Jan Hammer

We're excited to participate in the $26.5mn Seed round of Inversion led by Dragonfly alongside Faction VC, VanEck, Mirana Ventures, ParaFi Capital, Metalayer Ventures, Volt Capital, HashKey Capital, Race Capital, Portal Ventures, Moonrock Capital, Breed, Topology Ventures, Bodhi Ventures, Lvna Capital, Escape Velocity (EV3), Folius Ventures and leading angels Inversion is pioneering a unique private equity-style model to make blockchain technology "invisible" to the end user. The company's strategy focuses on acquiring traditional, low-margin businesses and integrating blockchain solutions to enhance their efficiency, speed, and cost-effectiveness. This innovative approach aims to de-risk institutional investment in the crypto space by offering a familiar private equity product with a clear "margin of safety." The newly secured capital will be used to establish Inversion Capital, a separate fund of at least $500 million, which will finance future acquisitions. Inversion has initially targeted South American telecommunications companies and has submitted several acquisition bids, as blockchain could allow these companies to lower their data purchasing costs, among other improvements. The company's plans to target both private and public companies with large user bases, particularly in sectors such as payroll services, supply chain management, and consumer financial services. The company's first acquisition is expected to take place within the next year, paving the way for a new era of mainstream crypto integration. More here: WSJ: https://lnkd.in/dDWvKm-s Ventureburn: https://lnkd.in/dQ9efYfh

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Turns Out… My Fund LP Portfolio Beat My Direct Portfolio 😅 Maybe I’m not a bad investor — maybe FoFs aren’t the fallacy we think they are. I’ve been reading several great pieces lately and reflecting on a touchy subject: venture funds of funds (FoFs). Hamilton Lane has hammered this home for years: venture isn’t really an “asset class” — it’s an access class 🚪. • 📊 VC has the widest dispersion in private markets. • ⏳ That dispersion hasn’t narrowed, even as VC scaled — the power law still rules. • 📉 Drawdowns cut deeper here than anywhere else. Translation: pick the right managers and you look like a genius; miss them and you look… well, like me on some of my direct deals 🙃. That’s why the Pattern Ventures benchmarking caught my eye: across 2005–2019 vintages, FoFs (yes, even with “double fees”) actually: ✨ Beat direct VC in the median quartile in 14/15 vintages (+0.68x TVPI). ✨ Outperformed the bottom quartile every single time (+0.89x) — and never fell below 1x. ✨ Smoothed returns, with dispersion nearly halved (0.58x vs. 1.21x). Confession time: even in my own portfolio, my LP fund investments have outperformed my direct ones. Not sure if that makes me a bad direct investor 😅 … or just a better FoF LP. Either way, it taught me something about where my real portfolio chops lie. In hedge funds, double fees kill. In venture, access math beats fee math. So the real question for LPs isn’t: “Do we avoid FoFs?” It’s: Do we build selection capability — or buy it? Because in venture, average is the most dangerous place to hide.🚫 🔖 David Dug Skylar Anurag John Samir #VentureCapital #PrivateMarkets #FundofFunds #LPInsights #VCInvesting #PowerLaw #Dispersion #InstitutionalInvesting Asilica | Redshift Capital | Allocate

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Can you build startups with fewer people & lesser capital? Yes and no! I’ve now been investing for the last 12 years. Every cohort gets sharper, more prepared, more efficient. The startup journey that was largely first principles-based, is now a lot more playbook-led. And that’s showing up clearly in how companies are being built today. Looking at companies started between 2010 to 2016-17, it was quite normal to see 20-30 people in the team around $500K ARR and 50-60 people to reach $1M ARR. Why? Most of these companies were targeting SMBs & the GTM motion wasn’t PLG, but largely sales-driven. That sales motion wasn’t too well understood from India, with lots of trial & error. Founders were still figuring out outbound, how to run LinkedIn/Google campaigns, how to build an inside sales team. So, each function needed more people + iterations. But that has changed. 1 – India has moved up the experience curve. Founders today have much more clarity on the basics when they start out. There are enough existing founders to go to as mentors & enough operators to hire so that you do not have to discover the journey necessarily through your own mistakes. 2 – Tooling has come a long way. There’s even a term now, called “GTM engineering”. People are thinking of the GTM function almost like a product. What tools should I use? How do I engineer GTM internally? 3 – Building software’s become a lot easier. Even before GPT, dev-cycles were shortening. Post-GPT, the number of developers required & time taken to build has gone down sharply. Velocity of shipping has gone up sharply. So what does that mean? I’d say the number of people needed to hit early milestones is roughly half of what it was a decade ago. But this efficiency largely holds true in the 0-1 phase. As companies scale beyond $1M ARR (into $10M/$25M), complexity increases, teams grow, and hiring often happens ahead of revenue. Inefficiencies start to creep back in. The real question is: can we sustain this early-stage efficiency as we scale, not just in people, but in capital as well? That is the harder part. On a recent trip to the Bay Area, I met a number of founders & investors, and the unanimous view was that the cost of building a startup has not come down. Primary reason is that engineering talent has become costlier, and that is when it is available, particularly the cutting edge AI talent. While the number of engineers have reduced, cost has not.  And competitive differentiation now requires companies to also build/ship a lot more to stay ahead of others. Same is true for GTM costs. High inflation + interest costs, high economic uncertainty, obsolescence threat due to AI + a clutter of options has driven sales costs to be even higher than before. Avg sales & marketing efficiency of public software companies has dropped from 0.8x to 0.4-0.5x in the recent quarters (for every dollar of sales/marketing spend, they sell software of $0.4-$0.5). Would love to hear your thoughts!

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