Petar Petrov

LP Tip #20 out of #50: To re-up or not to re-up A re-up refers to an LP making an additional investment in a fund where they previously invested. Before we dive in I want to share that there are reasons an LP may not to re-up which are not in your control. Here is a short list: ➡️ The LP may have evolved their strategy and needs to scale back the #️⃣ of funds they invested in.   ➡️ The LP was never set up to re-up in the first place. ➡️ The LP could be an individual that found themselves in a cash crunch and overly exposed to venture.  I'm assuming that when you got your investment that the LP explicitly told you they do re-ups. Please do not assume they do. My expectation is also that the LP dug in to properly underwrite the fund. 🏴 Re-ups are not automatic. An LP will have to re-underwrite each fund. The speed of the re-up also depends on how much work was done in the initial investment, who led the investment, level of confidence there existed when that investment was done, and current pipeline of funds. There are 4 flags that may cause an LP to walk away. 1️⃣ Partner Breakups/Changes and Team Turnover:  The biggest risk in Partnerships are whether the Partners will stay together. One LP gave me a framework on how to think through this issue that has stuck with me. Partnership is like a marriage. If one feels there is potential for a breakup, does one believe it will be an amicable breakup? Or, does one believe the spouses will never talk again? If it is an amicable split that is manageable. The Partners can find a way to support the fund. Then the question becomes whether the LP wants to back the Partner/s that remain.  If there is high team turnover. The LP will want to investigate because team stability is used as a proxy for firm sustainability and ability to scale. 2️⃣ Fund Does not Execute on what they Sold the LP:  The LP “bought” something specific and typically it is because they were missing this strategy or wanted exposure to a strategy for the portfolio they were constructing. An institutional LP will invest and write a detailed investment memo flagging all the things you said you were going to do and then go back and compare when you come back to ask for money. If for example you pitched a generalist seed stage fund and you show up asking for money sharing that you invested in deeptech at A that will cause alarm. The same can be said for the number of companies you promised to invest in or check sizes you wrote. If there are big differences LPs will want to know why. 3️⃣ Bad Actions  The fund manager/s does something that is unethical. This is a no go for an LP.  What if the situation is not clearcut? This is different for everyone and requires discussions b/w GP and LP, but most LPs don't want headline risk. 4️⃣ Continuous Underperformance  If it's early days an LP will look at some key indicators to see how you are progressing over time.

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Kettleborough VC has been active for ~6 years, invested in ~30 companies, gone through ~80 funding rounds, witnessed ~12 exits including ~4 instances of partial principal erosion, seen ~4 seed to $100M journeys, tracking ~29% XIRR across the portfolio. A couple of IPOs from the portfolio surely seem very much in line ahead.

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ANNOUNCEMENT- I’m delighted to share that Asset Class has partnered with Palantir Technologies to bring AI to the Alternatives and Private Capital Markets. At Asset Class we will integrate the AIP platform and Foundry capabilities of Palantir with our Fund Operating System (Fund OS). We will work with the AIP Platform and Palantir's Foundry Ontology to support highly complex tasks / co-pilot features that will allow us to leverage the FULL dataset that comes with the Asset Class Platform. By linking EVERY step of the Investor and Fund journey on a SINGLE platform, we have a uniquely complete dataset that can inform our AI actions. Over time we will integrate this as part of our Asset Class Network. This will continue to ensure Asset Class is a far more powerful system than any other in the market. We are a platform - not just an app. In the near future, we will be an AI Enabled, Networked Platform I cannot wait to bring the power of AI to our platform to enable our clients to super-charge their investment in our platform. See the announcement here: https://lnkd.in/eCSy5Tnu

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A definite worthwhile short read that succinctly explains the current profile of VC investing in a historical context. Nurturing innovation should matter to all of us, wherever we operate, so it is important that the investing community does so as well.

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The Great Divide: Fundraising Fundraising Disparities Between Large and Small Buyout Funds With Bain Capital recently raising $9bn, the chasm between large and small buyout funds has never been more pronounced. Large buyout funds, with their established track records and deep-pocketed investor bases, continue to find fundraising relatively straightforward. These giants benefit from economies of scale, brand recognition, and the ability to attract institutional investors who are eager to deploy capital into proven entities. On the other hand, small buyout funds face a much tougher landscape. Despite often having innovative strategies and niche market focuses, these smaller players struggle to gain traction. The challenges are multifaceted: limited access to large institutional investors, higher perceived risk, and the need to demonstrate a track record in a highly competitive environment. Additionally, the due diligence process for smaller funds can be more rigorous, as investors seek to mitigate risks associated with less established managers. Looking ahead to 2025, the fundamental advantages held by large buyout funds will persist. However, whilst the fundraising landscape remains challenging for small buyout funds, opportunities for growth and differentiation exist. The key will be for these smaller players to leverage their unique strengths and continue to build compelling narratives that resonate with investors seeking both returns and innovation.

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"Small funds may have more incentive to produce higher returns The alignment of incentives between GP and LP varies drastically based on fund size. Managers of smaller funds who have invested substantial personal capital demonstrate an unwavering commitment to lucrative returns, while managers at the helm of colossal funds, buoyed by significant management fees, may exhibit a diminishing drive. This constitutes an almost incontrovertible structural benefit of managing a small VC fund. Furthermore, fund managers of established funds may, over time, raise larger subsequent funds and grow more risk averse. This can present a sub-optimal outcome for their LPs and, by virtue, manifests a bad omen for VC investing." https://lnkd.in/gHKgea4e

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Here’s a question investors are often asked: When evaluating early stage companies, how much time do you spend on due diligence around future exits? It’s not surprising we hear this question a lot. Also not surprising: it’s got a wide range of answers depending on the firm. Some don’t spend much time here at all. Others make it a point to put meaningful time in as part of their process. Our current thinking: take the time to do the work on public market comps. At Alpaca VC, we spend significant time understanding how public market investors will  realistically value a business based on margin profile, product, business model & TAM. In short, we want to know: how will this company be valued at scale when we get taken out? Yes, we can acknowledge that the journey toward exit is a windy road and that there may be pivots along the way, but there are still public market companies that have a business model similar to the early stage company you're evaluating. And you can always look at gross profit multiples if you think the margin profile will change over time. So we still do the work on the comps. Quantitative metrics we look at when making the comparison to public market comps include EBITDA multiple, revenue multiple, Gross Profit multiple or all of the above. As part of this process, it’s also important to factor in the public market company’s year-over-year revenue growth as this will also significantly impact the multiple it trades at. Simple example: if you have two public market companies with similar business models and similar margin profiles, but one's growing 100% year over year, and one's growing 50% year over year, then obviously the DCF (discounted cash flow) analysis is going to spit out a very different valuation for the one that's growing faster. Why this matters: When you take all of that information into account as you evaluate an early stage business, you can begin to create a realistic picture of how this company will be valued in the public markets at exit - or how an acquirer will value the company for an acquisition. Strategic acquirers may, of course, pay a premium, but we won’t underwrite for that. This allows us, for example, to form conviction around valuation based on revenue and gross profit predictions. If we think they can do $100M of revenue five years from now, we use this diligence process to form a thesis about whether the characteristics above (product, margin, business model, etc.) will cause the company to be valued at $200M vs. $500M vs. $1B at exit. Curious how other early stage investors think about underwriting an exit and how much time they’re spending on public market comps even though these companies are in their infancy.

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Small VC funds vs. Large VC funds by the numbers:‍ By now, most people have definitely concluded that in VC, small funds outperform their larger fund counterparts. Our view, both heuristically and backed by our own data, is that small funds can materially outperform, but there is a clear added dimension of risk that shouldn't be ignored. First, what does our data say by fund size? Keep in mind that the data below is from 253 funds, with only pre-2019 vintage years being included (to eliminate the J-curve effect and our belief that 4-5 years need to pass before a directional stance can be taken on a fund). Also, the mean performance numbers may look inflated, but they include some large outliers and have mainly (by our measure) very high quality fund managers. Also, we expect final DPI will show a softening of perhaps 20-40% in these numbers over time as markdowns continue. TLDR: Small funds (0-100MM) definitely can outperform when successful, but come with a much higher level of volatility (and even this data set has survivorship bias). The challenge for risk mitigation for small funds is discovery, diligence, and building an appropriate level of diversification through picking multiple of these funds. This can be very time intensive and laborious for most investors (given nearly 70% of current VC managers raised funs that fall in this range) Large VC funds can be fundamentally different products when it comes to risk/return through the inherent characteristics they possess, including 1)More Series B and later exposure given the higher % of reserves these funds hold for follow-on relative to seed funds 2) more portfolio companies 3) typically larger teams and brands. Also, many larger brands staple offerings of early stage + growth, which by definition will provide lower volatility and should provide liquidity sooner (in theory). However, the tradeoff for less manager risk/lower vol is a tighter band of potential outcomes, with top-end DPI outliers being very rare given the basic math of venture (especially when premium carry is taken into account). At the end of the day, each investor needs to assess their own constraints of time, risk, and return expectations to develop a portfolio of VC funds that makes sense for them. Here is the data: Small Funds (0-100MM): Mean TVPI: 4.3 Standard Deviation: 2.4 Median Portfolio Companies: 26 Mid-Sized Funds (100-250MM): Mean TVPI: 3.6 Standard Deviation: 2.0 Median Portfolio Companies: 24 Mid-Large (250-500MM): MeanTVPI: 3.0 Standard Deviation: 1.2 Median Portfolio Companies: 35 Large (500MM+): Mean TVPI: 2.7 Standard Deviation: 0.9   Median Portfolio Companies: 43  #VentureCapital #InvestmentStrategy #PrivateMarkets #VCInsights #RiskManagement #PortfolioManagement #Allocate

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What I'm reading this week... **SignalRank’s Rob Hodgkinson wrote a really interesting piece on reserve strategies across different fund styles and unicorn efficiency of follow-on** **There were three big takeaways from Google’s announcement of Willow** (1) Willow effectively reduces error rates as the number of qubits increases, overcoming a major hurdle in quantum computing and paving the way for practical, large-scale quantum machines. (2) Willow, can perform in under five minutes computations that would take today's fastest supercomputers 10 septillion years, showcasing unprecedented processing power. (3) The founder of Google Quantum AI, Hartmut Neven, wrote that that Willow’s performance was so powerful that it “had to have 'borrowed' the computation from parallel universes.” The chip is so powerful that it implies the existence of the multiverse. **Pitchbook: 9 VC firms collected half of all money raised by US Funds in 2024** (1) Rise of Mega-Funds: By 2022, the number of US VC funds raising over $1 billion grew from 10 in 2019 to 35, with large firms like Thrive Capital and General Catalyst leading the charge (2) Emerging Managers Shrinking: Emerging VC firms outside the top 30 raised just $9.1 billion in 2024, representing only 14% of US VC commitments, as institutional LPs cut support for first-time funds (3) AI Drives Capital Demand: The AI boom has increased pressure on mega-funds to secure vast LP commitments, as seen in 2024’s heavy reliance on sovereign wealth funds and public pensions like CalPERS and Sanabil Investments

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Interesting data and good work from Carta in their inaugural VC fund performance report analyzing benchmarks for more than 1800 funds across six recent vintages - https://lnkd.in/e5R_dhXp Take a look at the pace of investments. As expected, capital deployment has slowed since the 2020 peak, with 2022 funds only using 43% of their capital after 24 months, down from 60% in 2020. This trend seems broadly consistent across private company and fund investing, with GPs and their LPs being more cautious and selective with capital allocation. Raising capital is tough today. Full stop. It's easy to focus on that, but I'd encourage us all to instead consider and focus on what is less talked about - that this cautious approach has created a large amount of available capital aka "dry powder," dedicated and ready to support special founders. The best founders are aware of private market dynamics, which impact the time needed to raise rounds and the milestones they must hit to successfully do that, but their primary focus is always the mission and the customers they serve. They are committed to building products to solve their customers' problems regardless of what us investors think. Building generational companies is never easy, but many have been and will continue to be built in tough markets. I try to take a similar approach in supporting founders. That doesn't mean ignoring market conditions, which can greatly impact my investment decisions and outcomes, but like focused founders, I know it makes more sense to dedicate my limited daily energy to what I can control. That means finding and supporting founders who are transforming or creating markets and doing everything I can to help them win, no matter what the market is doing. I hope to collaborate with many doing the same. Have a great week and see you out there! Note: These thoughts and opinions are solely mine and and mine alone. They do not reflect and are not associated with any company I am part of.

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When making an investment decision the starting point for me is always the same: the founder. Not the market. No matter what stage, industry, or existing investment history – conviction in people is critical. Since I joined Index in 2008, I’ve seen a ton of change in venture. New VC entrants, shifting market dynamics, and major technological progress. Yet the core of the craft has remained the same - the focus on identifying outlier founders building the next outlier company. What defines an outlier founder? A unique combination of imagination, operational excellence, and high-quality decision making. I saw that in Olivier Pomel & Alexis Lê-Quôc at Datadog, Assaf Rappaport, Yinon C., Roy Reznik, & Ami Luttwak at Wiz, Joshua Motta & John Hering at Coalition, Inc. and other exceptional entrepreneurs. It was a pleasure to chat with Harry Stebbings about the complexities of VC and dig into key learnings from my time as an investor and board member on the latest 20VC episode. Thanks for having me, Harry!

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I recently saw this metric from Carta’s 1Q24 VC Fund Report, which is very concerning. DPI... is nowhere to be found in earlier vintages that probably should start showing DPI. Funding early-stage projects is great, but ultimately, these venture dollars need to exit their investments and pay back their limited partners. That’s where the metric Distributed to Paid-In Capital (DPI) comes in. While managing a fund, we get interim measures during the life of the fund (e.g. IRR, MOIC), but ultimately, “you can’t eat IRR.” If you want to build a lasting venture capital organization, you need to start showing DPI for your fund. Keep in mind that this is traditional VC data from Carta, and is not strictly crypto venture. Crypto venture tends to get liquidity earlier (tokens) and things tend to go parabolic sooner (faster, more unicorns) - but I’d wager that the data here is somewhat similar for Crypto VCs… Not as much DPI as there should be from these earlier vintages. Read the full article, as well as a recap of all the crypto fundraising rounds for August, here: https://lnkd.in/g3eVJ-iF

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verifiable inference. yes, that again... Luke S. just published a new thread on that https://lnkd.in/e-9dMZrF but right now, I'd like to counter the article by Casey https://lnkd.in/eduygZzD where she argues against the need for decentralized inference (in short, his argument is that we don't need it because we will have local models and edge computing). There's a key gap in these arguments against decentralized inference. 𝗠𝘆 𝗰𝗼𝘂𝗻𝘁𝗲𝗿𝗮𝗿𝗴𝘂𝗺𝗲𝗻𝘁𝘀: While advancements in edge computing and specialized chips are promising, they won't eliminate the need to use heavier, cloud-based models & inference in the near future. Hence, verifiability of decentralized models running on decentralized cloud infrastructures remains crucial. Decentralized inference ensures that computations performed on cloud services are transparent and trustworthy. This is particularly important for sensitive applications where privacy, accuracy, and resistance to censorship are paramount (i.e. - always). By implementing verifiability mechanisms, users can trust that the models used are legitimate and that the results are not tampered with. So, the main points I'm making are: 1. 𝙀𝙙𝙜𝙚 𝙘𝙤𝙢𝙥𝙪𝙩𝙞𝙣𝙜 𝙬𝙤𝙣'𝙩 𝙛𝙪𝙡𝙡𝙮 𝙧𝙚𝙥𝙡𝙖𝙘𝙚 𝙘𝙡𝙤𝙪𝙙-𝙗𝙖𝙨𝙚𝙙 𝙞𝙣𝙛𝙚𝙧𝙚𝙣𝙘𝙚 𝙨𝙤𝙤𝙣: Despite expected advancements, the computational power required for larger models will still necessitate cloud services. 2. 𝙑𝙚𝙧𝙞𝙛𝙞𝙖𝙗𝙞𝙡𝙞𝙩𝙮 𝙞𝙨 𝙚𝙨𝙨𝙚𝙣𝙩𝙞𝙖𝙡 𝙛𝙤𝙧 𝙙𝙚𝙘𝙚𝙣𝙩𝙧𝙖𝙡𝙞𝙯𝙚𝙙 𝙘𝙡𝙤𝙪𝙙 𝙞𝙣𝙛𝙚𝙧𝙚𝙣𝙘𝙚: Ensuring trust and transparency in cloud-based computations is crucial, especially when sensitive data and complex models are involved. 3. 𝙁𝙪𝙩𝙪𝙧𝙚-𝙥𝙧𝙤𝙤𝙛𝙞𝙣𝙜 𝙩𝙝𝙧𝙤𝙪𝙜𝙝 𝙫𝙚𝙧𝙞𝙛𝙞𝙖𝙗𝙞𝙡𝙞𝙩𝙮: As we transition towards more powerful edge computing, having robust verifiability mechanisms in place will ensure a seamless and trustworthy process during this period. My view is that it's a hybrid approach, where both edge and cloud-based decentralized inference coexist with strong verifiability protocols. find full article here: as promised, links in the follow-up tweet 👇 Luke's thread: https://lnkd.in/eH_efW45 Casey's full article. recommend: https://lnkd.in/e7EJ_xcW

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Can't wait to reconnect with the brilliant Amanda Herson and join the discussion with Maciek Gnutek and Iain Martin at Slush about our fund models and what they mean for Founders ❄ 🚀

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I really enjoyed breaking in our new studio to sit down with Temporal Technologies Co-Founders Samar Abbas and Maxim Fateev for the latest episode of Founded & Funded. Temporal was founded in 2019 based on an open-source microservices orchestration engine project but is the result of a more than decade-long partnership between the two co-founders that spanned their time working on different iterations of the same thing at AWS, Microsoft, and Uber. In this episode, Samar, Maxim, and I dive into the challenges of building an open-source ecosystem while also working on a commercial offering and scaling a successful company, how to navigate and adjust a product roadmap in an ever-changing world of AI and large language models, and how to successfully build an early startup team, navigate through a CEO transition, and bring on a startups first independent board member. It's a very compelling conversation and a must-listen for every builder out there. Watch the full episode on YouTube: https://lnkd.in/gymf9sES Find other platforms and the transcript on our website: https://lnkd.in/ggRtnKtM Be sure to subscribe to our channel for more deep dives into cutting-edge technologies and the minds behind them. Don't forget to like, comment, and share this conversation if you found it valuable!

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I posted last week about the shift in sentiment from LP's regarding secondaries. It used to be that GP's were told to hold their winners forever but more recently a lot of LP's are recommending pre-seed & seed stage VC's get good at navigating secondary transactions as early as Series B rounds. Obviously a lot of factors go into these decisions such as where you are in the life of the fund, how the rest of the portfolio is performing and the performance of the underlying company in question. But in this new reality, entry price and ownership relative to fund size really matter in order to still generate top decile funds by selling into later stage rounds; and that tends to be at odds with many seed fund strategies I see out there today. According to Carta, the average seed round valuation is $15M and I see some seed funds doing seed deals at $20M - $30M valuations. Many seed funds between $40M - $75M in size (avg of $50M) write seed checks at $15M valuations and probably invest $1.5M into each company to get 10%. The portfolio construction in this scenario is probably 20-25 companies given the check size. Assuming 50% dilution (probably low), you need a company to get to a $1B valuation to return your fund and you have less shots on goal to do that at a time where I think the probability of hitting a winner at seed for any single seed fund is going to go down per my post last week. It also means the opportunities to sell secondaries in later rounds in order to drive meaningful DPI is harder because it likely won't get to that sort of valuation until Series C or later; and you probably don't want to consider selling until the position is worth even more than your entire fund. Alternatively, if you write a $1M check into a deal at a $8M valuation out of a $50M fund, you are getting 12.5% ownership and probably investing in more like 30-35 companies given the initial check size. Higher ownership and more shots on goal. Assuming 50% dilution again, you get there with a $800M valuation. At even lower valuations, $1B outcomes would return multiples on the fund and it really opens up the options when it comes to selling in secondaries in order to manufacture liquidity. Given the current dynamics in the market, I wonder if more funds will start to focus on sub $10M valuation rounds.

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