👋 Hi, I’m Andre and welcome to my newsletter Data Driven VC which is all about becoming a better investor with Data & AI.
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Welcome to another Data Driven VC “Insights” episode where we cover the most interesting research & reports about startups and VC from the past week.
Why Habit Formation Matters More Than PMF in the Early Days
Grant Lee argues that Product-Market Fit shows up too late to guide early decisions, and founders should focus earlier on whether they are building a recurring habit or a one-off utility. The article reframes growth around frequency of pain, friction reduction, and habit loops rather than validation metrics alone.
50+ Daily Coordination Events and 57% Preference Shift: Lee points to Slack as an example of habit formation driven by frequency, where the need to coordinate with a team can happen 50+ times a day. He cites data showing that by 2022, 57% of workers preferred messaging apps over email for internal communication, signaling a clear habit shift tied to recurring problems.
40-Year-Old Tools and 90% Wasted Effort: Using Gamma as a case study, Lee explains that knowledge workers face the need to communicate complex ideas weekly or daily, yet rely on a 40-year-old tool. He highlights that tools like PowerPoint force users to spend roughly 90% of their time on formatting and only 10% on the actual message, creating unnecessary friction.
First 30 Seconds and 53-60% Drop-Off: The post emphasizes onboarding as a critical investment moment, noting that average 30-second retention across apps sits around 53-60%. Lee argues that getting users to create or customize something immediately builds switching costs and accelerates habit formation.
✈️ KEY TAKEAWAYS
Lee’s core insight is that durable products are built by owning a recurring moment, not just solving a problem once. By matching solution speed to problem frequency, reducing friction early, and designing for habit loops, founders can build products that stick even as markets change.
What’s Actually Working for AI in B2B GTM
The latest edition of Growth Unhinged by Kyle Poyar summarizes findings from interviews with 30 GTM leaders to identify where AI is delivering measurable impact in go-to-market teams. The piece contrasts teams seeing little return from AI with a smaller group using it to drive pipeline, conversion, and efficiency.
53% Seeing Little to No AI Impact: The article opens with data showing that 53% of GTM leaders report minimal impact from AI so far. Poyar explains that top performers stand out by connecting internal data and context directly to AI assistants, reducing dashboard hopping and turning insights into immediate actions.
30 Experts and 40 AI x GTM Use Cases: Kyle Poyar and Maja Voje interviewed 30 GTM practitioners and distilled their insights into 40 practical AI workflows. These use cases span four areas: content creation, growth and product marketing, prospecting, and sales engagement, with most relying on general-purpose LLMs rather than custom engineering.
1,600 Pages and 200%+ Pipeline Growth Examples: The article highlights concrete outcomes from advanced teams, including 1,600 AI-generated competitor pages in two months and AI engines contributing roughly 25% of total pipeline. Other examples show 50%+ email open rates and more than 200% quarter-over-quarter pipeline growth driven by AI-powered outbound systems.
✈️ KEY TAKEAWAYS
The article shows that AI advantage in GTM is less about tools and more about integration. Teams that feed real internal context into AI and apply it to specific GTM workflows are pulling far ahead of peers still experimenting at the surface level.
Where Does the Money Flow?
Peter Walker analyzes 1,263 US seed rounds raised on Carta to show how valuations and round sizes differ by industry. The visual maps median post-money valuations against median round sizes, with bubble size reflecting deal volume.
1,263 Seed Rounds and $3.4M Median Raise: The dataset covers 1,263 seed rounds from US startups, including priced rounds and SAFEs between $2M and $6M. Across sectors, the median amount raised sits around $3.4M, with many software categories clustering tightly around that benchmark.
$15.5M Median Valuation and AI Outperformance: Median post-money valuation across the map is $15.5M, but AI software stands out with both higher valuations and larger round sizes. Semiconductors also appear at the high end of valuations, signaling strong early-stage investor demand despite lower deal volume.
20% Dilution and Sector Clustering: Most bubbles align along an implied 20% dilution line, suggesting this remains a common expectation at seed. Deep tech sectors like hardware, biotech, and transport tend to raise slightly larger rounds at somewhat lower valuations, though the pattern is not fully consistent.
✈️ KEY TAKEAWAYS
The data shows a seed market where most software deals remain relatively standardized, while AI and semiconductors command premium pricing. Despite sector differences, dilution norms and median deal sizes still anchor founder and investor expectations.
Unicorns Are Staying Private Much Longer
Ilya Strebulaev presents new data on US unicorns and shows a sharp increase in how long recently founded companies remain private after reaching unicorn status. It frames this trend as a growing liquidity gap driven by capital market structure rather than company performance.
Nearly 800 Unicorns Still Private After 1 Year: The analysis shows that almost 800 unicorns founded between 2020 and 2025 remained private one year after becoming unicorns. This compares to 332 unicorns from the 2010-2019 cohort at the same point in time.
575 Unicorns Still Private After 3 Years: By year three, 575 unicorns from the 2020-2025 cohort were still private, nearly three times the 206 from the prior decade. The author highlights how dramatically the private timeline has stretched in just one generation of companies.
11 Unicorns From 1999-2009 at the 3-Year Mark: Only 11 unicorns founded between 1999 and 2009 remained private three years after reaching unicorn status. This comparison underscores how exceptional the current environment is relative to earlier venture cycles.
✈️ KEY TAKEAWAYS
Peter Walker shows a structural shift in venture exits, where abundant private capital and constrained public markets have extended private company lifespans. As more value stays locked inside private portfolios for longer, capital recycling becomes harder, increasing pressure on VC fund dynamics and future innovation funding.
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How Follow-On Capital Shapes VC Fund Performance
Odin published an analysis by Dan Gray exploring how venture funds structure reserve capital and whether follow-on investing improves overall fund performance. The analysis explores research, simulated portfolio data, and operational tradeoffs that emerging managers face when deciding how much capital to allocate to reserves.
4.0x Benchmark vs 2.7x-3.8x Returns With Reserves: Portfolio simulations referenced in the article show that a “no reserves” strategy can reach a 4.0x net multiple, while follow-on strategies often reduce returns. Reserves generating only 2x gross can drop net TVPI to 2.7x, while even strong 5x reserve performance still averages around 3.5x net, below the seed benchmark.
1:1 Reserve Ratio Doubles Fund Size and Reduces Diversification: A common rule suggests matching initial capital with equal reserve capital, effectively doubling fund size. The article highlights how this reduces portfolio breadth, limiting exposure to outliers and increasing risk for early-stage managers where diversification statistically improves the chances of hitting breakout outcomes.
30,602 Follow-On Decisions Reveal Sunk Cost Bias and Signaling Effects: Research covering 30,602 VC follow-on decisions between 2009 and 2019 shows investors are significantly more likely to reinvest when they have previously deployed capital or spent more time monitoring companies. The article also explains how skipping follow-ons can create negative signaling in later rounds, although this effect is weaker for emerging managers.
✈️ KEY TAKEAWAYS
Reserve strategies function as a performance magnifier that amplifies both strong and weak investment judgment. For emerging managers, prioritizing diversification and running small reserve experiments may produce better long-term outcomes than committing significant capital to follow-ons too early.
Thanks to Lea Winkler for her help with this post.
Stay driven,
Andre
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