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Welcome to another Data Driven VC āInsightsā episode where we cover the most interesting research & reports about startups and VC from the past week.
The Billion-Dollar Founder Study: 20 Years of Exit Data
Andy Chen & Amy Lin (whoāll speak about this research at the Virtual DDVC Summit in less than 24 hours) at Outcast Ventures published a comprehensive study of every U.S. tech company that went public or was acquired for $1B+ over the past 20 years, manually reviewing 2,000+ LinkedIn profiles to reconstruct the founding stories behind each exit.
Prior Colleagues Underperform Strangers: Founding teams who had worked together before achieved a median exit valuation of $2.3B, compared to $2.9B for teams with no prior work history - a 21% gap that directly contradicts the conventional wisdom that shared history de-risks a partnership.
Prior Exit Experience Nearly Doubles IPO Outcomes: Among IPO outcomes in the dataset, teams where at least one founder had previously built and exited a company reached a median valuation of $5B, versus $2.6B for teams without prior exits. CEO startup experience alone added a 41% premium ($3.8B vs. $2.7B median at IPO).
Solo Founders Nearly Disappear at the Top: While 82% of all $1B+ exits came from teams of 2+, among the top 5% of exits (valued between $24B and $98B), only one was led by a solo founder. Solo-founded companies also take roughly three years longer to reach a liquidity event.

āļø KEY TAKEAWAYS
The data reframes how VCs should evaluate founding teams: prior relationships are largely irrelevant, but prior exits are among the strongest predictors of scale. The 90%+ premium for teams with at least one previous exit suggests that founder pattern recognition ā not school pedigree or co-founder familiarity ā is the primary variable driving the largest outcomes. For investors, this means prioritizing repeat founders or those with deep operator experience over conventional signaling markers like alma mater or co-founder chemistry.

State of the Product Job Market in Early 2026: The Most Optimistic Reading Yet
Lenny Rachitsky, in collaboration with TrueUp (tracking 9,000+ tech companies globally), published the fourth edition of his biannual State of the Product Job Market, which turns out to be the most optimistic report to date ā despite ongoing AI and layoff headlines.
7,300+ Open PM Roles at a 3-Year High: Open product management roles at tech companies globally now exceed 7,300 and are already up nearly 20% since the start of 2026 ā sitting 75% above the trough hit in early 2023 and at the highest level since 2022.
67,000+ Engineering Openings with No AI Slowdown (Yet): There are more than 67,000 open engineering roles globally (26,000 in the U.S. alone), and the rate of increase has been accelerating since the start of the year. Recruiter demand is almost back to 2022 peak levels ā a leading indicator that sustained hiring demand is real.
AI Roles Are Hockey-Sticking: AI-specific roles ā defined as all positions at AI-native companies plus AI-focused roles at traditional tech firms ā are growing at an accelerating rate well above any other job category. Design roles, by contrast, have plateaued entirely.

āļø KEY TAKEAWAYS
Counter to the dominant "AI will eliminate tech jobs" narrative, the data shows the opposite is happening in the near term: AI is generating net new demand for product and engineering talent, particularly at AI-native companies. For VCs, this signals that portfolio companies recruiting PM and engineering talent are competing in an increasingly tight market. The plateau in design roles, combined with the explosion in AI PM demand, also hints at a structural shift in how product teams are being assembled ā leaning more toward technical and growth profiles.

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30 PLG Tactics Working Right Now: A Data-Driven Look Across Channels
Kyle Poyar at Growth Unhinged published a data-driven survey of what's actually working across PLG tactics, marketing channels, pricing, and sales touchpoints ā synthesizing findings from 200+ software companies and surveying GTM leaders on the top tactics driving growth in early 2026.
Outbound and Partner/Ecosystem Lead All Growth Categories: Intent-based outbound and ABM each account for 20% of the most impactful growth experiments reported by GTM leaders, with AI now enabling far more precise account targeting. Partner and ecosystem tactics, including B2B influencers and co-marketing, tied at the same level.
AI Discovery Is Emerging as a High-Intent Channel: AI discovery now accounts for 12.7% of high-intent leads at companies like Docebo (up 429% year-on-year) and 10% of signups at Webflow (up 4x year-on-year). Late-funnel SEO is proving to be the foundation for strong answer engine optimization (AEO).
Events and Community Drive 16% of Top Growth Experiments: Despite (or because of) the digital-first environment, in-person and community-led tactics ranked third overall among GTM leaders' highest-impact experiments ā ahead of product-led growth motions (10%), paid advertising (6%), and product launches (8%).

āļø KEY TAKEAWAYS
The data challenges the assumption that AI tools have automated outbound into irrelevance ā they've actually made it more effective by improving signal quality. For VCs evaluating GTM strategies at portfolio companies, the emergence of AI discovery as a high-intent channel represents a new acquisition lever that isn't yet saturated. The persistence of community and events in the top tier also suggests that human-first touchpoints continue to matter most at the conversion stage, even as top-of-funnel increasingly shifts to AI-mediated search.

How Long Does It Actually Take for VC Funds to Return Capital?
Peter Walker at Carta published an analysis of VC fund performance across 2,906 U.S. venture funds spanning vintage years 2017 through 2025, offering the most comprehensive current snapshot of when LPs actually get paid back.
Most LPs in Recent Vintage Funds Are Still Waiting: More than half of 2020-vintage funds have now begun generating DPI, but for 2021 and 2022 vintages that rate sits at roughly one-third and one-quarter respectively. The median DPI for 2017-vintage funds, the oldest in the dataset, was just 0.27x, meaning even the most mature funds have returned only a fraction of invested capital.
The 3x Return Threshold Remains Rare and Power-Law-Distributed: In the 2019 vintage, the 90th-percentile TVPI was 3.01x while the median sat at just 1.33x. The gap between top-decile and median performance is far larger than any other performance band and widens further back in time.
Secondary Markets Are Filling the Liquidity Gap: VC secondary transaction volume reached an estimated $61.1B in the 12 months from July 2024 through June 2025, surpassing VC-backed IPO proceeds of $58.8B over the same period. Active VC funds may ultimately take 15 to 20 years to fully return capital to LPs in the current extended-hold environment.

āļø KEY TAKEAWAYS
The data crystallizes a structural problem in the VC asset class: most LPs in recent vintages won't see meaningful cash returns for at least a decade, and the secondary market has emerged as the only viable near-term liquidity mechanism. For emerging managers trying to raise new funds, the LP liquidity crunch is a direct headwind. LPs who haven't received distributions from prior commitments simply have less capital to redeploy. The extreme right-tail distribution of TVPI also confirms what practitioners already knew: the difference between top-decile and median VC performance is not incremental, it is structural.

Stay driven,
Andre
PS: Last chance to join our Virtual DDVC Summit 2026 where 40+ expert speakers from Accel, Bessemer, NEA & more share their workflows, tool stacks, and discuss the latest insights about AI for VC
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