Coinbase Ventures has made 37 co-invested with Polychain, 32 with Pantera and 24 with Electric Capital.

Unlike venture capital, corporate capital pools are usually targeted at growth-term companies with considerable PMF. Therefore, the performance of this fund pool remains to be seen at a time when early risk financing declines.

A few years ago, after reading Neil Ferguson's "Platforms and Towers", he began to study the relationship network in the field of encryption. This book reveals how the spread of ideas, products and even diseases is associated with the Internet. It wasn't until a few weeks ago that it was realised that visualizing the connection network between sources of funding in the crypto space was possible.

The nature of such data sets and economic interactions between these entities can be used to design (and execute) mergers and token acquisitions between private entities. Both of these tasks are explored internally. They can also be used in business development and cooperation programs. We are still looking at how to enable specific companies to access these datasets.

Back to the topic just now.

Can the relationship network really help the fund achieve better performance?

The answer is a bit complicated.

The fund's ability to choose the right team and provide sufficient funds will be more important than a partnership with other funds. What really matters, however, is the personal relationship between the general partner (GP) and other co-investors. Investors will not share transactions with company logos, but with people. When partners change funds, this connection only transfers to their new funds.

I have a premonition about this, but there is a lack of means of verification. Fortunately, there was a paper in 2024 that looked at the performance of the top 100 venture capital firms over time. In fact, they looked at 38,000 rounds of financing from 11,084 companies and even segmented them into seasonality in the market. The core of their argument can be boiled down to several facts.

Previous co-investment does not represent future cooperation. If previous investment fails, the fund may choose not to work with other funds. For example, think of a network that broke down when FTX went out.

In times of fanaticism, co-investment tends to increase as funds want to deploy funds more actively. In the fanatical period, venture capitalists rely more on social signals than due diligence. During bear markets, the foundation deploys cautiously due to low valuations and usually acts alone.

The Foundation selects peers based on complementary skills. Therefore, if investors are focused on the same field in a round of financing, it usually leads to problems.

As mentioned before, ultimately, co-investment does not happen at the fund level, but at the partner level. In my personal career, I have seen some people jumping jobs between different institutions. Their goal is often to work with the same person, regardless of which fund they join. In an era when artificial intelligence is gradually replacing human work, it is helpful to understand human relationships as the basis for early venture capital.

In the study on how crypto venture capital networks are formed, there is still much work to be done. For example, it would be interesting to study the preferences of liquid hedge funds in terms of capital allocation, or how late deployments evolve with the market seasonality. Or how mergers and acquisitions and private equity are integrated into it. The answer may be hidden in the existing data, but these will take time.

Like many other things in life, this will be a continuous process of exploration, and once something is discovered, the relevant information will be conveyed in a timely manner.