Christian Voigt

All-in-One Guide to Venture Capital Valuation Method... What an investor wants from a startup, a successful exit. They seek a return equal to multiple of their initial investment or aim at a specific IRR. How do they make sure of that? How do they evaluate startups? They use “Venture Capital Method.” This Method assumes, startup will undertake an IPO at some point in the future.... The Future Value of the Firm is determined by multiplying the earnings of the firm in the year of IPO by the expected P/E ratio that the market will support. Let’s do simple math to understand this. Consider a VC looking to invest in a startup (Some assumptions) - - For a company, the month of IPO is 60 months from the First Investment - Based on the forecasting, the start-up’s Annualized Earnings at IPO is $6m - P/E Ratio at IPO= 15 - Market Cap at IPO= $6m*15= $90 m VC Firm Investment Round: First Round at Month 0: - Investment Amount= $1m - Expected IRR= 35% - Duration of Investment= 60 months - Firm Value at the Time of Investment = (Market Capitalization) / ((1+Expected IRR)^(Duration of Investment/12)) = $90m/ ((1+35%)^(60/12)) = $20.07m - Future Value of VC Investment at IPO = (First Investment Amount) * (1+Expected IRR)^(Duration of Investment/12) = $1m*(1+35%)^(60/12) = $4.48m - VC Ownership %= Future Value of VC Investment/ Market Capitalization = $4.48m/ $90 m= 5% - Investor’s ROI= $4.48m/ $1m= 4.48X Second Round at Month 12: - Investment Amount= $2m - Expected IRR= 30% - Duration of Investment= 48 months - Firm Value at the time of Investment= $90m/ ((1+30%)^(48/12))= $31.51m - Future Value of Investment at IPO= $2m*((1+30%)^(48/12))= $5.71m - VC Ownership %= $5.71m/ $90m= 6.34% - Investor’s ROI= $5.71m/ $2m= 2.86X Third Round at Month 24: - Investment Amount= $3m - Expected IRR= 25% - Duration of Investment= 36 months - Firm Value at the time of Investment= $90m/((1+25%)^(36/12))= $46.08m - Future Value of Investment at IPO= $3m*((1+25%)^(36/12))= $5.86m - VC Ownership %= $5.86m/ $90m= 6.5% - Investor’s ROI= $5.86m/$3m= 1.95X - VC Firm’s Ownership= 5%+ 6.34%+ 6.5%= 17.84% - Founder’s Ownership= 100%- 17.84%= 82.16% Now after this analysis, VC may negotiate with the Founder about ownership % - If after the first investment the #VC Firm’s target ownership= 10% - Future Value for Investor at IPO= $90m*10%= $9m - Investor’s ROI= $9m/ $1m= 9X - Investor’s IRR= ($9m/ $1m)^(12/60)-1= 55% Second Round: - VCs' target ownership= 7% - FV for Investor at IPO= $90m*7%= $6.3m - Investor’s ROI= $6.3m/ $2m= 3.15X - Investor’s IRR= ($6.3m/$2m)^(12/48)-1= 33% Third Round: - VC Firm’s target ownership= 6% - Future Value for Investor at IPO= $90m*6%= $5.4m - Investor’s ROI= $5.4m/ $3m= 1.8X - Investor’s IRR= ($5.4m/$3m)^(12/36)-1= 22% - VC Firm’s Ownership= 10%+7%+6%= 23% - Founder’s Ownership= 100%-23%= 77% Want access? 👉 Drop your email in the comments. I’ll send you the link! Or DM me your email if you prefer to keep private. . #startups #vc

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This company might be the fastest-growing enterprise AI company in the world. In January, they were doing $1B in ARR. Now? Over $4B... in just six months. And they're not done. The Financial Times reported that Anthropic is raising a new round at a $150B+ valuation - more than double their last round. Even more surprising? They're considering sovereign wealth funds from the Middle East — a reversal from their earlier stance of rejecting such money on ethical grounds. CEO Dario Amodei reportedly told employees: “No bad person should ever benefit from our success” …but admitted that’s a hard principle to scale a company on. Why does this matter? Because in the AGI race, principles, talent, and capital are all colliding - in real-time. Anthropic has: - $8B+ committed from Amazon - A strategic partnership with Google - 80%+ of revenue from business customers - Claude, which is gaining serious ground in coding and RAG workflows But it’s still an uphill battle. OpenAI leads on consumer usage with 500M+ weekly ChatGPT users. Meta is handing out $100M researcher offers like candy. xAI is shipping fast with Musk’s distribution firehose. And yet - if Anthropic pulls this off, it won’t just be a valuation story. It’ll be a case study in how to build a $100B+ AI company without playing by Silicon Valley’s old rules. This is the quiet giant that might end up rewriting the map.

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I’ve spoken to over 2 dozen MDs at PE firms I can confidently say that the arb of figuring out how to implement Vertical AI at portfolio companies is very real right now It will fundamentally change underwriting for those who can do it predictably and unlock generational returns. Most are aware they need to act. Very few have.

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VCs can earn 7 figures before a single exit. This is how VCs make money... 1.⁠ ⁠Management Fees (1–2.5%) - VCs (GPs) typically charge an annual fee (usually 2%) of the committed capital to cover operational expenses like salaries, office space, software, and travel. - ⁠These fees are taken regardless of fund performance, which means GPs earn money even if no exits happen. 2.⁠ ⁠Carried Interest (20%) - ⁠Standard carry is 20% of the fund’s profits after returning capital to LPs. It’s the primary way GPs earn big upside. - ⁠Super carry (25–30%) is accurate but typically tied to exceeding a return threshold (e.g., >3x net return or IRR benchmarks). 3.⁠ ⁠Hurdle Rate (6–10%) - A hurdle ensures LPs receive a minimum return (e.g., 8%) before GPs can earn carried interest. - ⁠Some funds do remove this to simplify payouts, especially in U.S. VC, though it's more common in private equity. 4.⁠ ⁠Equalization Interest (4–10%) - This is paid by LPs who join late in the fundraising process to “equalize” them with early LPs. It compensates early LPs for capital already deployed and gains accrued. - Some funds waive this for relationship or simplicity reasons. 5.⁠ ⁠Fund Setup Costs (~1%) - These costs cover fund formation legal work, admin setup, compliance filings, and sometimes initial marketing. GPs often front the cost, but it’s reimbursed from the fund pool once fundraising is closed. 6.⁠ ⁠Super Carry (25–30%) - Used in performance-tiered structures—only triggered if a fund hits a high return threshold. It's often viewed skeptically by LPs unless the GP has a strong track record. 7.⁠ ⁠Waterfall Distribution - European Waterfall: LPs must be fully repaid (capital + hurdle) across the entire fund before GPs receive any carry. Favored by institutional LPs for risk mitigation. - ⁠American Waterfall: Carry is taken deal-by-deal, even if the fund overall hasn't returned all capital. GPs can get rich early, even if later investments underperform. The truth? Only management fees really matter if you're talking about making money before exits happen. Everything else — carry, hurdle rates, clawbacks — only comes into play after the fund actually returns capital. If you're raising a fund (or being pitched by one), understanding the fee mechanics is non-negotiable. Follow the incentives. If you want to learn more listen to our podcast, VC.FM (Venture Capital Podcast)

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Most investors think liquidity in venture capital takes a decade. But with the right approach, the timeline can be compressed without incurring reckless risk. The reality? • Funds get stuck in 10–12 year cycles • Returns get locked for too long • There are missed chances for an earlier upside Here are 5 shifts that speed the journey: 1. Diversify Widely ↬ Spread across 100+ seed bets ↬ Risk balanced, upside unlocked faster 2. Target Growth Phases ↬ Focus on Series A–B rounds ↬ Catch the steepest value inflection points 3. Leverage Secondaries ↬ Buy discounted post-Series B shares ↬ Capture quicker liquidity opportunities 4. Optimize Fund Selection ↬ Back managers with proven exits ↬ Faster distributions follow strong track records 5. Use Co-Investments ↬ Invest directly alongside top funds ↬ Reduce fees, accelerate cash returns Long-term investing builds wealth. But smarter moves can shorten the wait. Which shift do you think matters most? If you’re ready to multiply your income and build for the long game, get my Passive Income Roadmap for free and start your step-by-step plan here: https://lnkd.in/gjuhjFJ7?

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Tech founders & CEOs. This is for you… Last hype cycle, every VC investor said this: Don’t build a services business. Why? Because fund structures - plus the power law - means they need multi-$BN outcomes. Only software fit their model. This hype cycle, the game has changed. Now, if you want to build a $BN+ tech company: Only build a services business. Let me explain... 1. Remote delivery is the norm In 2010-20, scaling services was crippled by clients demanding on-site teams. It drove up costs, wrecked culture, and created burnout. (Try persuading a hipster UX designer to sit in a corporate office 9-5.) Now, no client has the space or desire to host teams. Delivery is remote by default. 2. Global demand is unlocked Because of this, services were local by default. Supply tied to geography. Demand limited to region. Only software could scale. That constraint is gone. Supply can be anywhere. Demand can be anywhere. Services businesses can scale like never before. And here’s the multiplier… 3. AI has flipped the economics Services have always been anchored on outcomes: “I deliver X. It makes you $Y. You invest $Z.” That hasn’t changed. What has is the cost and speed of delivery. Today, you can deliver complex, high-value services 10x faster, at 10x lower cost - with much higher margins than software. That’s why General Catalyst launched a $1B+ strategy for AI services rollups. Accenture committed $3B to AI-enabled services. Bain partnered with OpenAI. Etc. I’m seeing this first-hand… Working with an enterprise-scale services company, we’ve evolved their operating model as part of a global growth strategy. 40% opex savings. Compressing speed-to-value from 12 weeks to a few hours. Or, put another way: better results for customers - at much lower cost. So, when you remove the geographic supply constraints. And turbocharge demand. Here’s the outcome: Faster growth. Higher margins. Higher multiples. Genuine business transformation. Now… The playbook? 1. Productise services → package into repeatable value. 2. Build distribution → content, partnerships, community. 3. Anchor pricing to value → tie to outcomes, not hours. 4. Leverage AI in delivery → compress timelines, cut costs, expand margins. 5. Think global → talent and clients are no longer bound by geography. The billion-dollar AI-era companies aren’t SaaS 2.0. They’re outcome-based services businesses. Leveraging AI. Designed for impact at scale. Sequoia Capital estimates this is a $10TN+ opportunity. Why not seize this now? — PS: In Q4, I’m looking for more 7-8 figure tech, software and services company founders & CEOs who want to scale to successful exit. Book a call: https://lnkd.in/ePCPva4U Or connect and DM, I’ll send the full details. — 👉 Follow me Thomas Cornwall for more on scaling and exiting companies. ♻️ Repost this if you think it can help someone in your network.

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𝐇𝐨𝐰 𝐕𝐂𝐬 𝐑𝐞𝐚𝐥𝐥𝐲 𝐕𝐚𝐥𝐮𝐞 𝐒𝐭𝐚𝐫𝐭𝐮𝐩𝐬 Most founders guess valuation. VCs model it—using structured frameworks like PWERM, OPM, and staged capital scenarios. This 50+ slide breakdown shows exactly how VCs think about risk, dilution, exit scenarios, and enterprise value. Key Takeaways: 1️⃣ Startup value = milestones met – The more milestones (revenue, hires, IP, customers) you hit, the more value you unlock—nonlinearly. 2️⃣ Staged funding is calculated, not emotional – Pre-seed might raise $1M at $4M pre, but Series C could raise $35M at $200M pre. Every round prices in risk reduction. 3️⃣ PWERM is how they model outcomes – IPO, M&A, downside, liquidation. Each outcome is weighted, discounted, and tied to your execution plan. 4️⃣ Preferred shares get the upside—and control – VCs negotiate both economic and control rights that convert based on price thresholds. 5️⃣ Cap tables get complex fast – Tranches, breakpoints, liquidation preferences, and option pools all affect who gets what at exit. Bottom line? Valuation isn’t a vibe. It’s math, risk modeling, and capital structure mechanics. Founders who understand it raise smarter—and keep more. Credit: Travis Harms, Mercer Capital PS. check out 🔔 for a winning pitch deck the template created by Silicon Valley legend, Peter Thiel https://lnkd.in/eQFrsUnE

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OpenAI replaced the pop quiz with a performance review. GDPval, its new evaluation framework, swaps trivia-style tests for real work outputs - legal briefs, vendor-fair layouts, care plans, engineering concepts - spanning 1,320 tasks across 44 occupations in 9 major GDP sectors. Each task includes real context files and blind professional grading. The question isn’t “Can the model pass a test?” but “Would a professional accept this work?” That subtle shift - from knowledge to output - crosses an economic line. Frontier models are now near expert parity on many tasks. Claude Opus 4.1 actually edged out others with the highest “win or parity” rate (just under half the tasks). It’s… kind of amazing that OpenAI released a benchmark where Anthropic beats them. That’s either admirable transparency or psychological warfare. You don’t hand your rival the mic unless you know your next verse will bury them. More important than the leaderboard is the slope. OpenAI says performance tripled between GPT-4o and GPT-5 in under a year. For those still calling AI a bubble, this is Exhibit A in exponential myopia. My early takeaways: 1. Models are crossing the “acceptable deliverable” threshold in many niches. The bottleneck has shifted from raw capability to integration: data, permissions, templates, review loops. 2. Capabilities are spiky by dimension. Claude excelled in aesthetics; GPT-5 in accuracy. “Real work” decomposes into different sub-skills, so expect model pluralism - routing tasks by strength (formatting, synthesis, quantitative rigor, etc.), not a single winner. 3. Progress is steep - and measurable. OpenAI shows large year-over-year gains on the same task family. That enables internal “capability CAGR” planning: if parity rates are compounding, refresh cycles and re-bids should be time-boxed (e.g., quarterly bake-offs). 4. It’s still one-shot. The first release doesn’t yet capture interactive, iterative work - think revisions after stakeholder feedback, clarifying ambiguous asks, or multi-day analysis. Expect the next versions to add context-rich, conversational and tool-enabled workflows; treat current scores as floor, not ceiling. 5. Open artifacts = faster learning loops. There’s also an experimental automated grader and a 220-task “gold” subset that’s open for replication. By publishing reproducible artifacts, OpenAI effectively turns AI eval into a public-good infrastructure layer - something that catalyzes progress across the entire ecosystem. GDPval doesn’t settle the “Are models as good as people?” debate; it reframes it into a more useful question: “On which deliverables - under realistic constraints - does a model already meet professional standards?” AI won’t replace labor overnight. It’ll seep into workflows, procurement checklists, and professional norms. The revolution arrives not with a pink slip, but with an accepted draft.

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How to construct a Pre-Seed VC fund in 2025? Here is the answer 👇 ◾️ You need capital for two critical functions: - Operations (keep lights on 3-4 years) - Portfolio construction (enough shots to achieve 3X) Get either wrong and you're toast. ◾️ Let me break down the operations side first. For a lean 2025 fund structure: - Solo GP or 2 GPs max - Outsourced back office - Part-time platform person - AI workflows for junior tasks Annual cost: ~$200K minimum At 2% man fee → $10M MINIMUM fund size ◾️ But portfolio construction is where most funds actually die. Here's the brutal math on unicorn hit rates: 0.5-2.5% chance a pre/seed company becomes unicorn (h/t Dan G.). Using 2% (generous assumption), your probability of catching ≥1 unicorn: - 15 companies: 26% chance - 25 companies: 39% chance - 35 companies: 50% chance - 50 companies: 63% chance ◾️ Now let's look at what this means for pre-seed in 2025. Average pre-seed: $1M round (assumed), $8M post-money (h/t Carta) If you lead rounds: - Your check: $500K (50% of round) - For 35 companies: $17.5M in checks - 50% follow-on reserve: $35M total - Including management fees: $43.75M fund size ◾️ Most emerging managers can't raise $40M+, so they don't lead. Without leading: - Your check: $200K (20% of round) - For 35 companies: $7M in checks - 50% follow-on reserve: $14M total - Including management fees: $17.5M fund size Much more realistic for emerging managers. ◾️ But here's where the math gets brutal. Your goal as emerging GP: 3X fund to raise Fund II. - $43.75M fund, 6.25% ownership → need 48X exit multiple - $17.5M fund, 2.5% ownership → need 120X exit multiple Formula: exit multiple × ownership = 3X fund return ◾️ This creates what I call the Emerging Manager Death Spiral: - Want more ownership (lower exit multiple needed) - Increase check size (more ownership = more capital per deal) - Need bigger fund (maintain company count) - Bigger fund harder to raise for first-time GP - Must reduce portfolio size (same budget ÷ bigger checks) - Lower probability of unicorn hit ◾️ I think the answer is BALANCE. You need to maximize diversification while preserving ownership. The question isn't "How much can I raise?", it's "How much do I need to not fail?". ◾️ Rely more on diversification if you're: - First-time GP without proven track record - Limited deal access - taking what's available - Risk-averse strategy - need to prove ability for Fund II ◾️ Rely more on ownership if you have: - Proven edge - domain expertise or operational background - Strong conviction framework with ability to spot non-consensus opportunities - Larger fund ($30M+) with sophisticated LP base ◾️ In practical numbers: - $15M fund: non-lead pre-seed, minimal ownership, basic survival - $25M fund: non-lead/lead pre-seed/seed, 20-30 companies, reasonable odds - $40M fund: lead pre-seed/seed, solid diversification, strong ownership + follow-on budget What do you think about pre-seed fund construction?

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