The Confluence 50

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Good morning 👋

We started Confluence six years ago, and over that same time we have met with thousands of investors at different venture firms. If you have followed our writing for a while, you know we tend to gravitate towards emerging managers (funds I - III).

After talking and evaluating different GPs as a peer, as a friend of founders, and as an LP, a quality bar emerged over time.

The result is the Confluence 50 - our curated tracker of 50 emerging venture funds that we believe are quietly shaping the next generation of the asset class. These are the managers who are too new to be obvious and too good to ignore.

We spent years curating the list, months collecting data (AUM estimates, check sizes, follow-on rates, thematic focus, GP backgrounds, portfolio companies, and fundraising status), and weeks putting together this report. Much of it required digging well beyond what's publicly available.

Given the depth and exclusivity of this reporting, The Confluence 50 - filterable by GP type, stage, sector, geography, and fund size - is available to subscribers of our premium newsletter. If you work in tech, venture capital, or traditional investing, this product alone should more than justify a year’s subscription.

Before uncovering the full list, here are some things that stood out from collecting this research …

Key themes

68% of the Confluence 50 is led by a solo GP.

For most of venture capital's history, the solo GP model carried a quiet stigma. A single-person fund meant no institutional backing, no built-in succession, no one to pressure-test the thesis. The assumption was that serious managers partnered up.

That assumption (at least from our research) has quietly collapsed.

Solo managers in this cohort are on Fund III, Fund IV, actively deploying tens of millions of dollars, and generating liquidity. The infrastructure around them has matured: fund administrators, legal templates, capital allocators who have built dedicated emerging manager programs, and communities of GPs who treat each other as a collective rather than competition. Running a fund alone is no longer a liability.

The 32% with multiple GPs reflect something different.

The strongest partnerships in this cohort pair complementary judgment: a distribution builder alongside a technical picker, an operator alongside a career investor. The argument for a partner is not headcount. It is the specific kind of perspective your partner carries that you do not.

62% of GPs in the Confluence 50 maintain an active writing portfolio.

As we wrote in Narrative Capital and Audiences and AUM, good writing is a flywheel for good early-stage investing. Whether is used for thesis creation, audience expansion, idea distribution, or opportunity generation, content flywheels are being used in more and more creative ways to chase venture alpha.

The 38% who do not write compete on network and track record, both of which compound over time. Writing compresses that timeline. A GP who can articulate a clear thesis in public, consistently, has already demonstrated something that a pitch meeting alone cannot prove.

We believe that all investors can be grouped into the following camps:

Operators are the dominant GP archetype in the Confluence 50, appearing as a primary tag on more than half the funds in the tracker, often layered with other classifications.

This has been directionally true in venture for years. What this dataset clarifies is that the operator-investor model has now survived long enough to compound. The funds in this cohort led by operators are not all on Fund I, making an early bet that operating experience will translate. Many are on Fund II, Fund III, and Fund IV, which means they have already raised a follow-on vehicle, the statistically hardest single leap in emerging fund management.

The tracker also captures three other archetypes in meaningful numbers.

Support Investors, whose differentiation is community and founder services rather than brand or check size, represent 15 funds. Thematic Investors, with deep domain conviction in a specific sector or technology trend, represent 13. Career Investors, GPs who came up through venture or finance rather than through operating, represent 11.

23 of the 50 funds are headquartered in San Francisco (46%). That concentration reflects proximity to the deepest startup ecosystem in the world, dense co-investment networks, and decades of accumulated institutional knowledge.

The more interesting number is New York. 13 funds are headquartered there (26%), a share that would have been implausible a decade ago for a cohort of this caliber. New York's rise in early-stage venture extends well beyond fintech or consumer. The city's advantage is operator density across financial services, media, healthcare, and enterprise software: experienced builders who are cycling into fund formation and deploying into the markets they understand best.

Austin claims three funds, a direct product of the tech migration that accelerated during COVID and has not reversed. The remaining 14 funds span nine other cities, including Cambridge, Miami, Los Angeles, Boulder, Seattle, Columbia, and Chattanooga.

Vertical AI appears as a primary theme in 18 of the 50 funds, more than any other category by a wide margin.

This does not mean 18 funds are making the same bet. The more precise observation is that Vertical AI has become the frame through which many emerging managers are articulating differentiation. One fund applies it to legacy industrial operations. Another applies it to financial services compliance. A third applies it to clinical workflows in healthcare. The theme is consistent. The conviction about where it lands is not, and that variation is where the interesting positions live.

Devtools and Infrastructure ranks second with 11 funds as a primary theme. Every new application layer creates demand for new infrastructure, and the managers who have built or studied developer tooling most closely are positioning to catch that demand early. Generalist strategies claim 10 funds, a valid position for managers who believe stage discipline and founder judgment outweigh sector specificity. Fintech has five dedicated funds. Consumer, SaaS, and Robotics each have meaningful representation.

Beyond these categories, a smaller group of managers is staking claims in territory without consensus names yet: Autonomous Agents, Physical AI, Scientific AI, Applied Artificial Super Intelligence, Software 3.0, The Token Economy. These are theses that will look either prescient or premature, and probably will not look in between. The managers making them are betting that the next category is being defined right now.

The numbers

Curating the data in this list is a labor of love. Making sense of the data is the more interesting part of the assignment.

Some snippets of data from the Confluence 50 are visualized below.

Our process

1. We started with the data, not the names. Rather than beginning with funds we already knew and working backward to justify their inclusion, we built from Harmonic to track investor activity across the market. Investment counts, follow-on rates, AUM estimates, portfolio composition, most recent deployment activity: the data came first. Our opinions followed from what it showed.

2. We established inclusion criteria before we started selecting. The Confluence 50 focuses on emerging managers, which required actually defining what that means. We prioritized funds on Fund I through Fund III, with a strong bias toward managers actively deploying in the last 18 months. Funds with significant institutional AUM were reviewed individually rather than automatically included. The goal was managers who still have room to run.

3. We broadened first, then narrowed. Our initial research surfaced well over 100 candidate funds. We filtered on activity signals: recent deployment, a publicly identifiable GP, and a thesis we could articulate clearly in a single sentence. We were not looking for consensus picks. We were looking for the managers who will seem obvious in five years.

4. We studied each fund and each GP individually. Every fund in the Confluence 50 has a profile built from original research: GP background, investment thesis, portfolio highlights, fundraising status, and a full Harmonic data pull. We read pitch decks. We listened to podcasts. We read the newsletters and interviews GPs have published. In a number of cases, we talked directly with the managers themselves.

5. We made judgment calls. Not everything reduces to a spreadsheet. Some funds with small investment counts made this list because their portfolio company quality is exceptional relative to their age. Some with high counts did not make it because the activity looked scattered rather than intentional. One question guided every decision: would an LP or a founder be meaningfully better off knowing this manager exists? If yes, they made the cut.

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The Confluence 50