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Brought to you by Affinity - The State of AI Investing
AI is moving from concept to reality in private capital.
Hear from experts at BlackRock, OpenAI, and Affinity as they explore how AI is reshaping workflows, unlocking new insights, and redefining what’s possible for deal teams.
This session is built for investors looking to understand where AI can drive real value and how to assess readiness across their portfolio and the broader ecosystem.
Welcome to our monthly wrap-up episode where we cover July’s most relevant content at the intersection of startups, VC, data & AI.
Said differently: We curated a summer reading list for you👇
MULTIPLES SNAPSHOT📸
88% of you want me to continue with the Multiples Snapshot - so I trust the data & repeat ;) Below is a snapshot from July’s state of the market. Full episode with all tables and deep dives here.
Source: Multiples.vc
Source: Multiples.vc
Source: Multiples.vc
INTERESTING RESEARCH & REPORTS📈
SaaS CAC Payback Periods Hit Troubling Highs
Kyle Poyar spotlights new data showing just how inefficient SaaS go-to-market teams have become. He shares numbers from Jamin Ball’s Clouded Judgement, indicating that sales and marketing costs now take years to recover, even for top performers.
Q1 2024 Average: Gross margin-adjusted CAC payback hit 57 months. That means even strong public SaaS companies need nearly five years to break even on new customer acquisition costs.
Best vs. Worst Performers: Zero public companies had CAC payback under 12 months. Only five were under 24 months, while 24 firms reported payback above 100 months or negative net-new ARR.
12-Quarter Trend: The average CAC payback over the past 12 quarters sits at 41 months, showing this is a persistent problem not explained away by one bad quarter or macro factors.
✈️ KEY TAKEAWAYS
Public SaaS companies face severe GTM inefficiency, with CAC payback periods stretching multiple years. This may force a reckoning as companies can’t rely on legacy customer subsidies forever.
Seed Round Liquidation Preferences Stay Standard
Peter Walker’s LinkedIn post analyzes current norms in early-stage venture deals, showing that seed and Series A rounds overwhelmingly use 1× non-participating liquidation preferences. He notes this consistency as evidence that founders shouldn’t fear hidden investor-unfriendly terms at these stages.
1× Non-Participating Remains Standard: Seed and Series A deals almost universally feature 1× non-participating liquidation preferences, with no cumulative dividends, keeping founder and investor interests aligned.
Bridge Rounds Often Have Tougher Terms: Bridge or extension rounds can include higher liquidation preferences, reflecting the higher risk for investors when companies raise unplanned interim capital.
Later-Stage Rounds Diverge: More mature rounds often include investor-friendly terms such as participating preferences or higher multiples, but these are not standard at seed.
✈️ KEY TAKEAWAYS
Early-stage funding rounds maintain simple, founder-aligned terms. Seed deals with preferences above 1× non-participating should be seen as warning signs.
INSPIRING TECH IN VC CONTENT💡
How RA Capital’s Legal Team Uses AI to Scale Their Operations: Tools & Case Studies
RA Capital’s General Councel Sarah Reed shares here how their legal team handles a massive workload by treating AI as a “junior associate.” They use sandboxed versions of Claude and ChatGPT to draft contracts, research laws, and even produce first drafts of fund documents—faster and cheaper than junior lawyers. AI isn’t replacing humans but augmenting them, offering extra precedent examples, surfacing new insights, and speeding up repetitive tasks while lawyers focus on high-value work.
They also pair AI with tools like Glean, which helps interns instantly answer questions and ramp up quickly, and Smartsheet, which streamlines contract intake and tracking. Even though many AI tools are still “wrappers around ChatGPT,” RA Capital is training them now, betting on compounding gains. By blending AI-driven drafting, automation, and standardization, they’ve built a legal “factory” that keeps pace with one of the busiest investment firms in biotech.
A New VC Operating Model Is Emerging
A recent feature from Invezz reveals how AI is reshaping VC from sourcing to portfolio management, raising the question about the erosion of traditional mentorship models and relationship‑based vetting. The article argues that firms must intentionally decide whether to become AI-native or preserve human-led instincts—or both.
AI is making venture capital faster, leaner, and more automated. But it’s also hollowing out the structures that gave the industry its staying power.
Fewer junior hires means fewer apprentices. Founders are exiting earlier. Deals are being picked by models, not relationships.
That’s efficient, but it’s also brittle. The venture business was built on long-term trust, shared experiences, and human judgment.
Those things don’t show up in training data.
The industry now faces a choice. Some firms will become AI-native, treating venture like a throughput machine.
Others will double down on the human edge, like deep relationships, conviction calls, and hands-on support.
Most will try to do both. But the ones who succeed will be those who choose their model deliberately. Not just as a thesis, but as a way of working.
How AI Is Transforming VC in 2025
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