David Sade, CFA

As an asset class, VCs rely on outlier exit events to get their money back — whether through secondaries, aquisitions or listings. Without them, there's no cash to be paid back to the limited partners who then are short on liquidity to make future commitments. Right now, this loop seems to be broken. Globally, there's an exit drought with US seeing just $10.4B of exit value during Q3'24 — just 40% of the historical median level. The result is predictable: funds are not able to return much money back to their own investors. According to PitchBook, the 12-month distribution yield of US VC plummeted to just 5.2% of net asset value during Q1'24 — nearing the 4.2% we saw in the aftermath of the global financial crisis. In today's edition of Due Diligence, I look at VC's quest for returns and markets cautious approach towards Swvl's turnaround. Read in Data Darbar: https://lnkd.in/eRYiwgHk

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More data streaming in that LMM independent sponsors can go up against traditional PE funds, family offices, strategics, and win 💪. Three examples below each in the $5M to $20M EBITDA range, all sourced via Axial. The charts reveal the distribution of buyer interest across each of the three deals 📊. Deal #1: Home Services 🏠🔨 + $14M of EBITDA + 21 interested buyers + Independent sponsor acquires the business for 6.5x EBITDA + Business Broker from Florida Deal #2: Plumbing & Home Services 🚽🪠 + ~$5M of EBITDA + 58 interested buyers, including 15 funds, 4 family offices, and 4 strategics + Independent sponsor acquires the business for 7.75x EBITDA + sold by a micro-boutique i-bank in Texas that does <10 deals a year Deal #3: Equipment Maintenance & Repair 🛠️🚜 + ~$7M of EBITDA + 70 interested buyers, including 26 PE funds, 9 family offices, and 3 strategics + Independent sponsor acquires the business 7.25x EBITDA How are they winning? If you interview the banker post-close, it's not winner's curse. It usually is some combination of the below three factors: + Factor 1: there is a compelling angle as to why you are a natural buyer for the business. This must be credible and must resonate with the owner. Industry expertise, prior portco experience, a great thesis, a family connection to the industry, it really varies. + Factor 2: you've shown the owner you can close. Even without committed LP capital. If you have a nice track record at a prior firm but no closed deals yet, that counts and you should lean on it. + Factor 3: you've persuaded the owner that you will do right by the company. That can take many forms, but it usually boils down to doing right by employees and customers It's a great time to start your own investment firm and bet on yourself.

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NEW ESSAY: The Opportunities and Challenges of L3s L3s are emerging as the next frontier in scaling Ethereum. At Syndicate, we've worked alongside dozens of teams to pursue that opportunity. Yet there are still many challenges... Today's L3s face: • Centralization risks • Cross-chain interoperability hurdles • Economic sustainability concerns Balancing low fees with profitability will be crucial for L3s' long-term success. New economic approaches and models must emerge. Despite these challenges, L3s represent a powerful tool for addressing important long-term issues related to scalability, customizability, and community ownership. If you're thinking about or building on L3s, let's talk. We're excited to share more about our work in the coming weeks. https://lnkd.in/g_EXeWPK

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“One of the hardest questions I’ve had to answer is whether to distribute or to hold and maintain.” Roger Ehrenberg has done a fantastic job of generating DPI for his LPs and even he finds it hard! For this post, I’m going to focus on how Roger has generated liquidity in the form of a mini case study on The Trade Desk. -- The Trade Desk (TTD) started in 2009, which couldn’t have been a worse time in the market for ad tech and given the hangover from the global financial crisis. But Roger and the team at IA were extremely confident in the founders and believed they were on the right track, executing well with a truly differentiated approach. Ad tech was out of vogue and it was a bad time in the markets, making it tough for TTD to attract new investors. IA stepped up and put more money in two bridge rounds. They ended up investing $2.2M out of their $50M Fund I in the company before its Series A. TTD was ultimately able to raise their Series A in 2011. When they went out for their Series B in 2014, Roger was able to recycle $3M in proceeds from a recent acquisition in their portfolio into that round. At this point, IA had invested over 10% of its committed capital in Fund I into TTD. TTD announced its Series C in Mar 2016 and went public 9 months later. On the S-1 filing, IA Ventures owned nearly 20% of the company. This is way more than 95%+ of seed firms have when they enter a company! IA was obviously locked up, but had demand registration rights. They sold shares post-IPO via two demand registrations at an average exit value of $2.2B. Their $5.2M total investment in TTD turned into $250M - $300M of net DPI to their LPs and alone made IA Ventures I a 5x-6x net fund in less than 10 years. TTD today has a market cap of $48B. Does Roger regret selling so early? Not at all. He says: “We’re not paid to be public market investors.” IA locked in a top decile fund both from a DPI AND IRR perspective and he would make the same decision all over again. — Going forward, Roger acknowledges that the bar for IPOs has been raised meaningfully higher and strategic M&A is getting much harder to do from a regulatory perspective. He believes that liquidity will be primarily driven by private equity firms and continuation funds in our current market, both of which have far lower valuation ceilings. Firms will increasingly have to take some money off the table via secondaries in later stage rounds, something that Roger sees Fred Wilson (who he frequently refers to as his biggest VC mentor) doing a great job of. — With all of the success in Fund I and II, Roger and the team at IA have had their pick of the LP litter. In my next (and final post), I’ll share the criteria Roger uses to evaluate which LPs are the best fit for IA. Stay tuned!

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Should you raise VC money❓ A question for the ages, answered by Dylan Collins on the pod this week. No one is better placed to answer this than the man who sold three companies and is now an active investor. 💡 Dylan breaks down: - Why VC money isn’t for everyone - The hidden costs of taking investment - And how to know if you’re truly ready to scale 🚀 Listen or watch now YouTube: https://bit.ly/3COsRU1 Spotify: https://spoti.fi/4fPu6Rv Apple: https://apple.co/4gaVuJo

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Predictors of SaaS Growth: CAC Payback Period & NDR The one and only Kyle Poyar from Kyle Poyar’s Growth Unhinged explores in a recent post how combining CAC payback period with net dollar retention (NDR) offers strong insights into SaaS growth efficiency: Top performers: Companies with high NDR (100%+) and a low CAC payback period (<18 months) show a median growth of 65% YoY and a Rule of 40 at 45%. Enterprise focus: High NDR but a longer CAC payback period (18+ months) yields 35% YoY growth, but the Rule of 40 drops to 5%. PLG model: Low NDR and a short CAC payback period correlate with 25% YoY growth and a Rule of 40 at 35%. KEY TAKEAWAYS Pairing NDR and CAC helps identify growth efficiency, especially across different business models like PLG and enterprise SaaS. Subscribe to ‘Siliconnector’ Telegram channel for insights and news from Tech world and Silicon Valley: https://t.me/siliconnector

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So turns out I screwed up... I made a modelling error when looking at the timing of each round relative to the venture scale curve 🤦🏻‍♂️ As a background - I've taken Carta's Q1 2024 valuation data added their time between rounds and then mapped both to the definition of venture scale to get (an estimate of) Venture Multiples at each stage 😃 But initially I was using the latest Q1 2024 data for every round 😞 → when I should have been using it only for the latest round... So if we look at Series C as an example, I was saying... → Seed to Series A in 26 months (Q1'24 data) → Series A to B in 27 months (Q1'24 data) → Series B to C in 27 months (Q1'24 data) For a total of 80 months from Seed to Series C Historically the time between rounds didn't change much year over year and this wasn't really an issue. But they've grown dramatically over the last few years and that creates a huge issue in the way I'm modelling things here So if I want to map a Series C to the venture scale model, I'll need to work backwards... Which just means I need to use WAY more vlookups in gsheets 😂 So let's look at the Series C again... → Series C was raised in Q1 2024 → Based on Q1'24 data, that took 27 months → So their Series B was raised in Q4 2021 → Based on Q4'21 data, that took 19 months → So their Series A was raised in Q2 2020 → Based on Q2'20 data, that took 19 months → So their Seed was raised in Q3 2018 For a total of 65 months from Seed to Series C Fortunately Peter Walker's data goes back in time so I can do these look backs 🙏 This issue didn't really impact the Seed/Series A multiples, but really compounded to make things worse for the later B/C/D/E rounds - I've taken down the previous posts with the errors Hope this helps ❤️ and let me know if you find any more big errors 💪 -- ♻️ Repost to help a founder in your network Click 'visit my website' at the top of this post to register for my free Unlock your Series A session on how to navigate VC expectations in this market

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My saas.wtf newsletter just cracked the 2,500 subscriber mark. 🎉 These were the 5 most popular articles: 1️⃣ My Go-To SaaS Benchmark Reports: https://lnkd.in/eVwEqJPj 2️⃣ SaaS Valuation Myths: https://lnkd.in/e2sA_Bgn 3️⃣ Time to hit $1M ARR: https://lnkd.in/emmyt-Hy 4️⃣ Micro M&A for SaaS Startups: https://lnkd.in/e5sCK2mB 5️⃣ Cohort Analysis using AI: https://lnkd.in/ebAxhdma I hope you enjoyed the others too. 🙂 On to the next 2500 subs! #saas #startups #founders #kpi #analysis #valuation #ai #benchmarks #pricing #newsletter

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The “SaaS Monetization Sandwich”: A Rock and a Hard Place Every week, Jamin Ball publishes sobering SaaS margin facts. Here’s a highlight: Public SaaS Industry Operating Metrics * Median S&M % Revenue: 41% * Median R&D % Revenue: 25% * Median G&A % Revenue: 17% In simple terms, the average SaaS spends 41 cents on every $1 of revenue in sales and marketing. And 25 cents on research and development. And 17 cents on general admin. I’ve read these jarring numbers on his Substack, ‘Clouded Judgement,’ for the last few months. Little has changed. But quarter after quarter, the Big Three cloud providers pump out revenue growth. And we know all about Meta and Google Ad revenue growth! In the early 1920s, copper miners working in Arizona were caught between: 1. A "rock" - the dangerous, physically demanding work in copper mines and 2. A "hard place" - the threat of being fired if they complained about working conditions One hundred years on, SaaS companies aren’t breaking any rocks or doing any mining, but they face a similar rock. And a very hard place! No profits! The Cloud Platforms and Ad Platforms have them caught in a vice. “”” The SaaS Sandwich 🥪 “”” Ai? Ai? What about Ai? I hear you ask. Well, with Ai, infra costs will go up in the short term. And, Ad Costs? Who knows? Maybe up. Ai increases the number of startups. Maybe down. Ai helps optimise Ads. So what? SaaS has two obvious choices: 1. Optimise the old - PLG, SLG, Hybrid 2. Originate the new? I recently read a LinkedIn post by Alessandro Monti, who advocated for Monetization-Led Growth. It seemed timely to me. Or at least worthy of debate. But it received quite a bit of push-back. Why? In Finance, we’re taught that the numbers don’t lie. (Unless, of course, we’ve prepared them to deceive 🤣 ) Jamin’s numbers are there for all to see. SaaS is a great rental business model. But with too many superior landlords!!! “There is nothing more powerful than an idea whose time has come”…and all that jazz. Maybe…Monetization Led Growth is an idea that this SaaS profit revolution needs. #monetization #pricing

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public SaaS multiples, measured by market valuation against annualized current run-rate ARR, have now reverted to levels seen around 2016. for me, this suggests that the market may have hit rock bottom, opening up opportunities for growth (link to the report is in the comments). the elevated multiples observed during COVID, driven by the expectation of a permanently virtual and remote work setting, are gone for good, though.

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How are recent venture capital funds performing? For fund managers and LPs, it's a question with multibillion-dollar implications. Unfortunately, it's also a question that can be difficult to answer with too much specificity. That's why Carta decided to begin publishing a new report series looking at VC fund performance, leveraging data from more than 1,800 individual funds to look at how key metrics like IRR and TVPI are tracking across different segments of the venture landscape. This below chart is I think indicative of some of the larger trends. As you'd probably expect (shoutout to J-curves), older fund vintages are more likely to have higher IRRs than the 2021 and 2022 vintages, which have had far less time to help their portfolio companies grow and eventually reach an exit. But the details of this data aren't the type of thing you can intuit. Among the 2017 vintage, for instance some 31% of all funds have an IRR higher than 20%. On the other end of the spectrum, about 39% of 2017 funds have an IRR in the single-digits or lower. Been wondering how your own venture fund's metrics compare to those of its peers? Well, now you can find out. In addition to J-curves, shoutouts to Peter Walker, Hamza S., Michael Young, and Alex Lester for their work on this one. Link in the comments!

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Harry Stebbings finally hosted Cem Sertoglu, the best-returning VC of his vintage. While Harry's summary below effectively captures the key points of this excellent episode, I still encourage everyone to listen to the full conversation, as it contains many valuable insights (including these two highlights): - "The biggest predictor of a fund's (return) performance is its vintage" [crucial insight for Family Offices and Asset Managers in the Fund of Funds space] - "We opened UIPath to over 40 VCs at early rounds" https://lnkd.in/eJV-7K3t

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June 3 2024 - 10 Notable Articles To Read Article: A single customer made up 19% of Nvidia's revenue last year. UBS thinks it's Microsoft. Takeaway: The company reported that 2 customers accounted for 24% of its Q1 revenue, with recent Nvidia disclosures underscoring the concentration of its customer base. Source: Business Insider Link: https://lnkd.in/dxwNERHz Article: Is the London market regaining its fundraising mojo? Takeaway: Large block trades have dominated volumes, but lacking fund inflows, UK investors favor investing in well-known blue-chip companies over taking chances on unfamiliar growth companies. Source: Financial Times Link: https://lnkd.in/dFCFrUiq Article: Digital bank Monzo Bank reports first annual profit Takeaway: Launched nearly a decade ago, boosted by higher interest rates and increased growth in transaction fees and subscriptions. Source: Financial Times Link: https://lnkd.in/dS_8Eeqv Article: The 2023 SaaS Pricing Report Takeaway: The most prevalent pricing strategies are competitor pricing at 38%, value-based pricing at 33%, and cost-plus pricing at 30%. Source: Sammy Abdullah Link: https://lnkd.in/dF6nEjcQ Article: The state of tourism and hospitality 2024 Takeaway: Most travel is close to home, face of luxury travel is changing, and destinations will need to prepare to mitigate overcrowding as tourism grows. Source: McKinsey & Company Link: https://lnkd.in/d8Ji5fX3 Article: Tech and generational changes increase urgency of upskilling Takeaway: Not staying informed about the rapid changes in technology will have negative consequences for businesses and workers. Source: Financial Times Link: https://lnkd.in/dd6swRA8 Article: AI is promoted from back-office duties to investment decisions Takeaway: Asset managers are increasingly leveraging AI to guide investment decisions, monitor portfolio manager habits, and identify moneymaking opportunities. Source: Financial Times Link: https://lnkd.in/dW9_S8wz Article: Steve Jurvetson’s Future Ventures targets AI with $169M fund Takeaway: The firm invests in frontier technologies, including space exploration, robotics, deep learning, and lab-grown meat. Source: PitchBook Link: https://lnkd.in/dyhwgKCk Article: China Manufacturing Growth Fastest in Two Years, Survey Says Takeaway: The rapid expansion indicated by the private gauge could alleviate some concerns about slowing momentum in the manufacturing sector, which Beijing is counting on to bolster the economy this year. Source: Bloomberg Link: https://lnkd.in/ddQH3Hcr Article: These Accounting Firms Took a Different Approach to Shaking Up Their Structure Takeaway: FORVIS acquired the U.S. unit of Mazars as part of a new international partnership, aligning with similar moves by peers involving private equity, IPOs, and a new employee ownership model. Source: The Wall Street Journal Link: https://lnkd.in/dzMGnWGd #news #research #business

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xAI just raised a record breaking $6B. https://x.ai/blog/series-b Why is that company so valuable? Next Thursday (June 6) at 12:30pm, Collective[i] Forecast will feature AI investor, author, and AngelList founder Ash F. to discuss his view that AI-first companies will dominate and be trillion dollar enterprises. Ash's book, "The AI-First Company" was the best summation I've read of how companies like OpenAI, x.AI, DeepMind and Collective[i] approach building technology. Though we may look like traditional software, we operate and innovate using a completely different paradigm. We aim to optimize outcomes and the impact they have on people. Ash (in a conversation with David Perell) had a great example. "We’re so used to having a lot of unpredictability in our lives. If you think about someone tending to a farm hundreds of years ago, they had no way of telling if it was going to be a good or bad season, whether the weather was going to be good or bad, whether they could even sell what they produced, whether there was going to be demand for it, whatever else. We have a lot more predictability around those things now because we have predictive systems. That’s very good in the farmer’s case, probably. Because that predictability allows them to make decisions, like, 'All right. I know I’m probably going to be able to sell most of my crop this year, so I’m going to invest in my house. I’ll invest in my house, and then my family will have somewhere better to live, and then maybe I can consider having another kid.' That’s a decision, a really significant decision you can make based on having more information about whether or not you can sell your crop this year". Thanks to satellites (data) and AI, farmers are more empowered than most businesses. CEOs, CROs, and their teams because many rely on history and opinion (not dissimilar to The Old Farmer's Almanac) to predict how economic conditions will impact their sales, often with catastrophic consequences. Collective[i] combines data and AI to help businesses radically improve decision making related to revenue. We are AI-first and have developed a foundation model trained on a massive network of buying and selling data combined with AI to surface risks and economic opportunity. Our mission is to increase prosperity with collective intelligence. We differ from SaaS providers who developed features that support forecasting and sales processes designed in the absence of AI. We took a different (and often misunderstood) approach that required training data and the systems to process it. As a result, our AI makes predictions that are responsive to changing market conditions and enables enterprises to dynamically forecast and adapt as markets evolve. For anyone who wants to learn more about what it means to be AI-first, I hope you'll join us. #ciForecast #AIfirst #AI #opportunity #prosperity

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This is the right approach to answer a recurring VC question. 👇🏼

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VCs: 'shareholder promote' is a cap table modeling cheat code. I used to struggle to follow the calculations required to model pro forma cap tables. I would constantly run into circular logic that was hard to reason about. That circular logic required the 'iterative calculation' setting in Excel, which would break the model if it wasn't turned on. Then I read a 2017 article by Jeffrey Bussgang at Flybridge, in which he introduced the concept of 'founder promote.' Jeff tells the story of losing a deal because a founder chose another VC firm's term sheet offer with a higher pre-money valuation. Jeff's offer was more favorable to the founder despite having a lower pre-money valuation. The reason was that Jeff's offer had a smaller option pool. Jeff realized that if the founder had calculated the $ value of the founder's ownership (the 'founder promote') for both deals, the founder promote would have been higher for Jeff's deal. At Flybridge, founder promote became the standard way he and his colleagues communicated the economics of their term sheet offers to founders. After reading this article, I realized that founder promote was also a more effective way to model cap tables. However, I needed to expand the concept beyond the promote for founders to also include earlier investors. So, I came up with the concept of "shareholder promote". Shareholder promote = the existing shareholder's % ownership of the startup after an investment round, including expansions to the option pool, the conversion of SAFEs/notes, and the assumption of warrant exercise. Let's say existing shareholders would own 60% of the company after a Series A round because the new money would own 25%, the option pool would be expanded to 10%, and prior SAFEs/notes would own 5% after the conversion (1 - 25% - 10% - 5%). If there are 10m shares on the cap table before the investment, you now know there will be 10m shares / 60% shares on the cap table after the investment. Dividing 10m / 60% = 16.67m "post-money" shares on the cap table after the investment. Now you can calculate the # of shares to issue to all stakeholders by multiplying 16.67m by each stakeholder's ownership %. Series A investors = 16.67m * 25% = 4.17m shares Target option pool = 16.67m * 10% = 1.67m shares SAFE/note holders = 16.67m * 5% = 833k shares No hard-to-reason about circular logic and no iterative calculations in your spreadsheet are needed. Once I started modeling cap tables by first figuring out the 'shareholder promote' and then using it to calculate the post-money shares, then working backward from the post-money shares to determine the # of shares to issue to each stakeholder, modeling cap tables became WAY easier. -- Do you want to level up your ability to do VC mental math? I host a weekly 1-hour free webinar every Friday at 4 pm EST on another tricky term sheet valuation concept, the 'option pool shuffle.' Apply here: https://lu.ma/f35acifg 

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"It's all a series of experiments, ideally very low-cost experiments, to try and understand what resonates in the market and what doesn't. Our initial funding [at Auth0] to fund the development of the MVP that we could show to the first customers to try was $20,000." - Jon Gelsey, Former CEO of Auth0 by Okta & Xnor.ai It's pretty amazing both just how scrappy the Auth0 team was in the early days and the true power of a product-led growth motion if implemented correctly. In our latest Inside the Network #podcast episode, Ross, Mahendra, and I sit down with Jon to understand the Auth0 journey from 5 to 300 employees during his four years at the helm. Auth0 was acquired by Okta for $6.5B in February 2021 and is the only #PLG-first #cybersecurity company to achieve a multi-billion dollar exit. Post Auth0, Jon served as CEO of Xnor.ai (a computer vision and machine learning spinoff of the Allen Institute) and led it to a successful sale to Apple for ~$200M in January 2020. Here are five interesting highlights from the episode: 1) On bottom-up GTM: “There’s no better enemy or no better friend than developers.” While Auth0 competitors focused top-down on CISO/CIOs, Auth0 went bottom-up. The vision was to get to $100M ARR by selling a $100/year subscription to 1M developers. Things moved a lot faster, Auth0 closed its first 6-figure annual subscription within 6 months of launch and the first $1M annual subscription in 10 months. 2) On marketing: Jon appointed a developer to lead marketing in the early days of Auth0. The blog posts were written by Auth0 engineers and they typically only attended conferences if they could lead a technical presentation. The company also launched several free tools for developers to experiment with. 3) On fundraising: “You need to understand the psychology of VCs, they are greedy sharks who are trying to improve their returns as much as possible." Give VCs the ammunition so that when they stand in front of their partnerships they can clearly explain the pros and cons of investing in the startup without feeling embarrassed. 4) On picking founders: "Find founders who are honest and earnest." Find good people who will be fun to work with and who are top players in their field. 5) Advice for technical founders: "Trust your instincts but don’t confuse your technical wizardry with market excitement" If you want to learn new tricks on GTM strategy, fundraising, and negotiation, this is an episode you won't want to miss! If you like what you hear, please leave us a positive review on the podcast platforms and share it with others! Apple Podcast: https://lnkd.in/gbcjqnEj Spotify: https://lnkd.in/gM7PescX Podcast Website: https://lnkd.in/gUztJj8F

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Our VC Fund Performance report is out now. It’s the very first report of its kind that we’ve released. In it, we analyze aggregated and anonymized metrics from 1,803 venture funds currently using Carta Fund Administration. In this era of heightened competition amongst fund managers, our hope is that this report gives you better data to secure funding from LPs. Here are a few highlights ⤵️ ▪️ Funds in the 2022 vintage have deployed about 43% of their committed capital at the 24 month mark, the lowest share of any analyzed vintage (2017-2022). Prior vintages ranged from 47%-60% after 24 months.    ▪️ 30.6% of companies that raised a seed round in Q1 2018 made it to Series A within two years. Only 15.4% of Q1 2022 seed startups did so in the same timeframe. ▪️ Less than 10% of 2021 funds have distributed any capital back to their LPs after 3 years, as measured by DPI (distributed to paid-in capital).  These insights offer a snapshot of where the market is and where it might be headed. To see our analysis of fund performance metrics—such as IRR, TVPI, and DPI by fund vintage year, check out the full report: https://lnkd.in/gC2E6KR2 

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