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Welcome to another Data Driven VC “Insights” episode where we cover the most interesting startup research & reports from the past two weeks.
Size of HR and Talent Teams Across Company Sizes
In his research, Matt Schulman examines current benchmarks for HR and Talent Acquisition team sizing, linking them to company size and structure. He uses recent market data to show how many full-time employees are typically supported by one HR team member.
38 FTEs per HR for Private Companies Under 1,000 Employees:
The analysis finds a median ratio of 38 full-time employees per HR team member in smaller private companies, which translates to roughly 13 HR and recruiting staff for a 500-person organization.32 to 33 FTEs per HR for Larger and Public Companies:
Private companies with more than 1,000 employees average 32 FTEs per HR team member, while public companies sit close at 33, suggesting tighter HR coverage as organizational complexity increases.59 FTEs at the 75th Percentile Shows Wide Variation:
At the upper end, smaller private companies reach up to 59 FTEs per HR team member, highlighting significant variance and showing how tooling, structure, and efficiency can stretch HR capacity.
✈️ KEY TAKEAWAYS
Across company types, HR and recruitment staffing ratios cluster between 32 and 38 employees per HR team member, but wide percentile ranges suggest there is no single ideal model. The article positions these benchmarks as a diagnostic tool, especially as AI adoption may push future HR teams toward leaner configurations.
Who Should Send Recruiting Messages
Aline Lerner analyzed nearly 8,000 recruiting messages from Hired’s early years to test whether outreach from founders performs better than messages from recruiters or engineers. After controlling for personalization and company effects, she shows that the sender matters far less than many founders assume.
7,818 Messages Across Early-Stage Hiring: The dataset covers 7,818 unpersonalized outreach messages sent to engineers across hundreds of companies, allowing for a clean comparison without personalization bias. Recruiters accounted for the majority of messages, followed by founders and engineers.
53% vs. 49% vs. 47% Response Rates: Recruiter-sent messages achieved a 53% response rate, while founders saw 49% and engineers 47%. The difference was statistically significant at first glance but largely disappeared after controlling for company brand, role, and compensation.
Tone Over Title Explains Most Outliers: High-performing recruiter messages stood out for enthusiasm and clarity about opportunity, not seniority or technical depth. Dryer, more tentative messages from non-recruiters underperformed despite coming from founders or engineers.
✈️ KEY TAKEAWAYS
The analysis suggests that who sends a recruiting message matters far less than how it is written, especially in unpersonalized outreach. Enthusiasm, clarity, and repetition at scale explain why experienced recruiters often outperform founders, not inherent credibility or authority.
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Financial Modelling Guide for Founders
Ruben Dominguez breaks down financial modeling into a practical framework founders can use to manage runway, plan growth, and prepare for fundraising. Instead of abstract theory, the article walks through concrete revenue, cost, and cash-flow drivers with examples that reflect how startups actually operate.
Revenue Drivers: Pricing, Funnels, Retention Metrics
The article explains how ASP, sales funnel conversion rates, CAC, and gross versus net retention determine revenue outcomes. It contrasts top-down targets like “$1M ARR” with bottom-up modeling based on MQL-to-SQL conversion, win rates, and churn, showing why combining both approaches produces more realistic forecasts.Cost Drivers: Fixed vs. Variable Burn Dynamics
Dominguez separates fixed costs such as salaries, G&A, and software from variable costs like cloud hosting, interchange fees, and customer support. He highlights that fixed costs scale in steps while variable costs scale with revenue, making unit economics and the CAC to LTV relationship central to avoiding growth that destroys cash.Non-Operating Drivers: Working Capital, Depreciation, Interest
Beyond revenue and costs, the guide emphasizes cash flow levers like payment terms, collection cycles, depreciation planning, and interest rates. These non-operating factors often determine whether a startup needs to raise capital earlier than expected, even when the P&L looks healthy.
✈️ KEY TAKEAWAYS
Strong financial models are built on a small set of operational drivers rather than high-level assumptions. By explicitly modeling revenue mechanics, cost structure, and cash-flow timing, founders gain clearer visibility into runway, fundraising needs, and the true economics of growth.
The New Time to $1B Revenue
In one of his recent posts, Peter Walker reflects on how extreme revenue acceleration in AI startups is reshaping expectations across the startup ecosystem. Using Cursor’s growth and valuation as a reference point, the author explains how this shift affects founders, investors, and funding dynamics more broadly.
$1M to $1B Revenue in 24 Months: This post highlights Cursor’s jump from $1M to $1B in revenue within two years as a historic outlier that is now influencing what investors perceive as exceptional growth.
$30B Valuation and 12x Jumps in One Year: It points to nearly $30B valuations and rapid multiple expansion as signals that revenue scale currently outweighs concerns around margins or profitability.
Few IPOs and Rising Late-Stage Retail Access: Peter Walker connects these valuations to a market with limited IPOs, which is driving more attention toward retail-style access to late-stage private companies.
✈️ KEY TAKEAWAYS
Extraordinary AI-driven growth has redefined what counts as great performance in startups, raising expectations across the market and squeezing attention away from slower narratives. The article argues this makes it more critical than ever for founders to carefully consider whether aligning with venture growth targets still makes sense for their companies.
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Make or Break the Company: When It’s Time to Change Vision and Direction
A new article by Wildfire Labs Substack uses well-known founder case studies to explore a recurring leadership tension: knowing when conviction saves the company and when it nearly destroys it. Through contrasting examples, it shows that the same behavior can be either decisive or disastrous depending on what is being questioned and when.
Airbnb 2009 vs. 2011 Decisions: In 2009, Airbnb had roughly 40 customers and $30,000 in debt, and listening to customer feedback led to a critical product change that unlocked growth. In 2011, despite strong pressure from investors and advisors to step aside as CEO during rapid scaling, Brian Chesky ignored the advice and stayed, which proved to be the right call.
Conviction Failures vs. Conviction Wins: Stewart Butterfield raised $17 million for Glitch but shut it down after noticing stronger demand for an internal tool, which became Slack. In contrast, Travis Kalanick’s unwavering belief that Uber’s mission justified any behavior ultimately led to his removal as CEO.
Stage-Specific Tradeoffs Over Time: Pre-PMF companies benefit from maximum flexibility except for the core insight, as shown by Instagram’s eight-week pivot from Burbn. Post-PMF companies need stronger conviction around positioning, while scaled companies like Amazon combine rigid values with flexibility across business lines.
✈️ KEY TAKEAWAYS
The article argues that successful founders are not defined by being consistently stubborn or flexible, but by being precise about where conviction is non-negotiable and where learning should override ego. Companies scale when founders protect their core thesis and values, while staying willing to change execution, tactics, and assumptions as evidence accumulates.
Growth Playbooks That Actually Work
Kyle Poyar summarizes insights from a survey of 130 practitioners on their most impactful growth experiments of 2025. It aggregates results across tactics to show where teams focused their efforts and what delivered measurable outcomes.
130 Responses Show 20% Outbound and 20% Partner-Led Growth: Outbound and ABM accounted for 20% of responses, matched by partner and ecosystem strategies, indicating continued reliance on established, execution-heavy channels.
16% Events and 12% Content Still Drive Results: Events and community represented 16% of successful experiments, while content and AI discovery followed at 12%, suggesting human connection and distribution still matter alongside automation.
10% PLG and 8% Launches Round Out the Mix: Product-led growth made up 10% of responses, with product launches at 8%, reinforcing that core product motions remain relevant but rarely dominate alone.
✈️ KEY TAKEAWAYS
The data shows that most effective growth experiments in 2025 relied on well-known tactics rather than novel ideas, with impact driven by strong execution. Despite widespread attention on AI, outbound, partnerships, and community-led efforts continue to deliver consistent results across teams.
Thanks to Lea Winkler for her help with this post.
Stay driven,
Andre
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