👋 Hi, I’m Andre and welcome to my newsletter Data Driven VC which is all about becoming a better investor with Data & AI.
Two highlights of the week:
Join our live webinar with Atomico, Bain Capital Ventures, and Vestberry this Wednesday to learn how top investors leverage AI for portfolio insights and value creation
Get 20% discount across all our paid subscriptions until Friday night
Brought to you by Foresight - Unify and talk to your data
Private market data was disconnected, which made getting a single view of a company, a fund, or a portfolio challenging. Foresight was the first to use AI to connect your data - fund accounting, cap tables, CRM, KPIs and 3rd party - which revolutionized sourcing, diligence, and portfolio management.
Now we’re thrilled to unveil yet another first: letting you talk with your unified data through AI. Learn what you can do here:
Welcome to another Data Driven VC “Insights” episode where we cover the most interesting startup research & reports from the past two weeks.
AI Native Companies Allocate More Equity, Faster
Matt Schulman introduces an analysis comparing annual net equity burn across AI native companies, non-AI tech companies, and non-tech firms. It highlights how aggressive hiring and larger equity packages push AI companies’ burn significantly higher. The author uses Pave’s dataset of over 8,500 private companies to illustrate how the competition for technical talent shapes equity strategy.
AI Native Company Medians: AI native tech companies have a median total equity burn of 4.2 percent and 2.1 percent from new-hire grants.
Non AI Tech Company Medians: Non AI tech companies show lower medians at 2.8 percent total and 1.3 percent for new-hire grants.
Sample Composition: The dataset includes 8,500 plus companies with 66 percent of the Forbes Top AI 50, covering examples from OpenAI and Mistral to Stripe and Plaid.
✈️ KEY TAKEAWAYS
Equity burn rates are materially higher in AI native companies because they hire faster and issue larger grants to attract technical talent, creating a widening structural gap compared to the rest of tech.
When Down Rounds Become the Better Option
Peter Walker examines why down rounds, while painful, are often a healthier alternative to raising at inflated valuations with heavy investor protections attached. His post highlights how common valuation resets have become and why they are not necessarily a sign that a company is failing.
Down Round Frequency: Carta data shows that around 20% of all startup fundraises in recent years were down rounds, and nearly 40% of Series D deals last year fell into this category.
Tradeoffs in Flat Rounds: Flat valuations often come with complex investor terms such as participating preferred stock, cumulative dividends, or high liquidation preferences, which can significantly reduce exit value for founders and employees.
Historical Precedent: Major companies like Facebook, Airbnb, and Ramp all went through down rounds, using them to reset expectations and continue building momentum.
✈️ KEY TAKEAWAYS
Down rounds are difficult but increasingly common. They can be a more founder friendly option than flat rounds with restrictive terms and can ultimately position companies for healthier long term growth if handled strategically.
Upgrade your subscription to access our premium content & join the Data Driven VC community
Why Most Founders Pitch Wrong
According to Vijay Rajendran, many founders still treat fundraising as a volume-first exercise, which leads to wasted effort and weak outcomes. The article by Todd Gagne presents his structured alternative that relies on targeted outreach, memorable storytelling, and disciplined process management.
Storytelling Framework: Founders often overwhelm investors with data. Rajendran suggests using real customer stories that illustrate a before and after, which increases memorability.
Investor Targeting: Instead of pitching 200 investors, the author recommends researching thesis fit, building relationships early, and running a six to twelve week process with a focus on high quality meetings.
Four Pillars: Rajendran breaks the fundraising motion into four phases: storytelling, organizing materials, targeting investors based on fit, and actively managing the closing phase.
✈️ KEY TAKEAWAYS
Successful fundraising depends more on process design than network size. A focused approach grounded in storytelling, preparation, and targeted outreach leads to stronger investor alignment and better long term outcomes.
AI Agents Are Quietly Transforming Modern GTM Teams
Kyle Poyar dives into how AI agents can supercharge GTM teams with real, production-grade workflows, not just shiny proof-of-concepts. He uses the example of SafetyCulture to show how agentic AI can enrich leads, automate sales outreach, personalize lifecycle messaging, and build a unified AI-first interface across CRM and data systems.
Agent-Driven Lead Enrichment: SafetyCulture’s AI agent calls multiple data providers in parallel, verifies data, and synthesizes a clean, high-fit lead profile, cutting manual research and driving near -100 % data coverage.
AI Auto-BDR for Outbound: An agent handles inbound leads: it fetches context from Salesforce and HubSpot, drafts intelligent outreach, and books meetings, delivering a 3× boost in meeting booking.
Lifecycle Personalization and Feature Adoption: Another agent uses retrieval-augmented generation and usage data to recommend new features, generating thousands of personalized message variants and boosting feature adoption by 10%.
✈️ KEY TAKEAWAYS
AI agents are not just shiny automation: when built with care, they can dramatically increase GTM efficiency by enriching data, scaling outreach, and tailoring customer touchpoints. The secret sauce lies in combining data hygiene, agent orchestration, and human oversight to build high-leverage, production-ready workflows.
Share this article with others who might benefit.
How Founders Can Cut Through The Noise in AI
Jennifer Neundorfer (January Ventures) argues that in today’s overcrowded AI space, the startups that catch her eye aren’t those tinkering with “10x improvements”, but those defining completely new behaviors, workflows, or business models. She urges founders to clearly articulate what makes them different from dozens of lookalike AI startups, and lean into being category creators. She also warns of an upcoming market reset, saying the biggest winners will be the teams who build not just for now, but for tomorrow.
Stand Out By Building New Workflows, Not Just Better Features: Neundorfer is most excited about founders who use AI to create bold shifts, not marginal upgrades. She favors companies that reinvent how work is done or how users behave, rather than simply scaling what already exists.
Invest in Narrative, Not the Noise: As AI fatigue sets in, she stresses that differentiation is not just about the product, it’s about the story and the team. Founders must clearly communicate why their vision, team, and execution are different from dozens of other AI startups.
Prepare for a Market Correction: Neundorfer predicts a cooling or correction in the AI investing landscape. She advises founders to stay focused on long-term value, build resilient business models, and deeply understand customer needs.
✈️ KEY TAKEAWAYS
In a saturated AI market, Neundorfer argues that only bold, category-defining startups will thrive. Founders need to articulate a differentiated vision and team, and build for the future, not just the hype. Her advice is especially relevant now, as she sees a likely market reset on the horizon and believes resilient, mission-driven companies will prevail.
Vintage Year Matters More Than Quartile Labels in VC
John Rikhtegar’s examination challenges the industry’s obsession with “top quartile” branding and shows that the same quartile label can mean very different things depending on the vintage year. By looking at 24 years of TVPI and DPI data, the author illustrates how dispersion across vintages makes quartile rankings far less meaningful than most investors assume.
Vintage Dispersion: Across 24 vintages from 1999 to 2022, the bottom quartile of “top quartile” TVPI sits at 1.4x, yet 13 vintages delivered higher medians and 6 vintages posted higher bottom quartiles.
Recovery Year Performance: Post-correction years such as 2010 and 2011 rank in the top decile across all TVPI and DPI quartiles, including bottom quartiles above the 0.9x DPI floor of “top quartile” funds.
Vintage Exposure: Cycles dominate outcomes, with several recovery-year vintages between 2010 and 2016 showing top quartile TVPIs between 3.09x and 4.04x and bottom quartiles between 1.37x and 1.73x.
✈️ KEY TAKEAWAYS
Vintage diversification drives long-term venture performance because the strongest vintages follow market corrections, and relying on quartile labels alone obscures how much outcomes vary across cycles.
Thanks to Lea Winkler for her help with this post.
Stay driven,
Andre
PS: Join our free webinar with Atomico, Bain Capital Ventures, and Vestberry to discover how top funds leverage AI for portfolio insights and value creation this Wedn 26th 5-6pm CET here
