Tomasz Tunguz

Theory Ventures

Student of startups, author of tomtunguz.com, investor, & partner to founders.

General Partner

Redpoint Ventures

Privileged to support the journey of early stage SaaS, data and infrastructure founders. Worked with Looker ($2.6b M&A), MotherDuck, Mysten Labs (SUI), Monte Carlo, Omni, Chorus ($575m to ZI), Expensify (IPO), ThredUp (IPO), Kustomer ($1b sale to META), & others.

Managing Director

Winning with Data

I co-authored Winning with Data with Looker CEO Frank Bien.  Winning with Data: Transform Your Culture, Empower Your People, and Shape the Future explores  the cultural changes big data brings to business, and shows you how to adapt your organization to leverage data to maximum effect.

Co-Author

Google, Inc.

Managing AdSense for Social Networks including the $900M MySpace deal.
Managed AdSense International including product launches in Greece, Israel, Thailand, Indonesia and Bulgaria

Product Manager

Appian Corporation

Architected, developed and deployed systems for the US Government serving DHS

Engineer

Student of Startups
Backer of 9 unicorns
Author of http://tomtunguz.com 
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Founder

AI breaks the data stack.

Most enterprises spent the past decade building sophisticated data stacks. ETL pipelines move data into warehouses. Transformation layers clean data for analytics. BI tools surface insights to users.

This architecture worked for traditional analytics.

But AI demands something different. It needs continuous feedback loops. It requires real-time embeddings & context retrieval.

Consider a customer at an ATM withdrawing pocket money. The AI agent on their mobile app needs to know about that $40 transaction within seconds. Data accuracy & speed aren’t optional.

Netflix rebuilt their entire recommendation infrastructure to support real-time model updates1. Stripe created unified pipelines where payment data flows into fraud models within milliseconds2.

The modern AI stack requires a fundamentally different architecture. Data flows from diverse systems into vector databases, where embeddings & high-dimensional data live alongside traditional structured data. Context databases store the institutional knowledge that informs AI decisions.

AI systems consume this data, then enter experimentation loops. GEPA & DSPy enable evolutionary optimization across multiple quality dimensions. Evaluations measure performance. Reinforcement learning trains agents to navigate complex enterprise environments.

Underpinning everything is an observability layer. The entire system needs accurate data & fast. That’s why data observability will also fuse with AI observability to provide data engineers & AI engineers end-to-end understanding of the health of their pipelines.

Data & AI infrastructure aren’t converging. They’ve already fused.

References
Netflix Technology Blog. (2025, August). “From Facts & Metrics to Media Machine Learning: Evolving the Data Engineering Function at Netflix.” https://lnkd.in/g7XhVf2u ↩︎

Stripe. (2025). “How We Built It: Stripe Radar.” https://lnkd.in/gXtkcWjq ↩︎

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