👋 Hi, I’m Andre and welcome to my newsletter Data Driven VC which is all about becoming a better investor with data and AI.
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Welcome to another Data Driven VC “Insights” episode where we cover the most interesting research and reports about startups, GPs, LPs, AI & automation.
Where to Make Money in AI
Tomasz Tunguz argues in The Most Important Market in AI is the Middle that AI price competition is concentrated in the mid tier, where most multi-step business workloads run.
Frontier Demand Is Thinning: Anthropic's Fable 5.1, its most capable model, took only 3.7% of gateway spending in its first twelve days, and frontier models fell from 53% to 45% of large corporate token consumption between early August and September.
The Mid Tier Holds the Budget: A chart in the piece shows the mid tier claiming 40% of spend and 30% of tokens, which Tunguz describes as demand shaped like a bell curve with a fat middle.
Prices Are Collapsing Beneath It: Open models now run most gateway token volume at an 86% discount to closed models, and large customers fine-tune open-weight models to cut costs further, e.g. Harvey cut cost per cell 55% against Sonnet 5 while scoring above Fable 5.
✈️ KEY TAKEAWAYS
If demand keeps shifting toward cheaper models, as Tunguz suggests it may, the same work keeps getting cheaper to run. For VC firms building their own AI workflows, that means defaulting to mid-tier or open models for high-volume tasks like deal screening and research summaries, and reserving frontier models for the few steps where the extra quality clearly pays off.
Supervoting: Ten Votes to Your One
Stanford's Ilya Strebulaev explains in Ten Votes to Your One how supervoting lets founders control companies they barely own, and where that control stops.
Votes Can Detach From Ownership: Before Figma's IPO, Dylan Field owned roughly 9% of the equity yet held just over half the voting power, through 15-vote Class B shares and a proxy from his departed co-founder.
Control Has Hard Limits: Strebulaev notes supervoting delivers no board seats, cannot override protective provisions, and is irrelevant in any vote requiring a majority of preferred, so it mainly shields founders from being outvoted by common holders.
How Fast Extra Votes Add Up: In Strebulaev's illustrative SoftMet example, a Series C owning 7.7% of the company goes from 9.1% of the votes to exactly half at ten votes per share, enough to block any decision but not pass one alone.
✈️ KEY TAKEAWAYS
Strebulaev advises founders to rarely ask for supervoting, since it signals an expected shareholder conflict and can cost valuation. Voting proxies from departing co-founders are the cheaper route, and investors increasingly add sunset clauses that remove extra votes after a set period or the founder's exit.
New Fund Performance Data
Carta's Hamza Shad shared the latest fund performance data, which tracks median net IRR by fund age for every vintage since 2017. Late-2010s funds have been falling steadily from the highs they reached around 2021.
Late-2010s Funds Are Giving Back Gains: Median net IRR for 2017 funds stood at 22.0% around 18 quarters in and has since fallen to 9.3%, while 2018 funds dropped from 22.3% to 6.3%.
All Three Peaked Around 2021: The 2017, 2018 and 2019 vintages reached their highest median IRRs roughly 18, 14 and 10 quarters in, which for each lands around 2021, the height of the valuation boom.
Younger Vintages Never Got the Boost: At the same age, 2021 funds sit at a 1.6% median IRR after 18 quarters and 2022 funds at 4.4% after 14, against 22.0% and 22.3% for 2017 and 2018 funds.
✈️ KEY TAKEAWAYS
The slide is not a final verdict: a fund's IRR keeps moving with every new valuation and exit, so these vintages can still recover before they wind down. The bigger question sits with 2021 and 2022 funds, which are starting from single-digit IRRs well behind where late-2010s funds stood at the same age.
The Emerging Manager Paradox
Team8 partner Aaron Dubin published The Emerging Manager Paradox, a report asking why first-time funds attract so little institutional capital despite a long record of outperformance.
Great Investors Start as Operators: Of nearly 50 investors who appeared on the Midas List seven or more times between 2016 and 2025, almost 70% had founder or operator experience, and only about 20% rose purely through career VC.
First-Time Funds Have Outperformed: Across 2000-2022 vintages, Preqin data shows first-time VC funds beating established managers in many cohorts, with a median net IRR premium of up to 15% in certain vintages.
Yet LPs Wait for Proof: Diligence and monitoring cost an LP about the same whatever the check size, and proof now means DPI, disciplined portfolio construction and institutional-grade reporting, which a first-time fund with unrealized holdings rarely shows.
✈️ KEY TAKEAWAYS
Team8 argues that waiting has a price: by the time a new manager looks safe to back, its fund is often already full. It suggests standardized fund admin, spreading bets across several new managers, and investing before a fund formally launches so performance signals arrive sooner.
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AI Roll-Up Strategy for VCs
Greg Isenberg published a playbook on AI roll-ups, arguing that buying services firms from retiring owners and rebuilding delivery with AI agents can roughly triple a firm's profits.
Margin Expansion Is the Thesis: Traditional services firms run roughly 5-10% EBITDA margins, and the roll-up thesis holds that agent-rebuilt delivery can reach 30-40% on the same clients and invoices, lifting profit 3-4x.
Buying Margins at a Discount: Buyers pay low multiples for service businesses because they assume the margins can't improve, Isenberg argues, so raising margins makes the business worth more. General Catalyst has put more than $750M of a $1.5B allocation behind this strategy.
Scale Isn't Required: Isenberg illustrates how smaller players can run the same playbook without building a platform first, starting with one firm on off-the-shelf AI models and staying close to each acquisition where big funds rely on hired managers.
✈️ KEY TAKEAWAYS
For smaller VCs weighing roll-ups, the biggest risk Isenberg flags is buying firms faster than you can integrate them. Owners often prefer selling to a person over a fund, and paying part of the price in shares, as General Catalyst does with about 30%, gives the seller a reason to stay and hand over their client relationships.
VC Tool Budgets Catch Up to Engineers
Our 2026 DDVC Landscape Report covered how firms split budget between engineering staff and data, tools & tokens. The ratio has moved from roughly 2:1 in 2025 to near parity in 2026.
Small Funds Now Spend More on Tools: Funds under $100M report an estimated average of $45K on data, tools & tokens against $40K on engineering staff, making them the only cohort where tooling outspends people.
Mid-Sized Funds Sit Near Parity: Funds of $100M-$500M spend $132K on tooling versus $153K on engineering HR, and $500M-$1B funds report an exact tie at $235K on each side of the budget.
The Largest Funds Still Lean on People: $1B+ funds report $341K on engineering HR against $247K on tooling, roughly 1.4:1. That remains well below the approximately 2:1 ratio DDVC measured across funds in 2025.
✈️ KEY TAKEAWAYS
For GPs building data-driven platforms, spend on data, tools & tokens is now close to what they pay the engineers who use them, so it needs its own budget line and ROI tracking. The open question for 2027 is whether tooling spend overtakes engineering pay at larger funds too, as it already has for funds under $100M.
That’s it for today!
Stay driven,
Andre
