What Kind of VCs Can Get Money from Fund of Funds? We Have the Answer After Reviewing 2000

marsbitОпубліковано о 2026-04-11Востаннє оновлено о 2026-04-11

Анотація

Moses Capital, a fund of funds focused on early-stage VCs, reviewed over 2,000 funds over two years and invested in 46, a 2.3% selection rate. The market is larger than perceived, with many new funds remaining invisible to most LPs. They identified four GP archetypes: founder-turned-investor, spin-out from established VC firms, community-native managers, and quiet technical experts. The top reasons for rejecting 97% of funds included team inexperience (30%), poor portfolio construction (25%), weak track record (20%), strategy misalignment (15%), and fundraising challenges (10%). The highest-quality deal flow source emerged unexpectedly from blind founder reference calls during due diligence, where consistently praised investors became top targets. Building a reputation through prepared, respectful engagement led to strong GP referrals and trust within the VC ecosystem.

Author: Moses Capital & Lev Leviev

Compiled by: Deep Tide TechFlow

Introduction: Moses Capital is a Fund of Funds (FoF) focused on early-stage VCs. Over the past two years, they have reviewed more than 2,000 funds and ultimately invested in only 46, with an approval rate of 2.3%. This article reviews the four archetypes of GPs they discovered during the screening process, the specific reasons for the 97% rejection rate, and an unexpected due diligence method that became the highest-quality source of deal flow. For readers interested in the VC ecosystem and the LP perspective, the information density is very high.

When I founded Moses Capital, I thought I had a general understanding of the market for emerging fund managers. A few hundred funds, concentrated in a few common cities, and all you needed to know was where to look.

That assumption lasted about three months.

Over the past two years, we have reviewed more than 2,000 funds for Fund I. We conducted 553 preliminary calls, completed 276 full due diligence processes, and ultimately added 46 funds to our portfolio—an approval rate of 2.3%. When you sit through that many conversations, patterns naturally emerge.

Here’s what we learned.

This Market Is Larger Than Anyone Thought

Before we built a systematic sourcing process, our deal flow was like most FoFs: relying on networks and inbound referrals. VCs refer other VCs. This approach works, but it also means your perspective is limited by "who knows you."

When we started scraping SEC filing data in real-time, the picture changed completely. Dozens of new funds are launched every week, many of which don’t appear on anyone’s radar until months later—by which time they are already fundraising. By 2025, we covered about 95% of U.S. VC funds. The sheer number of new funds surprised even us.

The key point: most of these funds are invisible to the majority of LPs. Not because they are bad, but because they are too early-stage, too small, and haven’t built the network that gets you on shortlists. This is precisely the gap we aim to fill.

Four Archetypes of GPs

After 553 preliminary calls, patterns began to emerge. We broadly categorized the managers we met into four types:

  1. Entrepreneurs Turned Investors

Former founders or former operating executives, usually with one notable exit, who then decide to start a fund. They have credibility among founders and strong deal flow in their niche. The challenge is that managing a fund and managing a company are two completely different things—portfolio construction, follow-on investment strategies, post-investment management—many learn on the job. Some pick it up quickly, but more only truly get it by Fund II or Fund III.

  1. VC Spin-Outs

Former partners or principals from established funds (tier-one or tier-two) who go out on their own. They have brand recognition, track records to show, and usually strong networks. What we primarily look at: how much of that track record is theirs, and how much is the platform’s? After leaving a large fund, do they still remain competitive among founders?

  1. Community-Native Managers

A type that has clearly increased since 2020—managers who have built their reputation through community building, writing articles, hosting podcasts, and managing social media. They have inbound deal flow, visibility, and usually a real community moat.

Within this category, there are actually two subtypes: one is investors who built a community first, using it to drive deal flow and create network value for portfolio companies; the other is community operators who started investing because deal flow naturally came to them. The distinction between these two is important. For both, we look at two things—the quality of their investment discipline, and whether the community creates real value for the founders they want to back.

  1. Quiet Technical Experts

This is usually my personal favorite type. The GP has deep technical or industry expertise in a specific field, honed over many years. They are the people founders turn to for advice when facing problems, and over time, more and more founders want them on their cap table early—not for the brand, but to help build the business from day one.

These individuals deliberately stay low-profile, building their reputation on expertise and accumulated relationships. They almost never reach out to us proactively. We find them through systematic external searches or, more commonly, through founder references during due diligence on other funds. We ask every founder: among your investors, who provided the most help? The answer is often this type of person.

What the 97% Rejection Looks Like

We rejected over 97% of the funds we reviewed. Each pass decision was made as carefully as an investment decision, and this process was refined with every fund we examined.

  • About 30% of rejections were related to the GP or team. Insufficient fund management experience, lack of clear differentiation from existing players, or networks that don’t translate into unique deal sourcing capabilities.
  • About 25% failed on portfolio construction. Too much exposure to later stages, lack of discipline in follow-on strategies, insufficient target ownership, or over-diversification—mathematically killing the possibility of power law returns. If a fund isn’t designed to generate concentrated big winners, it probably won’t.
  • About 20% were due to track record issues. Investment history too weak or insufficient, or a track record that doesn’t match the current strategy (different geographies, sectors, stages, check sizes).
  • About 15% were due to strategy mismatch. The fund’s current strategy doesn’t align with our investment themes, unrelated to performance—fund size too large, investment scope too broad, or involvement in areas or regions we deliberately avoid.
  • The remaining 10% were due to factors like fundraising dynamics. If a manager can’t raise money, they can’t execute their strategy.

The Best Sourcing Channel We Never Planned

Our sourcing evolved in stages. Initially, it relied on networks and inbound referrals. Then we built a systematic outbound engine that scrapes every new U.S. fund in real-time, automatically filtering by size, strategy, and GP background. At its peak, this channel accounted for 70% of our meetings. We could reach managers before most LPs even knew the fund existed.

But the sourcing channel that ultimately proved most valuable wasn’t one we designed. It came from our due diligence process itself.

For every GP, we conduct blind founder reference calls—sometimes up to 10 if the track record allows. In these calls, we don’t just ask about the manager we’re evaluating. We go through the cap table, asking founders for honest feedback on their other early investors. The names that come up repeatedly become our next targets for outreach.

This proved to be our highest-quality source of deal flow.

Building a Reputation

Moses Capital’s reputation initially spread through our investments and the relationships built around them. Now we receive many proactive inquiries from GPs who heard about us through the VC ecosystem. We strive to be worthy of that trust.

We are not anchor LPs, we don’t sit on LPACs, and our checks aren’t large. But we do our homework. Before engaging with a GP, we usually have been tracking them for a while—monitoring their online presence, conducting references, and forming our own judgments. Our questions are prepared. We understand how fund economics work. We don’t disturb managers unnecessarily. If a fund isn’t right for us, we say so directly and explain why.

Managers appreciate this, and as a result, they refer other managers to us.

What We’ve Learned Over Two Years

Two years, 2,000 funds. We have a deeper understanding of this market and the people behind it. Every type of manager has the right to win—the key is knowing what to look for. This is an ongoing learning process, relying on our ability to see a broad enough funnel and our continuously improving dynamic sourcing mechanism.

Пов'язані питання

QWhat are the four archetypes of GPs identified by Moses Capital after reviewing over 2000 funds?

AThe four archetypes are: 1. Founder/Operator Turned Investor, 2. Spin-Outs from Established VC Firms, 3. Community-Native Managers, and 4. Quiet Technologists.

QWhat was the single most effective sourcing channel for high-quality deal flow, which was not originally planned?

AThe most effective sourcing channel was conducting blind founder reference calls during their due diligence process. They would ask founders about other investors on their cap table who provided the most help, and those repeatedly mentioned became their next targets.

QWhat was the overall pass rate for funds reviewed by Moses Capital?

AThe overall pass rate was 2.3%. They reviewed over 2000 funds and ultimately invested in only 46 of them.

QWhat was the primary reason for approximately 30% of the fund rejections?

AApproximately 30% of rejections were related to the GP or team, such as insufficient fund operating experience, lack of a clear differentiation from existing players, or a network that could not be converted into unique deal access.

QHow did Moses Capital's perspective on the emerging manager market change after building a systematic sourcing process?

AThey discovered the market was much larger than anyone thought. By systematically scraping SEC filing data, they found dozens of new funds being formed weekly, most of which were invisible to other LPs because they were too early, too small, and lacked the network to get on shortlists.

Пов'язані матеріали

1996 or 1999? Walsh's First Test is 'How to View AI'

"1996 or 1999? Wall's First Big Test Is 'How to View AI'" Federal Reserve Chairman Wall's initial challenge is not whether to raise or cut rates, but a more fundamental judgment: what kind of boom is the current AI boom? This will determine the Fed's policy path and define his legacy. Economics is split between two opposing views, according to reporter Nick Timiraos. One sees imminent productivity gains that will increase supply and cool inflation, allowing the Fed to hold steady. The other argues that while productivity benefits are distant, demand shocks are here now, and waiting for data confirmation risks missing the intervention window, forcing sharper rate hikes later. Wall has signaled a leaning toward the first view, echoing 1996-era Alan Greenspan, who embraced strong, productivity-driven growth without fear of inflation. However, Wall faces a different macro environment than Greenspan did, with tariff pressures, expanding fiscal deficits, and diminishing globalization benefits, which could force more significant inflation pressures even if AI benefits materialize. Wall's logic, expressed before taking office, is that AI-driven productivity gains won't show in official data for years. If the Fed waits for confirmation, it might mistakenly tighten policy and choke off the very growth that could suppress inflation. This argues for using forward-looking narratives over lagging data. Chicago Fed President Austan Goolsbee presents a key counter-argument. He distinguishes between expected and unexpected productivity booms. A widely anticipated boom, like the current AI wave, can cause people to spend future wealth gains in advance, overheating the economy before productivity actually rises, thus requiring preemptive rate hikes. He cites rising costs for AI data centers as evidence of such overheating. Fed Governor Christopher Waller offers a rebuttal to Goolsbee, noting the "expected spending" mechanism only works if people can borrow against future income, which many households cannot do due to borrowing constraints. Wall also faces a paradox related to his desire to reduce the Fed's use of "forward guidance" (pre-announcing policy moves). This practice was established in 1999 when Greenspan began signaling hikes to avoid market shocks. If the economy follows a less optimistic path, Wall may be forced to choose between using the guidance he wants to abolish or risking market volatility by staying silent. The ultimate question defining Wall's first major test remains: Is this 1996 or 1999?

marsbit14 хв тому

1996 or 1999? Walsh's First Test is 'How to View AI'

marsbit14 хв тому

Ethereum Q1 2026 Report: Fees Decline, Users and Transaction Volume Hit New Highs

Ethereum Q1 2026 Report: Fees Down, Users & Transactions Hit New Highs Token Terminal's Q1 2026 report on Ethereum presents a pivotal development: the network achieved record highs in monthly active users (13.2M, +85.9% YoY), total transactions (200.4M, +81.5% YoY), and throughput (25.78 TPS), while transaction fees on the mainnet plummeted by 47.9% quarter-over-quarter. This shift is attributed to the network's strategic move into a "low fees for scale" phase, exemplified by the Fusaka upgrade which increased data capacity and lowered block space costs, releasing pent-up demand (a manifestation of Jevons's Paradox). The report highlights a core narrative shift for Ethereum: from a DeFi-centric blockchain to a global financial settlement layer. It maintains a dominant position in tokenized assets, holding majority market shares among top chains in stablecoins (61.8%), tokenized funds (73.0%), and tokenized commodities (84.0%). Growth in tokenized funds (+73.1% YoY) and commodities (+325.9% YoY) was particularly strong, driven by institutions like BlackRock and JPMorgan entering the space. Contrasting these usage gains, several USD-denominated value metrics declined in Q1: fully diluted market cap fell 30.3% QoQ, total value locked (TVL) dropped 11.0%, and ecosystem transaction volume decreased 24.0%. The report interprets this as Ethereum prioritizing long-term network expansion and cementing its role as the default settlement layer for finance over short-term fee capture. The commentary from Etherealize argues that, much like the early internet, Ethereum's open, permissionless model is poised to win over closed alternatives as institutional tokenization accelerates.

marsbit1 год тому

Ethereum Q1 2026 Report: Fees Decline, Users and Transaction Volume Hit New Highs

marsbit1 год тому

He Just Raised 2.7 Billion, and Li Fei-Fei Also Invested

Pete Florence, a former senior research scientist at Google DeepMind and a key contributor to the Vision-Language-Action (VLA) model architecture, is deliberately distancing his startup, Generalist AI, from the trendy "world model" label. He argues that the industry should prioritize concrete goals over buzzwords. His goal is to create robots that can perform a vast range of unseen tasks with high speed and success rates, without needing task-specific training data. Recently, his company raised $400 million (¥2.7 billion) at a $2 billion valuation. Notable investors include NVIDIA's NVentures, Bezos Expeditions, NFDG, as well as Xiaomi co-founder Lin Bin, Zoom founder Eric Yuan, and renowned AI scientist Fei-Fei Li. Florence's approach stems from his academic background at MIT under Professor Russ Tedrake, focusing on understanding the physical world. After joining DeepMind, he developed models like Transporter Network and co-created the VLA framework. He left in 2025 to found Generalist AI. The company has launched two models: GEN-0, which demonstrated that scaling laws apply to physical motion, and GEN-1. GEN-1 was trained on over 500,000 hours of physical interaction data collected via a specialized wearable device. It achieves a 99% success rate on precise mechanical tasks like folding boxes and maintains performance three times faster than its predecessor. Florence believes GEN-1 is reaching a commercial utility threshold similar to the GPT-3 inflection point. The substantial funding round, following GEN-1's release, signifies strong investor confidence in Generalist AI's practical, goal-driven path to creating versatile, useful robots, regardless of the "world model" terminology.

marsbit2 год тому

He Just Raised 2.7 Billion, and Li Fei-Fei Also Invested

marsbit2 год тому

Two Legends Lost in Three Days: Is Google's AI Talent Dam Cracking?

In three days, Google lost two AI legends. On June 18, Noam Shazeer, co-author of the seminal "Attention is All You Need" paper and Gemini co-lead, left for OpenAI. Just 48 hours later, John Jumper, 2024 Nobel laureate and AlphaFold lead, departed DeepMind for Anthropic. This follows Andrej Karpathy joining Anthropic in May. These moves highlight a structural trend: top AI talent is concentrating at mission-driven, pre-IPO firms like OpenAI and Anthropic, while Google becomes a primary source. The exodus stems from a core mission mismatch. Google's ad-centric model often subordinates AI research to product and revenue goals, creating friction for pioneers like Shazeer, who returned in 2024 only to leave again. In contrast, OpenAI and Anthropic offer singular focus on pushing AI boundaries, whether towards AGI or safety-aligned models, which deeply appeals to top researchers like Jumper. Financial incentives amplify the pull. With both OpenAI and Anthropic nearing IPO, employees stand to gain immensely from equity, an upside Google's mature stock cannot match. Furthermore, the 2023 merger of Google Brain and DeepMind, intended to consolidate strength, has instead created cultural tension and slowed the path from research to product, as evidenced by Gemini's pace. This talent redistribution is reshaping the AI landscape. While Google retains vast data and compute resources, its true crisis is the quiet, continuous loss of the people who define the field's future. The real moat in AI is not infrastructure, but the concentration of brilliant minds—a battle Google is currently losing.

marsbit4 год тому

Two Legends Lost in Three Days: Is Google's AI Talent Dam Cracking?

marsbit4 год тому

Торгівля

Спот
Ф'ючерси
活动图片