💥How to Engineer Your Fundraise… | Data Driven VC Summary

1. Key Themes

Theme 1: Private markets valuations are under scrutiny: unverified marks in both PE and VC

Tech buyout marks diverge sharply from venture marks

Jen Kha (a16z) argues PE owners self-report valuations, so stress may be hidden. Quote: "In H1 2026, venture funds marked their tech holdings up 16% while buyout funds marked theirs down just 4%. Kha argues PE owners set their own marks behind closed doors, so the real stress may not show yet."

2021-22 vintage buyouts are the source of distress

Quote: "Many buyouts struck near the 2021-22 peak can no longer pay interest in cash, so it gets added to the loan. Those two vintages account for 70% of the debt lenders took over in 2025 and 2026."

Hurdle for buyout returns has roughly doubled

Quote: "a typical 2015 buyout needed about 5% annual EBITDA growth to reach 2.5x in five years. With pricier debt and flat entry multiples, the same deal now needs 10 to 12%."

Venture marks face a similar "black box" problem

Dan Gray (Odin) argues that LPs no longer demanding liquidity allow marks to go untested. Quote: "When LPs stop demanding liquidity on a 10-year horizon, Gray argues, funds can hold assets at reported values that are never reconciled. He cites a 2020 study by Gornall and Strebulaev that put reported unicorn valuations 48% above fair value on average."

Cash returned to LPs has collapsed

Quote: "Odin's analysis of Cambridge Associates data shows US venture returning just $0.59 to $0.91 per $1 called each year since 2022, against $1.26 to $1.47 in 2016-20."

Theme 2: Data is becoming a priced, financeable asset class

Training data lacks standardization today

Yoni Rechtman (Slow Ventures) draws on 60+ calls. Quote: "Training data has no standard unit or price, and deals carry wide spreads and high transaction costs. Company data sits unvalued on balance sheets until it's sold, usually as an entire dataset from a failing company."

Value accrues downstream of raw data

Quote: "Most value is added after collection, as data is turned into tasks, training environments and deployed models. If tasks sell for 2 to 5x the raw data, Rechtman argues sellers should build them, and the largest vendors are already moving into training and deploying models."

Novel real-world data will be scarce; synthetic data will commoditize the low end

Quote: "He expects synthetic data to flood the cheap end of the market, leaving the value in novel real-world data. Companies deploying AI generate it constantly, as each deployment hits edge cases models have never seen."

Investment implication: data-originating businesses and data-backed rollups

Quote: "For investors, the opportunity sits in businesses that originate novel data through their own operations. Rechtman also expects data to reach balance sheets as a financeable asset, opening room for data-backed rollups and making what to keep or share a core CEO decision."

Theme 3: GP/LP diligence is getting more rigorous and more skeptical of headline metrics

LPs should dig into sourcing and conversion, not just TVPI

Alex Menn (Begin Capital) after ~300 Fund II meetings. Quote: "LPs should ask how many deals a fund reviews each year, how sourcing splits across channels and how each channel converts into investments."

Marks should be tested against external evidence

Quote: "LPs should ask exactly how TVPI was calculated, whether each markup is backed by a round with an external lead, and whether those valuations still hold today."

Reweigh distributions over reported value

Quote: "For LPs, the piece is a reason to weigh distributions and outside-led rounds above reported value when judging managers."

Founders do reciprocal diligence on investors

Quote: "For investors, the framework shows the diligence founders run on them, from portfolio fit and reserves to which partner will champion the deal."

Theme 4: Brand-building as a deliberate GP strategy around a breakout win

A single winner isn't a brand without deliberate packaging

Laurie Owen (Refinery Media) on Menlo Ventures. Quote: "Menlo's roughly $1B Anthropic stake could be worth about $21.8B at a $1.5T valuation, yet Owen points out that earlier hits like Roku and Siri never built Menlo's brand. This time, the firm deliberately turned the win into an identity."

The playbook was a combination of familiar parts

Quote: "Menlo paired the bet with (1) a recurring enterprise AI survey the press kept citing, (2) a $100M fund for Anthropic-ecosystem startups that backed 45+ companies within about a year, (3) technical partners with their own followings and (4) builder-focused content."

Category association outlasts single-company association

Quote: "For GPs with a breakout winner or a strategy shift, Owen's advice is to tie the firm to the category the company is winning, since a single company fades as a brand asset within 10 to 15 years."

2. Contrarian Perspectives

Venture's problem is a confidence game, not a returns game

Dan Gray argues that large, liquidity-insensitive LPs have changed venture's incentives so that shared markups matter more than exits. Evidence: "a 2020 study by Gornall and Strebulaev that put reported unicorn valuations 48% above fair value on average," plus "US venture returning just $0.59 to $0.91 per $1 called each year since 2022." Implication: "Gray expects capital to drift back toward small specialist firms eventually, though he admits it's unclear what would trigger that shift."

Losing deals to tier-1 funds is a good sign for emerging managers

Quote: "Menn treats losses to tier-1 funds as a healthy signal for emerging managers, since a fund claiming to win every deal it wants is likely seeing weak deals or overpaying." This inverts the usual LP instinct to favor managers who win every competitive deal.

Private equity may be in worse shape than reported marks suggest (with a caveat on the source)

Quote: "Kha argues PE owners set their own marks behind closed doors, so the real stress may not show yet." The newsletter flags the bias: "Her case for venture rests on access to the top 5% of managers, and it comes from a firm that raises venture capital."

Raw data is the smallest slice of the data economy

Quote: "Most value is added after collection, as data is turned into tasks, training environments and deployed models." This runs against the assumption that owning the data is the moat.

3. Companies Identified

Menlo Ventures

Description: Venture capital firm
Why mentioned: Case study in rebuilding brand around an Anthropic bet
Quote: "Menlo's roughly $1B Anthropic stake could be worth about $21.8B at a $1.5T valuation... the combination made Menlo the default enterprise AI firm."

Anthropic

Description: AI lab
Why mentioned: Menlo's breakout investment and the center of its brand strategy
Quote: "a $100M fund for Anthropic-ecosystem startups that backed 45+ companies within about a year"

Spark Capital and Sound Ventures

Description: Venture firms also invested in the same winner
Why mentioned: Contrast case where large carry did not translate into brand gains
Quote: "Spark and Sound are set to earn about $2B and $1.5B in carry from the same winner, yet Owen's anecdotal read is that their brands gained far less."

Roku and Siri

Description: Earlier Menlo hits
Why mentioned: Evidence that a winner alone doesn't build a brand
Quote: "earlier hits like Roku and Siri never built Menlo's brand"

a16z

Description: Venture firm
Why mentioned: Publisher of Jen Kha's PE analysis
Quote: "Jen Kha at a16z asks whether private equity is cooked"

Odin

Description: Research/analysis outfit (Dan Gray)
Why mentioned: Source of "The Black Box" analysis of venture returns using Cambridge Associates data
Quote: "Odin's analysis of Cambridge Associates data shows US venture returning just $0.59 to $0.91 per $1 called each year since 2022"

Slow Ventures

Description: Venture firm
Why mentioned: Yoni Rechtman's AI training data research
Quote: "draws on 60+ calls across the data supply chain to argue that AI training data is becoming a priced asset"

Wave Function Ventures

Description: Venture fund founded by Jamie Gull
Why mentioned: Source of fundraising pipeline framework
Quote: "Jamie Gull, founder and GP at Wave Function Ventures and a former SpaceX engineer"

Begin Capital

Description: VC fund
Why mentioned: Alex Menn's 12 LP questions
Quote: "after around 300 meetings, calls and events for Fund II in which almost no one asked them"

Refinery Media

Description: Media/content firm
Why mentioned: Publisher of Laurie Owen's Menlo analysis
Quote: "Laurie Owen at Refinery Media unpacks how Menlo Ventures rebuilt its brand"

Bain

Description: Consulting firm
Why mentioned: Source for the EBITDA growth requirement benchmark
Quote: "Citing Bain, Kha says a typical 2015 buyout needed about 5% annual EBITDA growth to reach 2.5x in five years."

Affinity

Description: AI-first private capital CRM (sponsor)
Why mentioned: Sponsored webinar on AI in investment workflows
Quote: "why messy-data scoring models lose trust, why 80%+ of IC papers are recycled, and how permission-aware MCPs protect sensitive deals"

Final Close

Description: LP database
Why mentioned: Sponsored promo
Quote: "Find new LPs in our database of 30k+ active family offices, fund of funds, pensions & more via Final Close"

Goodwin and Harmonic

Description: Co-hosts of Data Driven VC's Slush breakfast
Why mentioned: Event promotion
Quote: "DDVC x Slush Breakfast in Helsinki Nov 18 with Goodwin & Harmonic"

4. People Identified

Jen Kha

Description: a16z author
Why mentioned: Argues tech buyouts face structural pressure
Quote: "Kha advises LPs to ask managers how much interest on 2021-22 deals is being added to the loan, then plan an exit route."

Yoni Rechtman

Description: Slow Ventures
Why mentioned: Research on AI training data as an asset class
Quote: "Rechtman argues sellers should build them [tasks], and the largest vendors are already moving into training and deploying models."

Jamie Gull

Description: Founder and GP at Wave Function Ventures; former SpaceX engineer
Why mentioned: Fundraising pipeline playbook
Quote: "Gull's CRM targets 200+ names, each with a named partner and a reason to care"

Dan Gray

Description: Odin, author of The Black Box
Why mentioned: Argues venture has become a confidence game
Quote: "large, liquidity-insensitive LPs have turned venture into a confidence game where shared markups count for more than exits"

Laurie Owen

Description: Refinery Media
Why mentioned: Analysis of Menlo's brand rebuild
Quote: "Owen's advice is to tie the firm to the category the company is winning"

Alex Menn

Description: Begin Capital
Why mentioned: 12 questions LPs should ask VCs
Quote: "Menn treats losses to tier-1 funds as a healthy signal for emerging managers"

Gornall and Strebulaev

Description: Academic researchers
Why mentioned: 2020 study on unicorn overvaluation
Quote: "a 2020 study by Gornall and Strebulaev that put reported unicorn valuations 48% above fair value on average"

Andre Retterath

Description: Author of Data Driven VC
Why mentioned: Newsletter author and webinar host
Quote: "Join Andre Retterath of Data Driven VC and Lindsay Bayuk, Affinity's CMO, on Nov 17 at 1 PM ET"

Lindsay Bayuk

Description: CMO of Affinity
Why mentioned: Co-host of sponsored webinar
Quote: "Lindsay Bayuk, Affinity's CMO"

5. Operating Insights

Build the investor pipeline like a sales funnel before the first pitch

Quote: "Gull's CRM targets 200+ names, each with a named partner and a reason to care, ideally shown by an adjacent, non-competing portfolio company. Check size filters fastest, since a fund writing $10M Series A checks won't lead a $3M pre-seed." Pair with: "For each target, Gull asks roughly three people who genuinely know the investor whether they'd forward a note at some point, so every intro can go out at once on launch day."

Rehearse on low-priority investors, but watch the clock

Quote: "A pitch needs 10 to 50 meetings to lose its kinks, so he starts with real but lower-priority investors and friendlies before his hot list. Practicing for two months, though, risks letting the round go stale." Timing note: "Founders planning a January raise should be building this list now."

GPs raising Fund II should pre-answer the LP diligence list

Quote: "GPs raising a Fund II should prepare answers to all 12 before the first meeting, including an introduction to a founder whose company was written off." Related guidance: "how many deals a fund reviews each year, how sourcing splits across channels and how each channel converts into investments."

6. Overlooked Insights

AI has commoditized workflow automation; the bottleneck is organizational design

Quote: "AI has made investment workflows cheap to automate. The hard part is organizational: who owns scoring, who sees which deals, and which IC memo sections need a human." The same blurb notes "80%+ of IC papers are recycled," a quiet signal of where AI-driven efficiency gains may be largest.

Emerging managers face a strategic tradeoff on markups versus exit quality

Quote: "Emerging managers can ride alongside large firms for markups and accept a fragile portfolio, or avoid their categories and accept slower markups, possibly in exchange for healthier and earlier exits." This framing of portfolio fragility versus markup speed is a notable strategic choice for small funds.