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Sarah Tavel: Will Foundation Models Be Commoditised? | E1149

Sarah Tavel is a General Partner @ Benchmark, one of the most successful and renowned venture firms in the world. At Benchmark, Sarah has led rounds in Chainalysis, Hipcamp, Medely, Rekki, Glide, Cambly and more. Prior to Benchmark, Sarah was a Partner at Greylock Partners. Before Greylock, Sarah was the first 30 employees at Pinterest. Sarah joined Pinterest in 2012 after co-leading the Series A investment while at Bessemer Venture Partners. ----------------------------------------------- Timestamps: (00:00) Intro (00:51) Background (09:11) Value of Selling the Work in the Application Layer (23:37) The End State of the Model Landscape (36:06) The Challenges & Future of AI Models (39:15) The Role of Competition in the AI Industry (46:02) Reflecting on Missed Investment Opportunities (58:03) Quick-Fire Round ----------------------------------------------- In Today’s Episode with Sarah Tavel We Discuss: 1. Becoming a GP at The Most Renowned Firm in Venture: How did the process of Sarah joining Benchmark start? How did it progress? What was it that convinced her to leave Greylock and join Benchmark? What does Sarah believe makes Peter Fenton the world-class investor that he is? What does Sarah know now that she wishes she had known when she started in venture? 2. Foundation Models: Is it All Going to Zero: Will foundation models be commoditised? Will 99% of the funding going to foundation models go to 0? How does Sarah view the future of open vs closed source? Why does Sarah believe that all frontier models of the future will be closed-source? Why does the business model of foundation models remind Sarah of the food delivery business? 3. Application Layer: Where $BN Companies Will Be Built: Why does Sarah believe that sustainable value-creating companies will be in the application layer? How does Sarah determine between a wrapper on top of ChatGPT and true product value? Are enterprises opening real budgets for AI today or are we still in experimental budgets? How does Sarah think about how AI companies differentiate when there are so many in the same space of customer service, sales team support etc etc? Why does Sarah believe that it is rational to pay more for these companies when investing in them? What does Sarah mean when she says the future is “selling the work and not the tools”? 4. Inside Benchmark: How the Best Do Venture: What is the one rule that Benchmark is willing to break when doing a deal? Why do Benchmark aim to be the best recruitment firm in the world? Why do Benchmark not agree with the concept of reserves? In a case where Benchmark have lost, why did they lose? How did they change their approach? ----------------------------------------------- Subscribe on Spotify: https://open.spotify.com/show/3j2KMcZTtgTNBKwtZBMHvl?si=85bc9196860e4466 Subscribe on Apple Podcasts: https://podcasts.apple.com/us/podcast/the-twenty-minute-vc-20vc-venture-capital-startup/id958230465 Follow Harry Stebbings on Twitter: https://twitter.com/HarryStebbings Follow Sarah Tavel on Twitter: https://twitter.com/sarahtavel Follow 20VC on Instagram: https://www.instagram.com/20vchq Follow 20VC on TikTok: https://www.tiktok.com/@20vc_tok Visit our Website: https://www.20vc.com Subscribe to our Newsletter: https://www.thetwentyminutevc.com/contact ----------------------------------------------- #20vc #harrystebbings #sarahtavel #benchmark #ceo #founder #venturecapital #startup #partnership #hiring #aifuture

Sarah TavelguestHarry Stebbingshost
May 5, 20241h 5mWatch on YouTube ↗

At a glance

WHAT IT’S REALLY ABOUT

Sarah Tavel on AI, Venture Discipline, and Foundation Model Oligopolies

  1. Benchmark partner Sarah Tavel discusses how AI is reshaping both the infrastructure and application layers, arguing that most long‑term value will accrue to applications that “sell the work” rather than just improve productivity. She predicts foundation models will likely be controlled by a small oligopoly due to escalating compute and power requirements, making frontier models predominantly closed source, with Meta’s LLaMA as a possible exception. Tavel explains why many first‑wave AI startups are just thin wrappers around models and how newer companies must own more of the workflow and outcome to be defensible against incumbents. Beyond AI, she walks through Benchmark’s partnership model, views on pricing and dilution, board quality, and why strong “why now” timing and founder ambition matter more than ever in hyper‑competitive markets.

IDEAS WORTH REMEMBERING

5 ideas

AI enables startups to “sell the work,” not just productivity tools.

Instead of charging per seat for small productivity gains, AI companies can package and deliver entire outputs (e.g., HR ops, translation, sales workflows), effectively selling a 90–95% productivity improvement and pricing against headcount rather than marginal efficiency.

Most first‑wave AI apps are too dependent on foundation models to be defensible.

If 90% of an app’s value comes directly from an OpenAI API, incumbents can quickly replicate it and bundle it into existing products, so newer winners must own more of the workflow, data integration, and end‑to‑end experience to avoid being commoditized wrappers.

Foundation models are likely headed toward an oligopoly, not broad commoditization.

Training each successive frontier model is increasingly compute and power constrained and 10x more expensive, which favors a small number of players able to fund massive infrastructure, keeping frontier models mostly closed source except for special cases like Meta’s LLaMA.

“Why now” is a critical and often underweighted determinant of startup success.

Strong timing acts like a current that can push a company forward despite mistakes, whereas weak or purely conceptual ‘why now’ stories leave founders paddling hard against powerful incumbents or countervailing consumer behaviors (e.g., BeReal vs. TikTok attention gravity).

The best AI application opportunities compound as models improve, rather than get steamrolled.

Tavel aligns with the view that if a 100x better model doesn’t massively enhance your product, the model provider will likely outcompete you; good AI apps should become more capable, less human‑dependent, and higher margin as underlying models advance.

WORDS WORTH SAVING

5 quotes

If you want a model that's on the frontier, you're gonna be closed source.

Sarah Tavel

AI enables a very different unit of work that you sell, which is doing the work.

Sarah Tavel

When you have a strong why now, it’s like this strong current that just pushes the company forward.

Sarah Tavel

If you like everything but the price, you pay the price.

Sarah Tavel

The model of partnership with big platforms is more about scaling the GP than it is the founder.

Sarah Tavel

AI as sustaining vs. disruptive technology for incumbents and startupsApplication layer value creation and “selling the work” vs. per‑seat softwareEconomics and end‑state of foundation models (oligopoly, open vs. closed source)Defensibility, differentiation, and competition among AI application startupsVenture discipline: pricing, dilution, reserves, and Benchmark’s partnership modelThe importance of “why now” and market timing in startup successBoard roles, founder–VC trust, and what makes an effective investing partner

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