Kevin Guo

Impactful Biz, Ops & Investment Roles at Early Startups & VCs: • Chief of Staff to Ex. Head of Product at Google • Founding GTM at the most YC product used by other YC companies in the past four years • Chief of Staff to a Partner at a Top-Tier VC Fund • Head of Platform of a NY State Backed Accelerator • Investment Associate at a Seed Stage Fund Investing in Category-Defining Companies • Partner at a Defense-Focused VC Fund • Chief Revenue Officer of a New Space Company in El Segundo • Venture Capital Analyst at a fund investing in overlooked geographies • Investment Associate at a Spirit-Focused Fund • Strategy & Ops Lead at a Company Focused on Helping Improve Autism • Senior Product Manager at a Company Focused on Building Shopify for Content Creators • Program Manager for a Space Security Company • Open-ended Investment Position at a Top-Tier Accelerator • Biz & Ops Lead for One of the Most Loved Digital Consumer Products 💸 All companies are well-funded and backed by top-tier investors. ⬇ You can find links to the roles above, plus many more, in the comments.

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AI memory is fundamental for conversational AI and AI agents. It is critical for building personalized experiences and improving reasoning. Check out Letta’s course to understand how to use AI memory to enhance the customization and quality of your AI app.

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After a multi-year period when it seems that venture capital funds would only get larger, CRV is moving in the opposite direction. Instead of raising a $500 million growth fund and then a $1 billion vehicle to follow it, the firm is returning just over half of its 2022-era growth fund. Why release $275 million worth of LP commits? "Market conditions have changed for the worse. And valuations for startups are too high relative to their potential payoff." Will other firms follow CRV? If exits do not recover in the next few quarters, the answer may be yes. The nuances of capital calls can play a larger role in eventual IRR than you might have thought.👇

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Have you heard of #CapVC? It’s an application platform designed specifically for the use of #VC firms. Essentially, it’s a robust #AI-powered tool made to simplify the daily operations of managing a #fund. Isn’t that something, a VC funded start-up designed to help improve the productivity of VC funding! According to this article from TechCrunch, Cap VC is looking to become much more than just an ‘operating system’ — the company aims to be a simplified and extremely efficient way to run a VC firm: reducing digital clutter by turning PDF files, income statements and balance sheets into readable data, automating company suggestions based on the context of a VC’s portfolio, and more. Cap VC is currently developing native apps on Mac and Windows, alongside releasing an API so that developers can build on top of their foundations. So, are any of my fellow VCs interested in using this software? If not Cap VC, is there any interest for similar software to be created down the line? Are there downsides to using a third-party application like this? I want to know what you all think.

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I love YC Demo Day season. It’s an insane concentration of driven founders building something they deeply believe in at the frontier of tech. I know it's more trendy for VCs to complain about YC. But I believe Garry Tan when he says 5-10% of companies become worth a billion dollars. Maven’s hit rate from YC is even higher: 16% have had $B+ exits, and we’re trending toward a 40% graduation rate from Seed to Unicorn with investments we’ve made alongside YC. Since Maven’s founding in 2013, we’ve funded 12 YC startups out of the 66 companies we’ve invested in. Here are a few: Cruise: acquired by GM for $1B+ Embark: $4B SPAC, acquired by Applied Intuition Chariot: acquired by Ford before Series A May Mobility: raised $300M+ in follow on funding, including from Toyota Carrot Fertility: raised $100M+ in follow on funding, including from CRV and USVP Daybreak Health: Series A from Lightspeed, Series B from USV Moment Robotics: acquired by Cruise founder Kyle Vogt’s new company, The Bot Co (Maven is an investor) Our strategy differs from other YC investors. Many funds build a YC “index fund”, investing in many startups per batch. It’s not a bad move. But we focus on concentration, meeting all the companies in our thesis area and investing in one or two. Gratefully, I think every company from the list above can point to Maven as one of their most trusted partners that made a difference: intros to customers, closing follow-on funding from top VCs, connecting key hires, and offering genuine care and advice in both the best and toughest of moments. What are we looking for in a YC investment? 1) Team - a technical product team with startup DNA and hustle that can achieve a bold vision. Brilliant, determined founders no one would bet against who are ambitious enough to build an iconic company. 2) Vision worth fighting for - what are you bringing into the world that is worth all the time, effort and money required for success? Why aren’t existing solutions good enough? Are we proud to help bring this idea to the world? 3) Massive market - because of the valuation premium YC demands, even a $B outcome may not return today’s average Seed fund. Honestly, that math sucks. In order to pay the premium, we have to believe that if we’re right, we’ll be wildly successful. 4) Consumer trend - Maven invests in tech companies addressing emerging consumer trends. Even if some investments are B2B or B2B2C, they are always grounded in a consumer insight: autonomous vehicles in 2014, fertility care in 2017, mental health in 2020. Today, we’re looking at consumer applications of AI and frontier consumer health. Many successful companies we fund are outside of YC as well – like Zoom, Hello Heart, Epic!, Class, Wildtype. So we match YC’s Unicorn % across the rest of our portfolio, too 💪 YC founders in consumer tech and digital health: if you want to build an iconic company that delivers a positive impact for millions of consumers - I can’t wait to talk to you!

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Sharing a sanitized version of the mu ventures Q2 q'ly LP update. Included musings: 👉 SEOs shrinking impact, and the rise of LLMO (large language model optimization) 👉 examples of the Commerce AI application layer coming to life 👉 a modern fund strategy that accepts companies require less capital, time, and people to trigger the power law 👉 Mu's guide to investing in AI 👉 all 30 companies in the Mu-niverse (and several others we admire) Founders, VCs, LPs - if you like how we think and suspect we can partner, hit us up! https://lnkd.in/dTJ2zjav

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How do investors price pre-seed rounds?  This is perhaps one of the most misunderstood aspects of fundraising.  Oftentimes, founders want some framework to better understand what market value is when raising capital into their business.  Public equities and later stage privates have a well defined matrix of revenue/margin profiles that dictate valuation, surely there must be some framework for arriving at a pre-seed valuation. So how do investors price startups that are pre-revenue, or have maybe one of two early customers? In reality, pricing is more a byproduct of a VC fund’s portfolio construction model than any metric of the underlying business.  VCs model out portfolios to include a certain number of companies with an ownership target in each startup.  There are variations to portfolio construction, and the chosen strategy will have a direct impact on the types of rounds a fund will participate in. For example, an early stage fund might raise a $60M fund to lead investments in 30 companies and target 12.5% ownership.   After management fees, there is ~$48M in investable capital.  Assuming a fund holds back ~30% for reserves(this number varies across funds) that leaves ~$30M to invest in initial checks.   In this case a fund must average $1M checks at $8M valuations to reach their intended ownership target of 12.5%.   When you are in diligence with a fund, they are first and foremost trying to get to a yes on the investment opportunity.  When arriving at a valuation though, the math outlined above is the determining factor, more so than any multiple of revenue on the business, or the stated desires of where a founder wants to raise capital.  At the end of the day, a VC is trying to arrive at a valuation that works within their portfolio strategy. This means they are very unlikely to price a deal at $15M cap given the aforementioned portfolio construction, as they would either not own enough($1M = 6.67% ownership) or have to cut too large a check( $1.875M = 12.5%). The thing that is most likely to move an investor off their target valuation is another investor in the picture that is driving the price higher, but even then there are upward bound limits of how high an investor is willing to price a deal. I always advise our port cos to ask an investor early in conversations what their ideal check size/valuation is to make sure there is alignment with the founder's objectives.    

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The largest acquisition in crypto history occurred just last month: Stripe's $1.1B deal for Bridge. This isn't just a headline—it's a signal of how payment rails are evolving at the intersection of crypto and traditional finance. This week at Lw3 in Dallas, I’ll be speaking on why this acquisition is more than a milestone—it's a glimpse of the future. Crypto-native payment rails are no longer a niche experiment; they’re becoming foundational to global commerce. At Reciprocal Ventures, we’ve been investing in the crypto payments ecosystem since the early days, partnering with pioneering companies like Coinflow Labs, Higlobe and True Markets. This space has been at the forefront of innovation, and we’ve seen firsthand how scalable, efficient, and transparent payment rails are reshaping the way value moves in a digital-first world. I’m excited to explore what this acquisition means for the growth of this ecosystem and why it's only the beginning of a broader wave of innovation and opportunity. If you’re at Lw3, come say hi. I'll be with Steven Feldman — we’d love to connect and share ideas.

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NVIDIA's Jensen said "ChatGPT democratized computing, Llama2 democratized generative AI" ...and now Llama3 is the next level 🔥 It's fascinating to watch Meta's strategic moves. With Llama3, they prioritized training on a massive 15T token dataset to pack all into a lean 70B param model, instead of building a massive model. This allows Llama 3 to match trillion+ parameter models like GPT-4, but at 1/10th the compute, storage and inference costs! 💰 This technique was published by Google DeepMind a few years ago https://lnkd.in/gx7VU8aA Meta is not a dark horse in AI anymore. They might be the top dog. https://lnkd.in/gRRJYuxt #llama2 #llama3 #llm #chatgpt #gpt4 #ai #ml

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Big rounds are back. VC Funding is flowing. In April, there were 28 fundraising rounds announced with >$10M in funding. We've seen private market (pre-launch) deals valued >$3B recently. Private Markets are hot. Public Market comps (FDVs) are hot. But who will buy our multi-billion dollar bags? Deal Flow Digest April w/: - Recap of largest web3 funding rounds - Airtable with ALL the data - Hackathons & Demo Days - VC Fundraises Read more here: https://lnkd.in/g7V4KrAj

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💡Benchmarks in the investment industry serve as a standard for measuring the performance of securities, mutual funds, or investment managers. They provide a reference point for analyzing allocation, risk, and return of a portfolio, enabling investors to make informed decisions. Thanks Peter Walker for keeping us all informed!! Can’t wait for this!! Follow and get on the waitlist for this one! #benchmarks #fundraising #Capitalraise #founders #womenshealth #femhealth #medtech #wellness

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At Forum we look at thousands of deals each year across our Accelerator and Pre-Seed funds but only end up investing in a little over 1% of those startups.  As an early stage VC, I am in the business of saying no to nearly every startup I meet, and while there is nuanced rationale for many of the startups I pass on, a vast majority of businesses that don’t meet the qualification to get funded typically fall into one of two buckets. ▪ First time founders have a tendency to go out to market too early to raise pre-seed capital ▪   I’m sure most founders have heard some iteration of this from VCs that ultimately pass on their business.  In some cases it's a blanket excuse covering up for other pass reasons- perhaps a VC doesn’t believe in a team or isn’t bullish on the market opportunity - but in many cases founders simply haven't proven enough to the market to warrant an investment. I meet many teams with an articulate vision for the product that they want to build, but they either haven't performed enough customer discovery to prove demand for their initial wedge product, or need to raise pre-seed capital to build out an MVP.  In those cases, pre-seed investors are unwilling to underwrite those risks.  To be default fundable, teams need to be able to keep costs low, perform extensive customer discovery, and ship some version of an early product - pilots and early paid customers are key. A huge piece of this is having founding engineering talent on the team. ▪Not every startup is a venture opportunity ▪ By definition, a venture backed business is solving a problem within a market opportunity so massive and so desperately needed, that its growth rate is off the charts.   It spreads like a weed, absorbing almost unlimited amounts of capital to fulfill demand within its customer base.   Each year there are maybe 20 companies launched that ultimately have venture scale outcomes. The bar to reach this rarified air is unfathomably high and the reality is the vast majority of founders simply aren’t building for opportunities that have the potential to be one of these generational companies. Many startup founders view VC as the only viable option for capitalizing their business, but not every great business is a venture business, and most successful founders never take VC dollars.  In order to reach an IPO/acquisition large enough to generate venture returns, a startup needs a credible path to $100s of millions in revenue. This is the standard by which early stage opportunities are evaluated. The flip side to this, and I share this feedback often with founders I meet that are building interesting business in non venture markets, if you are able to scale to $10- $20M in revenue that can be a massively successful outcome for a founder.  There are investors who focus on these type outcomes, namely lower middle market PE shops, and the typical path to those deals is a bootstrapped business or a friends/family capital raise.

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Ross Haleliuk posted the fantastic article below. This is the world where I live day-to-day. At TechOperators, we invest mostly in early-stage cyber, but we do things a little differently, and we believe there are ways to invest successfully outside of pure Power Law math. The article argues that many security problems are too small for VC. I agree. I often try to convince bootstrapped founders not to raise venture because doing so can turn a successful, slow-growing bootstrapped company into a failed venture-backed company because, despite a large infusion of capital, it couldn't double every year. VC is not monolithic--not by stage, strategy, or style. Venture is often equated with "Tier 1 Venture". Ross argues that VC is not always great for early-stage cyber--and he is right. Bootstrapping AND VC work when incentives are aligned. Does it work for an early-stage VC with a <$200M fund to invest in several early companies at reasonable valuations, setting up the conditions for reasonable exits that pay off for both investors and founders? Yes. Does it work for $800M funds investing in seed stage at $100M+ valuations? Well, that depends! Power law says it does (for VC), but the unfortunate externality is that these rounds destroy companies and founder equity more often than not. There is a role for patient capital in this ecosystem to fuel successful companies that retain exit optionality as they scale--driving exit value for both founders and investors.

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Data on emerging VCs shows 2022s are hard for them but they still remain on their feet. 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. 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 had any DPI after 3 years.

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Let the wave begin! Pioneer Fund company Recall.ai *grew their revenue 10x* in the past year, has 300 enterprise customers, and just raised a $10M Series A led by Ridge Ventures. David Gu and Amanda Zhu dropped out of college at age 19 to start the company. They've grown it exponentially with a tiny team. They make it easy to build meeting bots that are cross-platform. They're aggregating all these different APIs into one unified API, the way Twilio did with cell phones. At first it "sounds" easy to build a meeting bot -- just integrate with the Zoom API. But in fact there's a huge long tail of things you need to build, security and privacy pitfalls, automations, transcription complexities, integrations, the calendar invites where someone is using Google Meet or Teams, staying up-to-date with the libraries, etc etc. Recall takes care of all of that. It's WAY easier to build on top of Recall.ai and focus on the features that make you unique. Congratulations to the Recall team!

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Batch processing for LLM inference activity: "One sign of that is the rise of batch processing. Today, popular consumer AI products like ChatGPT and Perplexity provide users with near instantaneous responses, otherwise known as real-time inference. However, businesses don't always need immediate responses and are often willing to wait hours, days, or even weeks for responses, as long as they don't have to shell out as much money. Founders of cloud and inference providers have told me there's growing pressure from business customers for this kind of flexibility, otherwise known as batch processing." -- Nathaniel Whittemore on "The AI Daily Brief" (with much deserved shout-out to his inspo from Stephanie Palazzolo at The Information) I fall into this bucket as we have a lot of backfilling to do as we're building out the underlying data in our service, and some of that could just as well be done in the wee hours of the night. The new stuff flowing in I want processed ASAFP, but these backfills—I'd mos def take more cost-efficient delayed batch processing. There's also use cases like needing (near) real-time inference for the first inference pass for timeliness / contemporaneousness value, but then a second pass slow-time inference for wider classification exercises over an ever-growing data corpus. I could see us mixing Sonnet & Haiku models in both cases. Just a heads up to Anthropic Mike Krieger as you contemplate this, along with Amazon Web Services (AWS) Jeff Barr Mark Roy Swami Sivasubramanian on the Bedrock side of things...and I don't mean the "batch" mode that doesn't seem to have any real value other than helping AWS manage its own rate limit challenges. (DeepCast is using Bedrock.)

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Embrace the "Gen-side"; Q3-2024 Generative AI $3.9B (90-days flat) or $43m per day, accross 209 deals - Pitchbook. quote "Perhaps they’re right. A Forrester report predicts 60% of generative AI skeptics will embrace the tech — knowingly or not — for tasks from summarization to creative problem solving. That’s quite a bit rosier than Gartner’s prediction earlier in the year that 30% of generative AI projects will be abandoned after proof-of-concept by 2026. “Large customers are rolling out production systems that take advantage of startup tooling and open source models,” Brendan Burke, senior analyst of emerging tech at PitchBook, told TechCrunch in an interview. “The latest wave of models shows that new generations of models are possible and may excel in scientific fields, data retrieval, and code execution.”" end quote. #money, #investments, #dollar, #venturecapital, #privateequity, #israel, #siliconvalley, #lifesciences, #artificialintelligence, #ml, #machinelearning, #deeptech, #deeplearning, #cfa, #caia, #alternativeinvestments, #hedgefunds, #gold, #bitcoin, #oil, #germany, #switzerland #geneva, #technology, #startups, #startup, #generativeai

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