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Day One Ventures Financial Model

• Fund thesis: founder-centric VC firm that combines seed/early-stage equity investment with in-house PR and communications services for portfolio companies.
• Differentiation: communications help is described as "the product." The fund runs structured PR onboarding (messaging docs, media pitching) for each portfolio company, versus outsourcing to agencies costing $10–30k/month.
• Expansion vision: plan to extend value-add beyond communications to product, go-to-market, recruiting, and design.
• Investment stages: pre-seed, seed, Series A, Series B.
• Focus sectors: AI/ML, VR/AR, Consumer, Fintech, Edtech, Marketplaces, Impact.
• LP positioning supported by coverage in TechCrunch, WSJ, Reuters, Business Insider, VentureBeat, PE Hub.

Sector-level data presented as investment thesis context (not fund TAM):

• AI/ML: global enterprise AI market $845.4M (2017) → $6,141.5M (2022).
• VR: $7.90B (2018) → $34.08B (2023); AR: $11.14B (2018) → $60.55B (2023).
• Fintech: global VC funding exceeded $31B in 2017; US ~⅔ of global.
• Consumer subscription: 11M+ US subscribers in 2017; industry growing 200% annually since 2011.
• VC market context: ICO raises hit $5.6B in 2017; SoftBank Vision Fund launched at $100B; deal volume fell even as dollar volume rose.

No addressable LP market size stated for the fund itself. Not in deck.

Fund economics model:

• Management fee: 2.5% on committed capital during 4-year investment period, then on net invested capital thereafter.
• Carried interest: 20% (standard; hurdle rate not disclosed).
• Fund size: $30M.
• Fund term: close-ended, 10+1+1 years.
• Cash payback period: 5 years plain.
• No preferred return / hurdle rate disclosed in deck.

Angel / pre-fund portfolio evidence:

• NtechLab: invested May 2016, exit October 2017, 10.5× return.
• Acquired.io: exited via acquisition by Adjust.
• Feastly: exited via acquisition by ChefsFeed.
• ~30 prior investments shown across Fund I portfolio grid; includes Superhuman, Truebill, DigitalGenius, domuso, Holloway, Fable, Winnie, Octi, Pillar, lvl5, etc.

PR/communications track record:

• NtechLab: 100+ publications → 600+ B2B inbound leads.
• MEL Science: 50+ publications in Forbes, Engadget, HuffPost, TechCrunch.
• Piper: 100+ publications, 4 new investors brought in.

Dealflow:

• 200–250 opportunities reviewed per month.
• 10–15 per month into due diligence.
• 1–2 investments per month.

No AUM, DPI, TVPI, IRR, or fund-level financial performance data disclosed. One confirmed return (10.5× NtechLab). No aggregate portfolio value disclosed.

• Competitive framing: VC money has become a commodity; best founders are selective about investor value-add.
• Moat claimed: proprietary in-house PR function (not outsourced), founder-centric culture, strong co-investor network (Sequoia, a16z, NEA, Founders Fund, Benchmark, Lightspeed etc.).
• Angel network: notable co-investors include Sam Altman, Naval Ravikant, John Collison, Jason Calacanis, Paul Buchheit, Mark Pincus.
• No direct competitor funds named.

Team:

• Masha Drokova - Founder & General Partner; former angel investor and PR studio founder (WeWork, Houzz, HotelTonight, Gett, Toptal); named Business Insider top 50 PR pro in tech.
• Natalie Issa - Head of Communications; prior: Baidu AI, D-Wave, Drive.ai.
• CJ Huntzinger - Director of Communications; prior: SparkPR, Brew (Lemonade, SmartThings).
• Yury Molodtsov - Analyst; analyzed 4,000+ companies; background in applied math/physics and aerospace.

Advisors: Joel Englander (Google Cloud startup program, Blumberg/Redpoint), Ilya Zubarev (Runa Capital), Serguei Beloussov (Acronis/Runa Capital), Riccardo Di Blasio (DXC/EMC/VMware), Luis A. Navia (Verizon).

Funding ask:

• Fund size: $30M (hard cap not stated).
• Investment period: 4 years.
• No minimum LP ticket size disclosed.
• Use of funds: deploying 1–2 deals/month at pre-seed through Series A/B in AI, VR, Consumer, Fintech, Edtech, Marketplaces in US/Europe.

• Archetype + why: VC Fund Economics Model. This is an LP pitch for Day One Ventures Fund I, a $30M early-stage fund. The correct model is a closed-end fund waterfall / GP economics model - not an operating P&L. It should model: deployment schedule, portfolio construction, management fee income to the GP, carried interest, LP return scenarios (DPI, TVPI, net IRR). A standard 3-statement or SaaS model would be inappropriate.

• Forecast horizon & granularity: 12 years (10-year fund + 2 one-year extensions per term); annual granularity. Deployment in years 1–4; harvesting years 5–12.

• Key drivers & assumptions:

• Scenarios (Base / Bull / Bear - which variables flex):
• Base: 3× gross MOIC, 1–2 markups per year, standard power-law loss rate. ~2.3× net, ~15% net IRR.
• Bull: 1–2 breakout portfolio companies (10×+ à la NtechLab), 4× gross MOIC. >20% net IRR. Driven by: faster exits, lower loss rate, one large marketer (e.g., Superhuman/Truebill scenario).
• Bear: Higher-than-expected loss rate (60%), flat exit environment, slow deployment. 1.2–1.5× net MOIC; sub-8% net IRR (below hurdle).
• Flex variables: exit MOIC per bucket, loss rate, deployment pace, average check size, exit year.

• Required sheets / outputs:

1. Inputs / Assumptions - fund terms, deployment schedule, fee schedule, portfolio construction grid.
2. Deployment Schedule - annual capital calls, invested capital by year, reserves.
3. Portfolio Construction - number of companies, check sizes, follow-on, ownership % targets.
4. Management Fee Model - fee income to GP each year, transition from committed to net invested capital.
5. Portfolio Simulation - MOIC bucket distribution, proceeds by exit year.
6. Waterfall / Distributions - return of capital, preferred return (if any), carry split (20% GP / 80% LP).
7. GP Economics - total management fees + carry across fund life; GP net income by year.
8. LP Returns Summary - DPI, TVPI, RVPI, net IRR, net MOIC; by scenario.
9. Scenario Comparison - Base / Bull / Bear side-by-side on LP net IRR and MOIC.