Investment Thesis
Sarah's investing worldview is built on a few interlocking beliefs: (1) The best consumer companies create compounding engagement loops where users do MORE over time, not less. She formalized this into the Hierarchy of Engagement framework: first get users to a 'core action,' then create 'accruing benefits' and 'mounting loss' to retain them, then achieve self-perpetuation through network effects. (2) For marketplaces, aggregate GMV is a vanity metric and a red herring. What matters is 'Happy GMV' — the portion of transactions where both buyer and seller had an experience good enough to drive retention. Happiness, not scale, is your moat. No matter how large an incumbent, they are vulnerable to a new entrant that makes buyers and sellers happier. (3) She prefers founders who start in a 'thimble' of a market — a small, constrained market adjacent to very large ones — and expand outward, rather than going after a big market from day one. (4) In AI, she believes the next wave of consumer AI will be social, not solo. AI avatars, chatbots, and social AI products will win over single-player tools. (5) For B2B AI, she coined 'sell work, not software' — rather than selling a 15% productivity improvement via software, AI startups should sell the completed work itself, delivering a 95% improvement. Incumbents are stuck selling software; startups can leapfrog them. (6) She believes markets have their own physics — 'you can't change those physics, regardless of how great the founder is.'
What Excites Them
Founders who deeply understand their user's daily workflow and show a willingness to do unscalable things to win. Products with natural retention mechanics that improve with usage — accruing benefits and mounting loss. Marketplace dynamics where supply and demand reinforce each other and tip toward winner-take-most outcomes. Consumer products that are 10x better AND cheaper than incumbents. Singularly unique founders running the company. Companies that start in small constrained markets adjacent to very large ones. She is much more interested in the thought process that got a founder to the metric they're focused on than the metric itself, because that reflects how they're running their company.
What They Pass On
Pure aggregation plays without defensibility. 'Uber for X' pitches without real marketplace dynamics. Products optimizing for vanity metrics (total GMV, MAUs) over meaningful engagement (Happy GMV). Solo AI tools without social/collaborative dynamics. Companies going after big markets from the beginning without a wedge. Distributed founding teams pre-product/market fit (she strongly advocates for co-location in the early days). AI wrapper companies without a path to enduring value or data moats.
How to Pitch
Lead with the engagement loop — show how users do more over time, not less. If it's a marketplace, talk about Happy GMV and minimum viable happiness, not aggregate GMV. If it's consumer AI, explain why it's social, not solo. If it's B2B AI, frame it as selling work, not software. She's a product person with deep operating experience at Pinterest — show the product, not the deck. Start with your constrained 'thimble' market and show how you expand. Demonstrate the thought process behind your key metrics. Show customer obsession and willingness to do unscalable things. She responds to founders who understand user behavior deeply, not ones who lead with TAM slides.
Key Frameworks
hierarchy_of_engagement
{"summary":"Three-level framework for evaluating and building consumer companies.","level_1_core_action":"Identify and grow users around a single 'core action' that forms the foundation of the product, boils down its essence, and is highly correlated with retention. Example: pinning at Pinterest. If you're only growing users without getting them to the core action, you'll burn through early adopters.","level_2_retention":"Create 'accruing benefits' (the more someone uses the product, the better it gets for them) and 'mounting loss' (the more someone uses it, the more they'd lose by leaving). As users add data explicitly or implicitly, the company uses it to improve their experience.","level_3_self_perpetuation":"Achieve self-perpetuation through network effects and growth/re-engagement loops. The product becomes a flywheel that compounds on itself."}
hierarchy_of_marketplaces
{"summary":"Three-level framework for building enduring marketplaces, centered on happiness rather than GMV.","level_1_minimum_viable_happiness":"Kickstart transactions by picking a constrained problem. Reach 'Minimum Viable Happiness' — the point where both buyers and sellers are happy enough with the experience to come back.","level_2_tipping":"Reach a new happiness threshold where you're so much better than any substitute that the market 'tips' in your direction. Find scalable, systematic ways to create better buyer-seller matches over time.","level_3_domination":"Three vectors: (1) Outrun to become #1 by a wide margin in your original market, (2) Expand buyer use cases beyond your initial thimble, (3) Take your Level 1-2 playbooks and pursue multi-threaded domination of adjacent markets."}
happy_gmv
{"summary":"A metric framework that replaces raw GMV with 'Happy GMV' — the subset of gross merchandise value representing transactions where both buyer and seller had an experience good enough to drive retention.","how_to_measure":"Ask yourself: 'What is my best guess at the buyer and seller experience that will lead to retention?' Then measure the percentage of potential buyers and sellers that get that experience. The GMV that qualifies is your Happy GMV.","why_it_matters":"GMV is a vanity metric like MAUs. No matter how large an incumbent, they are vulnerable to a new entrant that makes buyers and sellers happier. Happiness, not scale, is your moat.","nps_critique":"Net Promoter Score is not a useful measure of marketplace happiness. Happy GMV is more actionable because it connects directly to retention-driving experiences."}
sell_work_not_software
{"summary":"AI enables a paradigm shift from selling software (15% productivity improvement) to selling completed work (95% improvement). Incumbents are stuck selling software; AI-native startups can leapfrog them entirely."}
10x_and_cheaper
{"summary":"The best startups create products that are 10x better AND dramatically cheaper than incumbents, recasting entire cost structures. AI is the technology catalyst enabling a new wave of these companies (e.g., DeepL, HeyGen, ElevenLabs, Midjourney)."}
thimble_market
{"summary":"Start in a tiny constrained market ('thimble') adjacent to very large ones, then expand outward. She'd much rather back a founder with a core insight into a small market who can expand, than one going after a big market from the beginning."}
Notable Writing
Framework for evaluating consumer companies: Level 1 is growing engaged users around a 'core action,' Level 2 is retaining users through accruing benefits and mounting loss, Level 3 is self-perpetuation through network effects and virtuous loops.
Updated version clarifying how the framework impacts product roadmaps, with added layers on what makes a core action strong and how to measure retention meaningfully.
Level 1 is about kickstarting transactions and reaching 'Minimum Viable Happiness' by picking a constrained problem. Focus on happiness, not GMV.
Reaching a new happiness threshold where you're so much better than any substitute that the market 'tips' in your direction. Find scalable, systematic ways to create better buyer-seller matches over time.
Three vectors to domination: outrun to become #1 in your original market, expand buyer use cases beyond your initial thimble, and pursue multi-threaded domination of adjacent markets.
To measure Happy GMV, ask 'what is my best guess at the buyer and seller experience that will lead to retention?' then measure the percentage that get that experience. NPS is not a useful proxy.
Create a 10x product and recast incumbent cost structures. The combination of dramatically better experience at dramatically lower cost is the formula for enduring businesses.
For the past 25 years, software startups sold productivity improvements. AI enables selling the completed work itself — a 95% improvement instead of 15%. Incumbents are stuck selling software; startups can leapfrog.
Follow-up expanding on the sell-work thesis with real-world examples and refinements.
LLM-based startups can build enduring value by accruing data assets as a positive externality of users using the application. This externalizes the moat beyond what's possible with LLMs alone.
A new wave of AI startups (DeepL, HeyGen, ElevenLabs) unlock new use cases by providing 10x better experiences at a fraction of the cost, recasting entire cost structures.
Companies like Midjourney, DeepL, and ElevenLabs demonstrate how AI creates entirely new markets by making previously expensive capabilities accessible and dramatically better.
Explores whether AI will produce as many breakthrough consumer companies as the mobile revolution did, concluding there will be countless consumer AI companies more akin to Uber or Google Maps that give consumers new superpowers.
As foundation models become more powerful, LLM companies will move up the stack and compete with their API developers. B2B AI startups need to build moats beyond API access.
Being distributed at the early stages is like sprinting with a parachute strapped to your back. Collaboration, trust, creativity from hallway conversations are all highest in person.
Applies her longstanding '10x better and cheaper' framework specifically to AI-native companies.
Podcast Appearances
Key Quotes
“No matter how large an incumbent may be, they are vulnerable to a new entrant that makes buyers and sellers happier — happiness, not scale, is your moat.”
— Hierarchy of Marketplaces series
“I'd much rather have someone have a core insight into a small market — the thimble of a market — and have the ability to expand beyond that, than to be going after a big market from the beginning.”
— 20VC interview
“I just think that markets end up having their own physics where you can't change those physics, regardless of how great the founder is.”
— 20VC interview
“I am much more interested than I used to be in the thought process that got to the metric they're focused on today, because that reflects how they're running their company.”
— interview
“My operating experience has given me an immediate level of empathy for the founders because it's so freaking hard.”
— interview
“For the past 25 years, application software startups have focused on increasing company and employee productivity. AI enables a paradigm shift — rather than sell software to improve an end-user's productivity, consider what it would look like to sell the work itself.”
— AI Startups: Sell Work, Not Software
“When you sell the work, instead of selling a 15% productivity improvement, you sell a 95% improvement. Incumbents are not only advantaged in selling software, they are stuck there.”
— AI Startups: Sell Work, Not Software
“The core action is the single action users can take that forms the foundation of your product, boils down the essence of your product, and is often highly correlated with retention.”
— Hierarchy of Engagement
“To retain users, you have to create accruing benefits and mounting loss within your product. With accruing benefits, the more someone uses the product, the better it gets. In mounting loss, the more someone uses the product, the more they would lose if they left.”
— Hierarchy of Engagement
“Being distributed at the early stages can be like trying to sprint while pulling a parachute strapped to your back.”
— Founding a Company? My Nudge to Choose Co-Located
“Consumer networks tend to have larger founding teams. There is something about the multi-dimensionality of getting a network's flywheel spinning that requires team depth.”
— Twitter/X
“To create a new consumer network, do you need a new atomic unit? Twitter with 140-character text, Instagram with the photo, Snap with the disappearing photo, TikTok with short video.”
— Twitter/X
Background
Sourced Bessemer Venture Partners' investment in Pinterest as a VP. Joined Pinterest as one of their first 35 employees and became their first product manager. Launched Pinterest's first search and recommendations features and led core discovery products. Moved to Greylock Partners as General Partner (led investments in Sonder and Gixo). Joined Benchmark in 2017 as the firm's first woman general partner. Transitioned to Venture Partner in April 2025.