💥VC Ranking Flaws, Borderless… | Data Driven VC Summary

1. Key Themes

Theme 1: AI Intelligence Spend Is Being Over-Provisioned at the Enterprise Level

The gap between AI token consumption and actual business value returned is widening, creating an investment opportunity in efficiency layers rather than frontier models.

"OpenAI reports that average reasoning-token consumption per enterprise organization rose roughly 320-fold over the past year, yet PwC's survey of 4,454 CEOs found 56% have not yet seen a significant financial benefit from AI."

"Amazon researchers estimate reasoning models generate 7 to 10 times more tokens than necessary on simple tasks, giving the 'overthinking' problem a concrete measurement rather than just an anecdote."

"Watch for internal routing layers and hybrid architectures to become the real infrastructure investment theme, ahead of frontier model access itself."

Theme 2: Tender Offers Are Becoming a Structural Private-Market Liquidity Mechanism

Rising volume, deal count, and fill rates suggest tender offers are no longer a niche escape valve — they are becoming a primary liquidity channel as companies stay private longer.

"Carta administered 71 tender offers in H1 2026, up 34% from the 53 tenders administered in H1 2025."

"Total transacted volume reached $3 billion, three times the $1.0 billion moved in H1 2025 and above the previous H1 record of $2.6 billion set in 2022."

"Close to six in ten eligible sellers took part, and more than 90% of the shares buyers sought to purchase were successfully sold."

Theme 3: Borderless Founders with Dual-Market Positioning Are a Differentiated Source of Alpha

International founders bring home-market customer access, elite talent pools, and diaspora networks that US-only sourcing misses entirely.

"44% of portfolio companies in a16z's Apps early-stage investments feature international founders, split evenly between US and abroad HQs."

"One seed company hired six technical leaders who had each been CTO at a domestic unicorn, a pool priced far below Bay Area equivalents and invisible to US-only sourcing."

"Diaspora networks are turning into a repeatable sourcing and customer-intro channel, not just a nice narrative."

Theme 4: VC Brand and Network Are Distinct Assets That Decay at Different Rates

A firm's brand can continue compounding from past wins while its early-stage sourcing network quietly atrophies, causing an unintentional drift toward later-stage investing.

"As GPs age out or lose relevance, a firm's network can weaken even as its brand strengthens on the back of well-known early wins."

"A strong brand helps a firm win competitive deals but doesn't help it find day-zero founders, which a fading network makes harder to source."

"Firms with strong brand and weaker networks gravitate toward later-stage investing, where brand recognition substitutes for early relationship access."

Theme 5: Standard VC Rankings Measure Scale, Not Capital Efficiency

The most widely cited VC leaderboards implicitly reward AUM and brand, not DPI, and reshuffling for capital efficiency produces radically different results.

"Applying a weighted moving average of fund size to Ilya Strebulaev's ranking moved Union Square Ventures up 37 places and First Round up 36 places, among other double-digit swings."

"The exercise only reshuffles the original list of 50 firms; Gray notes a true top 10 by DPI would likely surface many lesser-known micro and seed funds instead."

2. Contrarian Perspectives

The AI concentration narrative is a top-of-funnel illusion, not a structural market reality. The popular perception that a handful of mega-VCs are co-investing in the same AI companies and dominating deal flow is statistically misleading. The largest deals are visible and noisy, but the underlying market is still fragmented.

"Across roughly 1,600 startups that raised funding since 2023, only 167 shared five or more institutional investors with another company in the sample."

"Among companies backed by one of the ten most active investors, 74% have only one of those firms on their cap table. Just 6% have more than three."

"The most visible AI deals make VC look more concentrated than it is."

Frontier AI model access is not the real infrastructure moat — efficiency routing is. Conventional wisdom holds that winning access to the best models (GPT-5, Claude, Gemini) is the key enterprise AI advantage. The data suggests the opposite: enterprises that learn to route away from frontier models will outperform those that over-rely on them.

"Labs profit from selling more intelligence; enterprises profit from needing less of it."

"Intelligence per joule improved 18x in just 16 months, and the 27B-parameter Qwen3.8 already matches or beats Opus 4.6 Max on several coding and agentic benchmarks."

The best VC returns likely come from funds that never appear on "top 10" lists. Because public rankings reward AUM and brand recognition, the most capital-efficient funds — often micro and seed vehicles — are systematically excluded from the conversation.

"Most public VC rankings implicitly reward AUM and brand recognition rather than dollar-weighted returns."

"A true top 10 by DPI would likely surface many lesser-known micro and seed funds instead."

3. Companies Identified

OpenAI Description: Frontier AI model provider Why mentioned: Used as data source on enterprise token consumption growth — a 320x increase in reasoning tokens per org over one year, with no commensurate financial benefit reported by enterprise CEOs.

"OpenAI reports that average reasoning-token consumption per enterprise organization rose roughly 320-fold over the past year."

Carta Description: Cap table management and private market data platform Why mentioned: Source of the tender offer data showing 71 offers in H1 2026, $3B transacted, and record-level participation rates.

"Carta administered 71 tender offers in H1 2026, up 34% from the 53 tenders administered in H1 2025."

Exa Description: AI-native web search and data API Why mentioned: Newsletter sponsor; positioned as the tool for AI-forward VC firms to source companies by thesis and enrich targets programmatically.

"AI-forward firms use Exa to source companies by thesis, enrich target businesses programmatically, and monitor signals across the market in real time."

Union Square Ventures Description: Early-stage VC firm Why mentioned: Moved up 37 places in VC rankings when reweighted for capital efficiency rather than scale — one of the largest positive swings in the study.

"Applying a weighted moving average of fund size to Ilya Strebulaev's ranking moved Union Square Ventures up 37 places."

First Round Capital Description: Seed-stage VC firm Why mentioned: Rose 36 places under DPI-weighted ranking methodology, illustrating how capital efficiency metrics favor smaller, earlier-stage funds.

"First Round up 36 places, among other double-digit swings."

SV Angel Description: Early-stage angel fund Why mentioned: Emerged as the top-ranked firm under the capital-efficiency-reweighted methodology.

"The reweighted top 10 is led by SV Angel and Ribbit Capital."

Ribbit Capital Description: Fintech-focused VC Why mentioned: Co-leader of the reweighted top 10, alongside SV Angel, displacing scale-driven incumbents from the original list.

"The reweighted top 10 is led by SV Angel and Ribbit Capital, displacing several scale-driven names from the original 'tier 1' list."

H&M Description: Global fashion retailer Why mentioned: Cited as a named early customer win for a Stockholm-based seed company, demonstrating the home-market traction advantage of international founders.

"A Stockholm seed company closed listed H&M and multi-billion-dollar Stena Metall as its first two customers."

Stena Metall Description: Swedish multi-billion-dollar industrial conglomerate Why mentioned: Second named early customer for the same Stockholm seed company — illustrating the quality of home-market logos that international founders can access at seed stage.

"A Stockholm seed company closed listed H&M and multi-billion-dollar Stena Metall as its first two customers."

Salesforce Description: Enterprise CRM and cloud platform Why mentioned: A Spanish startup converted a shared university relationship with Salesforce's CRO into its first major enterprise contract — demonstrating diaspora and shared-network effects.

"A Spanish startup converted a shared-university tie to Salesforce's CRO into its first major contract."

4. People Identified

Jaya Gupta Description: Partner/investor at Foundation Capital Why mentioned: Author of the analysis arguing that most enterprise AI workloads are over-provisioned for frontier-level intelligence, backed by token consumption vs. CEO benefit data.

"Jaya Gupta at Foundation Capital argues that most enterprise AI workloads don't need frontier-level intelligence, and that the gap between lab incentives and enterprise outcomes is now measurable in the data."

Dan Gray Description: Analyst/writer at Odin Why mentioned: Conducted the VC ranking stress-test, reweighting Strebulaev's methodology by capital efficiency to reveal how much "top 10" lists depend on scoring approach.

"Dan Gray at Odin stress-tests a popular VC ranking methodology by reweighting for capital efficiency, revealing how much 'top 10' lists depend on how they're scored."

Hamza Shad Description: Analyst at Carta Why mentioned: Author of Carta's mid-year tender offer report showing the structural growth of private-market liquidity mechanisms.

"Hamza Shad at Carta shares a mid-year update on tender offer activity, showing that private-market liquidity mechanisms are scaling well beyond prior cycles."

Gabriel Vasquez Description: Investor at a16z Why mentioned: Authored a16z's playbook for backing international "borderless" founders, including the 44% international founder stat and the talent arbitrage thesis.

"Gabriel Vasquez, at a16z, lays out their playbook for backing international founders who keep one foot in their home market and one in Silicon Valley."

Ben Casnocha Description: Author, former Chief of Staff at LinkedIn, VC ecosystem thinker Why mentioned: Author of the framework distinguishing VC brand from VC network as separately decaying assets, explaining the structural drift of established firms toward later-stage investing.

"Ben Casnocha explains why a firm's brand and network can move in opposite directions over its lifecycle, and what that does to stage focus."

Ilya Strebulaev Description: Stanford finance professor and VC researcher Why mentioned: Creator of the original VC ranking methodology that Dan Gray reweighted for capital efficiency — his list of 50 firms served as the baseline for the analysis.

"Applying a weighted moving average of fund size to Ilya Strebulaev's ranking moved Union Square Ventures up 37 places."

Andre Retterath Description: Author of Data Driven VC newsletter; VC investor Why mentioned: Author and curator of the newsletter; focuses on applying data and AI to improve venture investing.

"Hi, I'm Andre and welcome to my newsletter Data Driven VC which is all about becoming a better investor with data and AI."

5. Operating Insights

Build hybrid AI architectures that route tasks to appropriately-sized models rather than defaulting to frontier intelligence. The cost and efficiency gap between frontier and smaller models is already measurable and compressing fast. Operators and product builders who implement intelligent routing layers will achieve the same or better outputs at a fraction of the compute cost — and this arbitrage is growing, not shrinking.

"Amazon researchers estimate reasoning models generate 7 to 10 times more tokens than necessary on simple tasks."

"Intelligence per joule improved 18x in just 16 months, and the 27B-parameter Qwen3.8 already matches or beats Opus 4.6 Max on several coding and agentic benchmarks."

Formalize diaspora and home-market relationships as a sourcing and business development channel — not just as a narrative. International founders can access enterprise customers and senior talent (e.g., former unicorn CTOs) through networks that are invisible to US-only operators and investors. These relationships compound early and are not yet priced into deal competition.

"Non-US unicorn alumni graphs and diaspora return-intent are indexable well before the market catches on."

"Funds should formalize relationships with home-market luminaries early, since those ties compound into deal flow and enterprise customers well before traction alone would justify them."

Treat network-building and brand-building as separate, parallel investments with distinct maintenance costs. For VC firms and even founder-operators who rely on relationship-driven deal flow or customer introductions, brand can mask a quietly decaying active network. Explicitly budgeting time and resources for live relationship cultivation — separate from content or PR — is required to avoid an unintended drift toward later, more competitive, more crowded stages or markets.

"Emerging managers should treat active network-building as its own ongoing investment, separate from brand-building, or risk being pushed up-market whether the GPs intend it or not."

6. Overlooked Insights

Tender offer fill rates signal a structural buyer-seller equilibrium, not just increased volume. The article focuses on headline deal count and dollar volume, but the near-complete fill rate is arguably the more significant signal — it means supply and demand are meeting efficiently, suggesting a maturing secondary market rather than a temporary liquidity spike driven by one side of the trade.

"More than 90% of the shares buyers sought to purchase were successfully sold."

This level of market clearing efficiency has compounding implications for employee retention design, LP liquidity planning, and secondary fund strategies that the headline numbers alone do not capture.

VC herding concentration is historically cyclical, not permanently structural. The article briefly notes that similar co-investment concentration appeared in 2016 around Uber, Airbnb, Snap, and WeWork — companies whose subsequent outcomes varied dramatically. This historical parallel is mentioned in passing but carries a significant warning about vintage-risk for the current cohort of mega-backed AI companies.

"Similar concentration occurred in 2016 around Uber, Airbnb, Snap and WeWork."

Investors treating today's top-five AI deal overlap as a sign of market consensus quality should note that the last comparable concentration episode included WeWork.