👋 Hi, I’m Andre and welcome to my newsletter Data Driven VC which is all about becoming a better investor with data and AI.
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Welcome to another Data Driven VC “Insights” episode where we cover the most interesting research and reports about startups, GPs, LPs, AI & automation.
LPs Are Missing the Venture Bus
Jen Kha, at a16z, makes the case that LP portfolios are still sized for a decades-old, small-allocation approach to venture, even as a handful of AI labs alone now represent trillions in private market value.
LPs Missed the First AI Wave: Kha writes that they regularly meet LPs holding close to zero exposure to SpaceX, Anthropic, and OpenAI, some $3.8-5T of combined value built largely in private markets, while venture sits at just 5-10% of a typical portfolio.
Venture Outcomes Now Dwarf PE's: Exit value for VC-backed companies has tripled PE's high watermark, and SpaceX's roughly $2.1T debut came in around 39 times the largest PE-backed IPO on record, Medline at $54B.
The Old Allocation Math Is Fracturing: The piece links AI's disruption of recurring revenue to buyout returns now near 15-year lows, arguing the conservative side of the alternatives book carries real risk of its own.
✈️ KEY TAKEAWAYS
For LPs, raising a target allocation achieves little without access, since a small group of funds holds the winners. Accolade Partners' Aram Verdiyan puts it at 20 of roughly 3,000 US firms compounding at 3x net over two decades. For GPs, the same dynamic runs in your favor once you're inside it: landing one behemoth earns the follow-on rights, founder referrals, and information edge that make the next one likelier.
Venture's Vanishing Middle Ground
Ethan Kurzweil at Chemistry argues that venture has split into two extremes with no viable middle ground, and lays out how his own firm is adjusting its portfolio strategy in response.
Capital Is Concentrating Into Fewer, Bigger Bets: Kurzweil cites Peter Walker's Carta data on venture moving toward higher valuations in a shrinking set of companies, what Walker calls "concentrated bets as an asset class."
Chemistry Has Backed Both Camps: The firm backs pricey consensus AI founders alongside others building in sectors far from the heat, and expects both camps to produce real winners and expensive disappointments.
Non-Consensus Sectors Risk Losing Momentum: As capital flows to consensus sectors like inference, silicon, and foundational models, Kurzweil expects non-consensus categories such as SaaS and commerce to see slower progress, carried mainly by contrarian founders.
✈️ KEY TAKEAWAYS
For VCs and founders, this is a call to make an explicit bet: pay a premium for the rare founder capable of a trillion-dollar outcome, or build conviction in overlooked categories before pricing catches up. Staying in the middle risks being squeezed from both sides as capital keeps polarizing.
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Seed Funds Need More Shots on Goal
Gil Dibner at Angular Ventures breaks down how inception-stage portfolio construction needs to change as Series A graduation rates keep falling.
More Shots on Goal, Even as Odds Fall: Dibner's model shows a seed fund expecting a 50% graduation rate at 24 investments should now plan for closer to 10-25% at 40, using volume to offset falling odds.
Graduation Rates Have Plummeted Since 2021: Seed-to-Series A graduation rates have declined since 2021 even as the qualifying bar keeps rising across metrics like ARR, growth rate, and NRR.
Follow-On Reserves Need a Much Higher Bar: Dibner argues seed funds should ruthlessly cut reserves for reflexive follow-ons, treating every follow-on dollar as competing against the option of one more initial check.
✈️ KEY TAKEAWAYS
For seed investors, Dibner's math is a direct challenge to standard portfolio construction: as graduation odds keep falling, funds need more initial checks and a much higher bar for every follow-on dollar. Funds still reserving capital for reflexive follow-ons may be underwriting a bet the data no longer supports.
Incumbents Are Coming for Vertical AI
Seema Amble at a16z maps out where AI incumbents and vertical AI-native startups will actually compete as agent capabilities move up the judgment ladder.
Incumbents Are Moving From Storing to Doing: Docusign's Iris reviews contracts, Atlassian's Rovo routes requests, and Klaviyo's Composer builds campaigns, each pushing past chatbots into agents that act. Salesforce's Claudeforce takes a different route, putting Claude in front of CRM data Salesforce still owns.
Four Agent Tiers, Ranked by Judgment: Agents run from 1) retrieval and 2) process work up to 3) policy calls and 4) principal decisions. A few incumbents are starting to reach tier 3, still bounded by the records they own.
The Learning Loop Is the Real Moat: Finished records show outcomes without the reasoning behind them. A signed contract leaves out the alternatives considered, and a closed ticket leaves out the hypotheses the team tested. Startups that capture that reasoning keep getting better at the job.
✈️ KEY TAKEAWAYS
For vertical AI investors, Amble offers a concrete diligence checklist: can experts judge the work, is judgment genuinely required, does it happen often enough to build a learning loop, and can the startup expand from one assignment into the full job. Data access alone won't be the moat once general agents like Claude can already reach across systems.
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AI's Biggest Impact Is in Sourcing
The 2026 DDVC Landscape Report analyzed 345 firms on where AI actually moves the needle, and deal sourcing and screening came out on top by a wide margin.
Front-Office Functions Dominate AI Impact: Deal sourcing and screening leads at 50%, followed by internal workflows and automation at 35% and due diligence and research at 32%, the three highest-velocity functions in the firm.
Mid-Tier Use Cases Trail Significantly: Investment memo and IC prep sits at 22%, portfolio monitoring and reporting at 18%, and CRM and pipeline management at 16%, each notably behind deal sourcing's 50%.
Relationship-Heavy Work Remains Largely Untouched: Outreach and communications, LP and IR, and legal and compliance all cluster at 10-11%, the functions closest to actual human relationships showing the least AI impact so far.
✈️ KEY TAKEAWAYS
The real opportunity may sit in the bottom three, outreach, LP and IR, and legal and compliance, all still at 10-11%. Legal's low usage likely reflects genuine regulatory limits on automation, but outreach and LP relationships look like an open gap a firm could turn into an edge without losing the human touch.
How to Win Over a Family Office
A family office investor writing at Iron and Interest lays out how emerging VC managers should approach family office fundraising, from targeting research to the objections that actually kill a pitch.
Family Office Size Predicts Operating Style: Small (under $500M AUM) and large (over $2B AUM) family offices run closer to a fixed-bucket endowment model; mid-sized offices in between are more entrepreneurial and faster to invest directly.
Generation Signals Risk Appetite: It maps three generational stages, a G1 patriarch or matriarch in control, a risk-averse G2 (children of G1) in consolidation mode, and a G3 more open to experimenting, a quick read on how receptive a family office will be.
Three Questions Decide the Pitch: Could they hand you their money, sail away for three years, and trust it was cared for? Why pick you over a16z or Sequoia? And what do you believe about the future that most investors don't?
✈️ KEY TAKEAWAYS
For emerging GPs, family offices are evaluating the person as much as the fund, and generational stage can predict receptivity before a single meeting happens. The track record objection is worth pre-empting by separating which wins were personal judgment calls from which depended on the firm's existing infrastructure.
That’s it for today!
Stay driven,
Andre
PS: Join the DDVC Investor Summit at Bits & Pretzels virtually or physically in Munich during Oktoberfest Sept 28-30
