Vertices Capital

Vertices Capital
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A podcast for new Limited Partners in Venture Capital. Vertices Capital specializes as an "Outsourced Chief Investment Officer" (a.ka. OCIO) specialized in Venture Capital. We partner with boutique family offices, independent asset managers, and regulated banks. We help them understand the Venture Capital landscape, build bespoke investment strategies, and execute them with precision. verticescapital.substack.com

  1. 4d ago

    48. The other side of venture capital

    Today we’re tackling a question that almost every new Limited Partner asks when reviewing recent venture capital vintages: How do you make sense of today’s disorienting venture landscape? Right now, the private venture market is sharply bifurcated. On one side, you have a small, highly visible cohort of AI infrastructure, foundation model, and chip companies raising massive funding rounds every three to six months at staggering valuations. You see companies scaling top-line revenue at historical speeds, accompanied by eye-watering multi-billion-dollar price tags. For a new LP, this creates an intense Fear Of Missing Out (FOMO). But to understand what is actually happening, you have to look at fund size math. Mega-platform funds managing $5 billion to $10 billion or more operate under strict structural constraints. To return a $10 billion fund, generating $1 billion exits isn’t enough; they require $5 billion to $10 billion net proceeds from single positions, forcing them into potential trillion-dollar outcome bets regardless of entry price. This dynamic fuels herd mentality, where momentum investors play with “house money” from earlier markups while others pay premium valuations just to signal AI participation to their LPs. But while consensus capital chases these hyper-crowded AI mega-rounds, a massive, highly lucrative segment of the venture ecosystem is being quietly overlooked. Anatomy of the overlooked opportunities. So, what is this “other side” of venture-backed companies that the AI spectacle is obscuring? It consists of high-performing, fundamental-driven businesses, both in traditional software and applied verticals, that are compounding at steady, healthy growth rates with durable economics. During periods of intense market hype, private capital frequently rewards raw top-line expansion without scrutinizing quality. In today’s hyper-hyped AI market, many fast-growing darlings suffer from single-digit gross margins, heavy token-reselling costs, and inflated growth masking customer churn. Contrast that with the overlooked category: companies growing at 2x to 3x annually at scale, scaling, for example, from $20 million to $60 million to $150 million in revenue, while maintaining high gross margins, capital efficiency, and clear paths to profitability. Let’s look at the LP math: A company with healthy unit economics that doubles its revenue every year for five years compounds by 32x. While consensus VCs turn up their noses at 2x or 3x growth because it doesn’t fit the hyper-growth narrative, veteran investors like Oren Zeev point out a crucial reality for LPs: a significantly higher percentage of these fundamental-driven companies will deliver top-tier, realized cash returns than speculative hyper-growth mega-rounds. If a company is growing 3x sustainably with healthy unit economics and no competitor is outpacing them 10x, it represents a prime venture risk-reward opportunity. Yet because they aren’t labeled as foundation models, they trade at far more rational entry valuations. The application layer, and real competitive moats. A common concern we hear from new LPs is: “If a company isn’t building core AI models, won’t foundation model providers like OpenAI simply absorb their software?” This narrative, popularized during peak market panic as the “SaaS apocalypse”, assumes that application-layer software is merely a thin wrapper with no defensible moat. But this fundamental misunderstanding ignores how enterprise software creates long-term value. In reality, underlying code alone is rarely the sole moat. The true moats in software and applied vertical AI stem from four key pillars: * Domain expertise and workflows: deep operational integration into specialized enterprise processes. * Proprietary data and context: unique, non-public customer data assets that generic base models cannot replicate. * Regulatory trust and precision: in fields like legal, finance, tax, and accounting, software cannot be right 99% of the time, it must be 100% accurate. Generic LLMs that hallucinate cannot replace compliance-grade, trusted application harnesses. * Customer distribution and relationships: established go-to-market channels and embedded organizational trust. Furthermore, every existing software business faces a binary reality in the AI era: they are either a beneficiary or a victim of AI. Founder-led application leaders willing to discard legacy assumptions are using AI as a massive tailwind to compress operating costs and expand customer value. Take corporate spend and travel platform Navan (formerly TripActions): as an AI-enabled application leader with a dominant market position, AI serves as an accelerator for margin expansion rather than a disruption risk. Fund maths and strategy for new allocators. Now let’s connect this directly to your asset allocation strategy as an LP. How should you structure your venture portfolio in response to these market dynamics? First, pay close attention to fund size discipline. Smaller, focused early-stage funds, typically in the $200 million range, possess a structural advantage over multi-billion-dollar mega-funds when investing in overlooked, high-fundamental companies. A $200 million fund does not require a $100 billion outlier to deliver a 3x or 4x net return to its LPs. It can generate outstanding cash-on-cash DPI through disciplined entry valuations in $1 billion to $3 billion market leaders. Conversely, mega-funds buying late-stage AI positions at 50x or 100x revenues face severe multiple compression risks when public market realities set in. Second, learn from historical tech supercycles. Drawing parallels to the 1999–2000 internet boom, the core technology transformation, then the internet, now AI, was 100% real and revolutionary. However, private market enthusiasm created a massive disconnect between private valuations and public market realities. When the cycle eventually normalizes, funds that bought high-margin, fundamental-driven compounders will hold durable assets, while funds that paid top-of-market multiples on thin-margin revenue will face difficult write-downs. Actionable LP diligence framework. To wrap up today’s episode, here is a 3-point diligence checklist for new LPs evaluating prospective General Partners (GPs): * Question growth quality, not just rate: ask managers whether their portfolio companies are building high-gross-margin, sustainable businesses or simply buying top-line growth at the expense of unit economics. * Evaluate the moat beyond the model: ensure the GP is investing in application leaders with proprietary data, deep workflow integration, and domain trust, rather than thin model wrappers. * Align fund size with strategy: verify that the GP’s fund size allows them to generate top-tier LP returns without depending on hyper-inflated mega-valuations. Market disorientation and divergence are not things to fear, they are where superior risk-adjusted returns are made. By seeing past the AI FOMO, sophisticated LPs can back managers who capture the massive upside of fundamental compounders sitting right in plain sight. Stay tuned for our next episode, and meanwhile, you can reach out to us, Vertices Capital, on our website: vertices.vc. Thank you for listening. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit verticescapital.substack.com

    48. The other side of venture capital

  2. Oct 8

    47. USV’s $900M set of new funds, and the new math of early-stage venture

    When Benchmark broke its 20-year pattern of keeping funds at $425 million or below to raise $2 billion across two new vehicles, it signaled that even the most discipline-first franchises were finding the market too expensive for the traditional early-stage playbook. USV raised $900 million across two new funds: a $500 million early-stage fund and a $400 million opportunity fund. The firm’s previous comparable vehicles were $275 million and $350 million, respectively, so this raise is by far the largest in the firm’s history, and echoes this exact structural shift. Examining both moves together reveals why top-tier early-stage firms are evolving their capital strategies, as well as the key differences in how they execute that evolution. First, the shared thesis: the market has evolved. Both Benchmark and USV are famously thesis-driven, concentrated early-stage investors that historically avoided bloated fund sizes to maintain tight ownership and high return multiples. Their strategy shifts reflect three shared market realities: Escalating check sizes. Benchmark created a $1.25 billion growth fund alongside its $750 million early-stage fund to handle larger, multi-stage funding rounds. Similarly, USV noted that typical Series A checks, which used to sit around $3 million, now start between $10 million and $30 million. To maintain target ownership percentages across a portfolio of roughly 30–40 core bets without diluting deal math, fund sizes naturally had to expand. Capital intensity in new tech cycles. While building an initial software prototype is cheaper than ever, scaling a breakout company now requires substantial capital. USV highlights that capital is heavily absorbed by subsidizing compute and inference costs to drive user adoption, cutting through market noise, and funding capital-heavy categories like energy, physical AI, and full-stack robotics. Bigger outcomes and competition: Both firms recognize that startup funding rounds are larger, faster, and more competitive, while potential market outcomes have expanded significantly. Second, the strategic divergence: multi-stage expansion vs. bottom-up discipline. While both firms scaled up, their fund architectures highlight two distinct approaches to managing style drift: Benchmark’s stage expansion. Benchmark broadened its scope by launching its first dedicated growth fund ($1.25B) alongside its core early-stage vehicle ($750M). For LPs, this represents a transition toward a multi-stage platform model, raising questions about team stability, decision rights, and whether later-stage investing might dilute their core early-stage edge. USV’s bottom-up fund sizing. USV explicitly chose not to raise a $1B+ “unicorn fund” or create a separate growth franchise as a vanity metric. Instead, they constructed the $900 million vehicle bottom-up based strictly on check sizes, target ownership, and portfolio reserves. About preserving outlier math. USV emphasizes that keeping their fund at $900 million, still relatively compact compared to mega-funds, ensures their return math does not depend on rare $100 billion mega-unicorns. Exits in the single-digit or $10 billion range can still return the fund multiple times and keep incentives aligned around carry rather than management fees. Finally, the LP evaluation framework. For Limited Partners analyzing this “great unfreezing” across legacy VC franchises, these two examples offer a clear framework for evaluating manager evolution: Strategy fit vs. AuM gathering. Is the larger fund size a bottom-up requirement driven by entry check size and ownership targets, or a top-down move to capture management fees and market share? Ownership discipline. Can the firm continue to win lead positions and secure meaningful equity ownership in higher-priced rounds without overpaying? Incentive alignment. Does the expanded capital base preserve partner focus on core portfolio execution, or does it introduce friction and team turnover? When premier firms like Benchmark and USV scale their funds, it is less about abandoning discipline and more about re-underwriting fund math to match the cost structures and deal mechanics of the AI era. Stay tuned for our next episode, and meanwhile, you can reach out to us, Vertices Capital, on our website: vertices.vc. Thank you for listening. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit verticescapital.substack.com

    47. USV’s $900M set of new funds, and the new math of early-stage venture

  3. Sep 22

    46. "Sourcing with Discipline" (ep. 17 of the series "101 VC Core Principles")

    Welcome to episode number seventeen of our series called “101 VENTURE CAPITAL CORE PRINCIPLES FOR NEW LPs, WILLING TO UNDERSTAND HOW VENTURE CAPITAL REALLY WORKS”… Let’s dig in. First, number 65. Investors should avoid “false coverage,” such as attending a demo day without following up with substantive engagement. Avoiding false coverage. Attending a demo day and simply collecting business cards is not real coverage; it’s theater that looks like diligence without creating any actual signal. beforeVC’s analysis of YC’s W26 batch makes this explicit: the angels who consistently win competitive allocation aren’t the ones who show up at demo day cold, but the ones who tracked 3 to 5 companies for weeks beforehand and already had valuation conversations in motion, proving that attendance without prior engagement is functionally worthless. Then, number 66. After a high-volume event like a demo day, the quality input is measured by selecting and pursuing the top five companies identified as promising. The top-five filter. After a high-volume event like a demo day, the real signal of quality investing is not how many companies you met, but how disciplined you were in narrowing to the handful worth real pursuit. TechCrunch’s coverage of YC’s Spring 2026 Demo Day shows this in practice: across an entire batch of hundreds of companies, investors converged on just eleven standout startups worth serious follow-up, filtering aggressively rather than chasing every pitch in the room. Now, number 67. For early-stage sourcing, investors must be in the right networks to meet founders before or immediately after they leave major companies (e.g., OpenAI or Anthropic). Sourcing at the frontier labs. In AI especially, the earliest and most valuable deals often come from founders stepping out of OpenAI or Anthropic, so being embedded in those networks before a public launch is now a critical sourcing edge. This dynamic is now so significant that at least a dozen investors who originally backed OpenAI have also moved to back Anthropic’s rivals and spinouts, and trackers like the “Exodus” AI talent tracker exist specifically to help investors spot the next wave of founders departing frontier labs before they officially launch. Finally, number 68. When a deal is passed on, VCs should document the rationale in a short memo for future retrospective analysis. Memos on the ones you pass. Writing a short rationale memo for every passed deal isn’t bureaucratic overhead; it is how a fund builds institutional memory and learns from its own blind spots over time. Bessemer Venture Partners turned this discipline into a public institution with its famous “anti-portfolio,” documenting the specific reasoning behind passes on companies like Airbnb and PayPal so the firm can study its own misses rather than simply forgetting them. Stay tuned for our next episode, and meanwhile, you can reach out to us, Vertices Capital, on our website: vertices.vc. Thank you for listening. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit verticescapital.substack.com

    46. "Sourcing with Discipline" (ep. 17 of the series "101 VC Core Principles")

  4. Sep 2

    45. "Mapping talent before the deal" (ep. 16 of the series "101 VC Core Principles")

    Welcome to episode number sixteen of our series called “101 VENTURE CAPITAL CORE PRINCIPLES FOR NEW LPs, WILLING TO UNDERSTAND HOW VENTURE CAPITAL REALLY WORKS”… Let’s dig in. First, number 61. VC firms develop proprietary data sources to inform decision-making. Proprietary data as an edge. Top VC firms treat proprietary data as a genuine competitive moat, building systems that nobody else can replicate simply by buying the same off-the-shelf tools. EQT Ventures built Motherbrain in 2016, a neural-network platform that ingests growth metrics, hiring trends, and web traffic across millions of companies, and it has already sourced wins like Small Giant Games, which EQT invested in before Zynga acquired it for $700 million in 2018. Then, number 62. One proprietary method is creating a “page rank for people”, a continually updated talent map that tracks highly respected individuals, including engineers and operators. PageRank for people. One of the most powerful proprietary methods is a continually updated talent map, essentially a “PageRank for people” that tracks who the most respected engineers and operators consider their peers. Sequoia built exactly this over more than a decade by asking every senior technical leader it meets one simple question, ”who are the five smartest people you know in this role?”, logging every answer into its CRM until repeated mentions across hundreds of conversations reveal exceptional talent long before that person appears on a pitch deck. Now, number 63. This talent map allows VCs to quickly assess the quality percentile of a company’s engineering team. Reading a team before the pitch. This kind of talent map lets investors instantly gauge where a startup’s engineering team sits relative to the broader market, often before founders have even raised a formal round. SignalFire’s Beacon platform does this at massive scale, tracking 80 million companies and 600 million people to identify companies like Grammarly early by analyzing hiring velocity and user growth, giving SignalFire conviction about team quality well ahead of competing investors. Finally, number 64. The process of gathering proprietary talent data must involve genuine helpfulness and “giving before we go,” as a transactional approach will yield superficial data. Giving before going. None of this data has value if it’s gathered transactionally, because people share honest, high-signal information only when they trust that the investor is genuinely helping first. Sequoia’s own talent-mapping process depends on this: the firm’s willingness to help portfolio companies fill VP of Engineering roles for free, asking about peer talent as part of a real hiring conversation rather than a data-harvesting exercise, is precisely why its CRM captures authentic signal instead of superficial, self-serving responses. Stay tuned for our next episode, and meanwhile, you can reach out to us, Vertices Capital, on our website: vertices.vc. Thank you for listening. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit verticescapital.substack.com

    45. "Mapping talent before the deal" (ep. 16 of the series "101 VC Core Principles")

  5. Aug 16

    44. "The data behind the decision" (ep. 15 of the series "101 VC Core Principles")

    Welcome to episode number fifteen of our series called “101 VENTURE CAPITAL CORE PRINCIPLES FOR NEW LPs, WILLING TO UNDERSTAND HOW VENTURE CAPITAL REALLY WORKS”… Let’s dig in. First, number 57.High accuracy rates are meaningless if they result from passing on the entire universe of opportunities. High accuracy, empty signal. A screening model that “correctly” avoids bad companies means nothing if it achieves that accuracy by passing on almost everyone, since true skill lies in also catching the great deals, not just avoiding the bad ones. Bessemer’s famous anti-portfolio makes this vivid: the firm’s rationale for passing on Apple, PayPal, and eBay looked defensible at the time, and its overall “pass rate” was technically very accurate, yet those correct-sounding passes on future giants cost the firm far more than any bad investment ever would have. Then, number 58. VCs should track decisions using “four quadrants”: true positives, false positives, true negatives, and false negatives. The four-quadrant discipline. Tracking every decision across true positives, false positives, true negatives, and false negatives forces a fund to see its blind spots, not just celebrate its wins. This is the same logic Bessemer eventually applied to its own history by publishing its anti-portfolio (its false negatives) alongside its real portfolio, turning missed deals into a structured dataset the firm studies rather than a source of embarrassment to hide. Now, core principle number 59. The job of a growth investor is not merely to meet founders, but to generate net multiple money returns, which can involve deep system research. Growth investing means deep research, not just meetings. A growth investor’s job is not to simply meet as many founders as possible, but to generate real net multiple returns, which increasingly requires rigorous, quantitative system-level research into a company’s fundamentals. Tiger Global built its entire growth strategy on this principle: rather than relying on relationship-driven meetings, it runs compressed, metrics-heavy diligence on ARR, growth rate, NRR, and CAC payback, treating each company as a data problem to be solved quickly and precisely. Finally, number 60. Before taking a meeting, an investor should spend time researching the company using the firm’s CRM and data science signals. Research before the room. Before ever taking a meeting, disciplined investors use their firm’s CRM and data-science signals to already understand a company’s trajectory, so the meeting confirms conviction rather than builds it from scratch. Sequoia’s proprietary CRM exemplifies this: it integrates a decade-old “talent map” of engineering networks and records every prior interaction, letting partners instantly see what percentile a company’s engineering team sits in before a single call is scheduled. Stay tuned for our next episode, and meanwhile, you can reach out to us, Vertices Capital, on our website: vertices.vc. Thank you for listening. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit verticescapital.substack.com

    44. "The data behind the decision" (ep. 15 of the series "101 VC Core Principles")

  6. Aug 2

    43. The limits of capital engineering in VC (about General Catalyst's Hemant Taneja's essay)

    Hemant Taneja’s essay is a diagnosis of a shift in venture, but it overstates the novelty of the prescription and underestimates the continuing value of judgment, distribution, and founder support. Its strongest point is that capital structure is no longer a back-office concern, it is becoming a source of strategy and differentiation. At Vertices Capital, we believe the essay correctly rejects the lazy habit of treating markups as evidence of skill. In a tighter, more expensive market, venture firms can no longer rely on multiple expansion and passive public-market tailwinds to mask weak underwriting. That is a healthy correction for the industry, and one that should force better discipline. It also usefully broadens the definition of investor contribution. For many companies, especially in infrastructure, hardware, and other capital-intensive businesses, access to the right financing can matter as much as a board seat or a warm introduction. That is a real insight, not just rhetoric. Yet, the essay draws too clean a line between the old venture model and the new one. Even in capital-constrained eras, the best firms have not won on capital alone, they have combined underwriting, networks, product intuition, and company-building support. Reducing the prior generation to “counsel or connections” is too crude. It also romanticizes financial engineering. The Drexel analogy is vivid, but it blurs the difference between useful innovation and the temptation to optimize structures that transfer risk rather than create durable business value. Capital ingenuity can help founders, but it can also seduce investors into solving the wrong problem elegantly. The piece assumes that more complex capital stacks are inherently founder-friendly. That is not always true. Sophisticated financing can lower dilution and extend runway, but it can also add covenants, incentives, and governance complexity that early teams are not prepared to manage. There is also a tension in the argument about “no-moats” businesses. If moats are weak, then capital structure is not automatically a moat substitute, it may simply accelerate competition. In that world, capital efficiency and operational excellence still matter more than theoretical financial creativity. For Vertices Capital, the most credible reading of this essay is not “venture should become finance-first,” but “venture should become capital-aware.” That aligns with a pre-seed philosophy that still prioritizes culture, trust, and founder quality while acknowledging that capital design increasingly shapes outcomes. The best version of this thesis is narrower and stronger: investors should learn to structure capital better for specific business models, without confusing financial sophistication with investment edge. In other words, capital should serve the company, not become the company. This is an important essay because it names a real transition in venture economics. It becomes less convincing where it turns that transition into a broad claim that capital engineering will dominate the next era by itself. The right conclusion is more modest: the next generation of venture firms will need to be as fluent in capital structure as they are in founders, markets, and timing. Stay tuned for our next episode, and meanwhile, you can reach out to us, Vertices Capital, on our website: vertices.vc. Thank you for listening. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit verticescapital.substack.com

    43. The limits of capital engineering in VC (about General Catalyst's Hemant Taneja's essay)

  7. Jul 21

    42. The Multi-Stage Test

    A multi-stage GP can serve seed and growth well, but only with dedicated teams, incentives, and senior attention at each stage. LPs should diligence those systems, not rely on a broad “full lifecycle” label. A common critique of multi-stage venture firms is that they cannot serve early-stage founders and billion-dollar growth companies at the same time. That is too absolute. Some firms can do both. But doing both well is difficult, and LPs should understand why. The point is not that a GP cannot invest at seed and at scale. It is that delivering an exceptional product at both ends requires deliberate institutional design, not simply a broad mandate. This is the question an LP should test. The challenge is not merely check size. It is that seed and late-stage investing are fundamentally different activities. At seed, the investor is underwriting ambiguity. There may be no revenue, no established category, and very little data. The work is founder-led, relationship-intensive, and often deeply hands-on. It requires quick decisions, genuine patience, and the willingness to support a company for many years before there is clear external validation. At the other end of the market, the work changes. A major growth investment is larger, more visible, and frequently more immediate in its effect on a fund’s economics. It involves different diligence, different governance, different portfolio construction, and often a different internal decision-making process. Neither is inherently better. But they compete for attention. Imagine a firm that has just committed a very large sum to a late-stage company with proven revenues and a widely understood market. That investment may become one of the most consequential positions in the portfolio. It is reasonable that senior leadership spends substantial time on it. The LP question is whether the firm has preserved the capacity to provide the same quality of attention to a founder raising an early seed round. This is where analysis needs to move beyond labels such as “multi-stage” or “full lifecycle.” An LP should ask: who actually owns the seed relationship? Who makes the decision? How quickly can they act? What resources are dedicated to supporting the company before it becomes an obvious winner? And, crucially, are the people responsible for the earliest investments genuinely incentivised to remain engaged as the company grows? A multi-stage platform can succeed if it treats each stage as a distinct capability, with real specialist ownership, appropriate economics, and decision-making authority close to the founder. It becomes more difficult when the early-stage strategy functions mainly as an option on later deployment. In that model, seed may attract attention when a company is becoming successful, but not necessarily when it most needs conviction. For institutional LPs, this is not an argument against scale. It is an argument for precision in diligence. Do not assume that a large platform produces a better early-stage product merely because it has a seed strategy. Examine whether seed investing has its own leadership, its own incentives, its own pace, and a protected share of senior attention. The best multi-stage firms are not generalists by accident. They are institutions that have intentionally built separate but connected systems for different stages of company building. That distinction matters. Because in venture, capital can be shared across stages. Focus usually cannot. Stay tuned for our next episode, and meanwhile, you can reach out to us, Vertices Capital, on our website: vertices.vc. Thank you for listening. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit verticescapital.substack.com

    42. The Multi-Stage Test

  8. Jul 11

    41. "The Coverage Question" (ep. 14 of the series "101 VC Core Principles")

    Welcome to episode number fourteen of our series called “101 VENTURE CAPITAL CORE PRINCIPLES FOR NEW LPs, WILLING TO UNDERSTAND HOW VENTURE CAPITAL REALLY WORKS”… Let’s dig in. First, number 53.The ultimate objective is partnering with top founders, not hitting arbitrary call metrics. Founders over metrics. The real objective of any investor is building relationships with exceptional founders, not hitting a quota of calls or meetings that looks good on an internal dashboard. GoingVC’s research on how top VCs reject founders highlights this distinction clearly: firms with strong reputations treat every “no” as a product that protects founder trust for future deals, rather than optimizing rejection speed purely to clear a metrics-driven pipeline. Then, number 54. For the growth stage, a good coverage rate is about 70% of deals closed by comparable investors. Growth-stage coverage benchmark. At the growth stage, closing roughly 70% of comparable deals signals strong sourcing discipline without wasting resources chasing every deal in the market. Insight Partners’ scaled software-investing platform, which closes hundreds of growth deals annually across a large team, reflects the kind of disciplined coverage that avoids both under-participation and excessive meeting volume relative to what peer firms are seeing in the same market. Now, core principle number 55. 70% coverage is seen as optimal, 100% risks taking too many meetings that fall into "CYA territory" (Cover Your Ass). Avoiding the CYA trap. Chasing 100% deal coverage at growth stage is a red flag, not a strength, because it usually means taking meetings purely to avoid the appearance of missing something rather than pursuing genuine conviction. Tiger Global’s aggressive 2021 growth-stage sprint, deploying into 315 companies in a single year, illustrates the downside of over-coverage: the fund’s subsequent markdowns showed how chasing near-total market coverage can substitute breadth for judgment. Finally, number 56. For early-stage (seed), coverage is typically lower, around 50% to 60% of tracked competitor deals. Lower coverage at seed. Seed-stage coverage naturally runs lower, typically 50% to 60% of tracked competitor deals, because early-stage sourcing depends more on unique network access than on systematically covering every visible deal. Precursor Ventures exemplifies this approach: as a solo-GP-style seed fund, it deliberately covers a narrower set of pre-seed opportunities sourced through founder relationships rather than trying to match the tracking breadth of larger multi-partner platforms. Stay tuned for our next episode, and meanwhile, you can reach out to us, Vertices Capital, on our website: vertices.vc. Thank you for listening. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit verticescapital.substack.com

    41. "The Coverage Question" (ep. 14 of the series "101 VC Core Principles")
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A podcast for new Limited Partners in Venture Capital. Vertices Capital specializes as an "Outsourced Chief Investment Officer" (a.ka. OCIO) specialized in Venture Capital. We partner with boutique family offices, independent asset managers, and regulated banks. We help them understand the Venture Capital landscape, build bespoke investment strategies, and execute them with precision. verticescapital.substack.com

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  • Creator
    Vertices Capital
  • Years Active
    2025 - 2026
  • Episodes
    48
  • Rating
    Explicit
  • Copyright
    © Vertices Capital
  • Show Website
    Vertices Capital