Can You Still Win in Venture Without a $1BN Fund? Menlo’s Venky Ganesan on the New Rules of VC

Venture/VC “new rules” and what’s changed

• Venture has become more expensive and faster-moving: teams report they “can’t find anything cheaper than $100M” even at pre-seed/seed levels. Venky argues that strategy shouldn’t be based on a snapshot because conditions change rapidly.
• Deal mechanics are increasingly value/positioning-driven rather than purely valuation-driven:
  • “Pricing, when to pay and when not to pay”
  • “How to think about market size”
  • “When to sell” / “how to be a team player in rounds”

Core VC framework: venture as an options portfolio

Venky frames seed investing as option bets and ties portfolio construction to that:

• Portfolio as options
  • Seed investments are option bets to see if a company can become an exceptional winner.
  • You need enough options (“enough tries”) to statistically hit exceptional outcomes.
• Dynamic position sizing
  • Increase position size only after quantitative evidence shows traction (e.g., revenue, usage, measurable metrics).
• Base assumption
  • Large funds can be indifferent to seed-stage valuations because their real upside comes from the ability to increase in later rounds if the company proves out.

Seed investing today: “do seed rounds still exist?”

Venky suggests classic “big seeds” ($3–5M era) are largely gone.

• Seed-stage rounds in fundamental/applied AI often attract $10M–$20M.
• Two drivers:
  • Round sizing is the main determinant of “seed-ness” now.
  • Large funds treat seed valuations as option pricing, reducing sensitivity to seed valuation comparisons.

Metrics integrity: beware of metric gaming

He describes how investors’ chosen KPIs can induce founders to “adjust” reported numbers:

• Key idea: “If an investor measures an indicator and attaches great importance to it, they begin to adjust it.”
• Example in SaaS: founders can inflate net revenue retention by structuring orders (small now, larger later), making headline retention look better.
• Takeaway: investors should prioritize ultimate value creation, not vanity metrics (“improving their grades”).

Reflexivity and market cycle behavior (Soros-style)

He discusses how “kings” (multiple consecutive strong rounds) can increase success odds via reflexivity:

• Successful company → higher markup → more capital → more attention/human capital → faster growth.
• Cycles can end when narratives become self-reinforcing and then break.

Funding structure tactics: multi-tranche rounds

He criticizes “multi-tranche” speed strategies (e.g., $100 this week, $200 next week) as cyclical and demand-like (“we buy any car/gold”).

• Conceptual concern: money types vary; he wants investors who add value (“joint construction”), not just capital.
• However, when everyone copies the tactic, it decouples from company quality.

Leadership/ego/behavioral playbook inside VC

• Primary goal: “make money for my investors”; “the rest is noise.”
• Ego management: avoid valuation/syndication ego traps. Humility matters because investors “get hit by things I don’t know” weekly.
• Individuality vs purpose:
  • VC must demonstrate uniqueness to win deals, but the purpose is still investor returns.
• Founder-ecosystem humility: “never let your ego get in the way.”

TAM / market assessment and deal pricing

He challenges simplistic “rethink TAM” reactions to overpaying:

• Overpaying sometimes reflects:
  • Higher perceived market possibilities, not literally a higher “price.”
  • Or competitive need to win the deal in an asymmetric game.
• Key VC asymmetry:
  • Errors of omission (missing winning deals) are often more costly than errors of action (overpaying for a deal).

Ownership dilution and concentration: how VC thinks about it

• He expects substantial dilution:
  • If Menlo invests with 10% ownership at the first check, they assume ~3.5–4% by the time of exit.
  • Expected ~60% dilution from the initial check due to:
    • further funding rounds
    • option pool expansion.
• This produces a time-horizon view of performance:
  • Share “erosion” depends on how long you hold (slow exits worsen dilution and IRR impact).

“IRR is the new scoreboard”

He argues VC should focus more on IRR than older cash-return metrics.

• Rationale: in modern AI/venture, MAG 7 participation and the “tax” to hyperscalers/base-model developers matter.
• Hurdle framing:
  • VC must beat public/Index-like alternatives by ~1,000 bps to justify investing in private markets.

Ownership stake philosophy (why stakes fell)

He acknowledges decreasing ownership expectations in modern VC:

• Stakes still matter, but context matters.
• It’s better to own a smaller % of a huge outcome than a larger % of a smaller one.
• You still need enough ownership to matter despite dilution and concentration realities.

When to “sell” / take money off the table (and how)

He provides examples and advice:

• Lesson from his stock story: a winner later drops ~90% → take some winnings off the table when appropriate.
• For VC holdings with large paper returns:
  • When there’s 30–40–50x growth, assess whether it’s worth “fixing anything.”
  • Timing matters: often best when entrepreneurs themselves consider withdrawing.
  • Example approach: tell the situation to “return part of the profits” to the main fund to lock results while keeping long-term exposure.
• Rule of thumb emphasized:
  • Selling doesn’t mean exiting fully—he typically would not sell the entire stake unless the company is sold.
• Framing:
  • Withdrawing some capital can improve fund-level consistency and DPIs without destroying upside.

M&A/exit window (high level)

He expects more acquisitions because of:

• competitive pressure among big AI players (one buyer forces responses)
• a regulatory regime enabling deals
• the idea that catching up in AI gets bought at a premium

He’s cautious about assuming strategic buyers will always “protect” investors: strategic acquirers often care primarily about founders/teams, not investor capitalization tables.

LP/VC fundraising dynamics

• He hears LP complaints:
  • “Enough with TVPI”—LPs want DPI (realized profit).
  • DPI is described as the “right side of the table,” making future fundraising easier.
• Capital deployment timelines:
  • VC funds are deploying faster; LPs worry.
  • He suggests this is driven by real AI capital needs and company growth rates (can’t “grow like Google” with small checks).
• Time diversification:
  • He cites Menlo’s track record and emphasizes fund creation timing and cycle effects.

Concrete examples / anecdotes used

• Avanex (AVNX): IPO shares grew from ~$5k to ~$200k, then dropped ~90%; he sold for ~$8–9k later.
  • Lesson: take winnings off the table.
• Plaxo board story / missed check chance:
  • He declined an early opportunity tied to a Sean Parker-era connection.
  • He values founders who can explain complex concepts simply (Parker cited).
• Menlo fund examples:
  • Menlo 8 (invested 10 months in 2000–2001) had poor outcomes; Menlo 7 was strong.
• Market examples (illustrative, not execution detail):
  • Instacart, Anthropic, SpaceX, Cazoo, OpenAI, Hugging Face/Nvidia, AMD buys, Stripe/OpenRouter.
• Dilution efficiency example:
  • He claims OpenRouter scaled quickly and suffered less erosion than the typical dilution pattern.

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Key KPIs / targets explicitly referenced

• Position sizing / entry discipline
  • Increase investment size only when evidence shows measurable success (revenue/usage).
• Ownership dilution assumption
  • From 10% initial ownership → ~3.5–4% at exit (assumes ~60% dilution).
• Return hurdle
  • VC must beat a public-market benchmark by ~1,000 bps to justify private markets.
• Fund reporting preference (LPs)
  • Shift from TVPI emphasis to DPI emphasis (realized profit).
• Large return thresholds
  • Selling discussions triggered around 30–40–50x returns.

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Actionable recommendations distilled from the discussion

• Portfolio construction
  • Treat seed as option bets; ensure enough “tries,” then scale winners with data.
• Metric discipline
  • Prefer ultimate business value metrics; be skeptical of KPI gaming when metrics become what’s measured.
• Deal economics
  • Don’t over-attribute mistakes to “price alone”; consider asymmetric outcomes and the opportunity cost of missing winners.
• Behavioral leadership
  • Manage ego; prioritize investor returns over syndication/valuation optics.
• Capital allocation / time horizon
  • Model dilution and IRR jointly using the likely holding period.
• Profit-taking
  • Consider partial realization (especially when entrepreneurs propose it) to lock results and improve consistency.

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Presenters / sources mentioned

• Presenter/guest: Venky Ganesan (Menlo)
• Host: Harry (name not provided in subtitles)
• Notable references/citations: Bill Gurley; Chuck Prince (quote); George Soros (reflexivity); Keynes/“bazel” reference; Doug Leone; Mike Moritz; Todd Masonis; Cameron Ring; Sean Parker; Jason Lemkin; Peter Thiel (indirectly via “MAG 7”/cycle framing, not explicit); Nikesh Arora; Frank Slutman; Ronald Reagan; David Solomon (Goldman CEO); Tom Riley; plus market/corporate examples including Nvidia, Microsoft, Apple, AMD, Stripe, Meta, Instagram, Snap, Instacart, Anthropic, OpenAI, SpaceX, Cazoo, Carvana, Leopold.