Interview with Vana's Founder: Dropping Out of High School, From the Federal Reserve to Y Combinator, Navigating Web3, Embarking on a Journey Toward Decentralized Data
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Interview with Vana's Founder: Dropping Out of High School, From the Federal Reserve to Y Combinator, Navigating Web3, Embarking on a Journey Toward Decentralized Data
"Data should not be just a static asset, but a dynamic financial instrument that can be monetized, shared, and managed." ——Anna, Founder and CEO of Vana
Navigating Web3 tides with focused insightsAuthor: Sunny, TechFlow
Guest: Anna Kazlauskas, Founder of Vana
Interview Overview
I met Anna at an ETHcc side event organized by Flock.io. By then, I had grown weary of the repetitive and uninspiring content at various hot project and VC forums—until Anna stepped on stage. Her style stood out from other Web3 founders: fresh, sharp, and energetic, like a high schooler deeply passionate about her personal project. Her talk made me stop in my tracks and listen closely.
Interestingly, I had previously interviewed the founder of Flock, whose project focuses on decentralized data contribution—but leans toward public data, whereas Vana specializes in private data. When I searched for Anna on Twitter, I discovered not only that her project had attracted top-tier investors like Paradigm and Polychain, but also that she wasn't actually a high school student, but rather a young entrepreneur who dropped out early and entered Silicon Valley's Y Combinator. Later, we arranged a coffee shop interview to dive deeper into her journey.
Anna once studied at MIT, where she was classmates with founders of Optimism, Blur, and Glow. So, what do these brilliant minds at the forefront of information, education, and capital think about their lives and careers? This interview reveals all.
About Vana
Vana is a decentralized platform revolutionizing data ownership and AI development in the Web3 space. Its core principles include:
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Data Ownership and Portability: Vana empowers users to control and manage their personal data, enabling it to flow freely across applications. Data is stored non-custodially, much like cryptocurrency wallets.
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Decentralized AI Development: Users can contribute data to train AI models and receive ownership shares in those models. Developers can leverage cross-platform data to build personalized apps and advanced AI models.
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Data Liquidity and Monetization: Vana introduces Data Liquidity Pools (DLPs), incentivizing users to contribute and validate valuable data. By contributing to these pools, users monetize their data, treating it as a flexible, modular financial asset.
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Ecosystem and Applications: Vana is building a suite of decentralized applications and data collectives, such as r/datadao for Reddit data, the Volara marketplace for Twitter/X data, and the Flirtual platform allowing users to control their dating app data.
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Technical Infrastructure: Vana has developed a purpose-built Layer 1 blockchain designed for data transactions and privacy. Through a multi-layer architecture—including a data portability layer and a data liquidity layer—and its Universal Connectome, it maps data flows across the ecosystem.
Vana aims to create a new paradigm for data ownership, AI development, and value creation—empowering individuals and driving innovation in artificial intelligence.
Interview Summary
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From Federal Reserve Internship to Y Combinator: Anna shares her journey from interning at the Federal Reserve in high school to working at the World Bank, how she used automation to solve repetitive tasks, and ultimately dropped out to join Y Combinator and launch her entrepreneurial path.
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Discovering AI and Data Ownership: While at MIT Bitcoin Club, Anna identified the link between AI and decentralized data, particularly around data quality and ownership challenges. She co-founded Vana with the mission of enabling users to benefit from the data they contribute.
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Vana’s Mission and Technical Architecture: Vana is a decentralized platform focused on private data ownership, allowing users to monetize their data through data liquidity pools and token mechanisms. Its technical foundation is a Layer 1 blockchain built for data privacy and scalability.
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The Impact of Quantitative Thinking on Anna: Anna has been naturally drawn to numbers and probabilistic modeling since childhood. This quantitative mindset shaped her unique perspective on AI and data systems, driving her exploration of commercializing data via AI models and decentralized platforms.
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Challenges and Opportunities in Decentralized Data: Anna discusses key challenges in decentralized data ownership—especially helping users understand the value of their data—and how a novel token economic model addresses this while creating a self-sustaining, data-driven ecosystem.
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Vana’s Business Model: Through data transaction models and decentralized AI development, Vana treats data as a financial asset. Users earn ownership stakes in AI models they help train and profit from their contributions.
From Federal Reserve Internship to Y Combinator—How Did Anna’s Entrepreneurial Journey Begin?
TechFlow: You interned at the Federal Reserve during high school, then joined the World Bank the following year. How did these experiences shape your later entrepreneurial path?
Anna:
Yes, it all started at Central High School in Minnesota. I’ve always been deeply interested in economics. Getting to intern at the Federal Reserve as a high schooler was highly unusual, but I was determined. I even had a photo of Janet Yellen hanging in my room! Later, when I joined the World Bank, I noticed many interns were stuck doing repetitive tasks. I wanted to streamline that work through automation. I built machine learning software to help classify documents, and the impact turned out greater than expected. That’s when I realized data and automation could fundamentally change how large organizations operate. That insight led me to drop out of high school and join Y Combinator—and that’s how I entered Silicon Valley.
TechFlow: What motivated you to drop out of high school and pursue entrepreneurship full-time?
Anna:
It was a major decision at the time, but I realized entrepreneurship is a huge commitment—almost a 5 to 10-year journey. I enjoyed problem-solving, but not just automating documents forever. My true passion emerged from seeing how artificial intelligence was evolving, with data quality at its core. I began exploring how decentralized systems like cryptocurrency could be applied to data ownership. That inspired me and my co-founder Art Abal to launch Vana. He was a graduate student at Harvard at the time.
The Intersection of AI and Blockchain—Anna’s Inspiration from MIT Bitcoin Club
TechFlow: Speaking of AI, when did you first see the connection between it and decentralized data or crypto technologies?
Anna:
My fascination with AI began during my time at MIT Bitcoin Club. I was deeply interested in econometrics and data modeling. In 2017, I saw the release of the paper "Attention is All You Need", which later became the foundation for ChatGPT. I suddenly realized everything in AI revolves around data—especially data quality and ownership. I wanted to find a way for people to truly own the data they contribute to AI systems. Starting in 2018, my co-founder and I began exploring how users could benefit from the AI models their data helps build.
Why Build a Layer 1?—How Vana Focuses on Private Data Ownership
TechFlow: I understand Vana runs on an EVM-compatible Layer 1 blockchain. Can you explain why you chose to build your own Layer 1?
Anna:
Vana is architected as a Layer 1 blockchain specifically designed for private data. This was a critical decision because it allows us to have tokens for specific datasets and tokens for specific models—fully programmable and compatible with the Ethereum Virtual Machine (EVM). This gives us the flexibility to support any AI model or dataset, while ensuring users retain control over their data. The Layer 1 architecture also helps address scalability and privacy challenges, which are essential for building a sustainable, decentralized data ownership ecosystem. Since private data is highly valuable but hard to monetize in traditional systems, building Vana as a Layer 1 enables us to tackle data licensing challenges and provide infrastructure for large-scale AI applications.
A Vision for AI Driven by Quantitative Thinking—The Business Model of Decentralized Data
During the interview, Anna also shared insights into her upbringing. Her father is a biochemistry professor, her mother a writer, and she herself has been sensitive to numbers from an early age.
TechFlow: It sounds like you've always viewed the world quantitatively?
Anna:
Absolutely! I’ve always been fascinated by quantitative thinking. It’s not about creating a perfect model, but making the model useful—understanding how changing one variable leads to different outcomes. That’s exactly what gives me a unique perspective on AI and data today. For example, I love probabilistic modeling, commonly used in baseball analytics. Instead of predicting a fixed outcome, you model the probabilities—like, if the ball lands here, what’s the chance of a home run? This kind of thinking has shaped how I approach AI and data systems.
TechFlow: Given your strength in quantitative thinking, do you have any prediction for when Vana might reach break-even?
Anna:
Great question! Given our business model, reaching break-even depends on the scale of data contributions and the value of the AI models we build. From a quantitative standpoint, we estimate we’ll be in a strong position to break even when we have around one hundred million users contributing data, and multiple high-value AI models generating revenue. The real challenge lies in ensuring stable data flow and developing AI models with commercial value. The exact timeline will depend on factors like user growth, adoption of AI models, and overall market demand for AI-driven solutions.
TechFlow: In building a company, especially in a complex field like decentralized data ownership, what are the main challenges?
Anna:
One of the biggest challenges is helping people understand the value of their private data. Early on, we tried paying users in cash or stablecoins for their data, but they didn’t resonate with that. It almost felt devaluing. Now we’ve shifted to offering ownership in the AI models created from their data, which resonates much more strongly. People want to feel part of something bigger, and owning a piece of an AI model is far more appealing. We’ve seen this clearly with our Reddit Data DAO—nearly one million wallets registered, with about 140,000 passing proof of contribution, meaning they provided valuable Reddit data. That’s far more compelling than just cash payments.
TechFlow: Can you explain Vana’s business model and how it generates revenue?
Anna:
Vana operates on a data transaction model. Every time data moves through our network, a small fee is generated—similar to gas fees in Ethereum—to cover network operating costs. As more users contribute data and more AI models are built using that data, the system becomes self-sustaining. We’ve also patented our non-custodial data wallet and designed data liquidity pools and model-specific tokens. These tokens allow users to jointly own both datasets and the AI models derived from them, creating a system where users can profit from the value of their data. For instance, through our Reddit dataset token, users collectively own that dataset and any AI models built upon it. As those models grow in value, contributors benefit directly.
TechFlow: In such a complex system, how do you ensure people stay motivated to contribute their data?
Anna:
We’ve found success through our data liquidity pools and model-specific token system. For example, via our Reddit Data DAO, users contribute their Reddit data and in return receive tokens representing their ownership stake in both the dataset and any resulting AI models. The key is making it tangible—they’re not just handing over data, they’re gaining ownership in something larger. We’ve moved from cash rewards to something more meaningful: ownership in AI models created with their data. That’s significantly more attractive to users.
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