"Data should not just be a static asset, but a dynamic financial tool that can be monetized, shared, and managed." — Anna, Founder and CEO of Vana
Author: Sunny, Deep Tide TechFlow
Guest: Anna Kazlauskas, Vana Founder
Interview Introduction
I met Anna at the ETHcc side event organized by Flock.io. At that time, I was exhausted from the repetitive and dull content at various hot projects and VC forums until Anna appeared. Her style was different from other Web3 founders; it was refreshing and crisp, reminiscent of a high school girl passionate about her personal project. Her speech made me stop in my tracks and continue listening.
Interestingly, I had previously interviewed the founder of Flock, whose project involved decentralized data contribution, but Flock leaned towards public data, while Vana focuses on private data. When I searched for Anna on Twitter, I discovered that her project not only attracted top investors like Paradigm and Polychain, but she herself was not a high school student; she had dropped out early to join Silicon Valley's Y Combinator as an entrepreneur. Later, we arranged an interview at a coffee shop to delve into her experiences.
Anna studied at MIT and was classmates with the founders of Optimism, Blur, and Glow. So how do these talented founders at the forefront of information, education, and capital view their lives and careers? This interview reveals all.
Introduction to Vana
Vana is a decentralized platform dedicated to revolutionizing data ownership and AI development in the Web3 space. The core concepts of the platform include:
Data Ownership and Portability: Vana empowers users to control and manage their personal data, allowing it to flow freely between different applications and storing it in a non-custodial manner similar to cryptocurrency wallets.
Decentralized AI Development: Users can contribute data to train AI models and gain ownership shares of the contributed models. Developers can utilize cross-platform data to build personalized applications and train advanced AI models.
Data Liquidity and Monetization: Vana introduces Data Liquidity Pools (DLP), incentivizing users to provide valuable data and validate it. Users can monetize their data by contributing to these pools, viewing data as a flexible, modular financial asset.
Ecosystem and Applications: Vana is building a series of decentralized applications and data collectives, such as the r/datadao for Reddit data, the Volara marketplace for Twitter/X data, and the Flirtual platform that allows users to control their dating app data.
Technical Infrastructure: Vana has developed a Layer 1 blockchain specifically designed for data trading and privacy, utilizing a multi-layer architecture (including data portability and data liquidity layers) and a Universal Connectome to map data flow within the ecosystem.
Vana is committed to bringing users a new model of data ownership, AI development, and value creation, empowering individuals and driving innovation in the field of artificial intelligence.
Interview Summary
From Federal Reserve Internship to Y Combinator: Anna shared her journey from interning at the Federal Reserve during high school to working at the World Bank, how she automated repetitive tasks, and ultimately dropped out to join Y Combinator to embark on her entrepreneurial journey.
Discovery of AI and Data Ownership: While at the MIT Bitcoin Club, Anna discovered the connection between artificial intelligence and decentralized data, particularly the challenges regarding data quality and ownership. She co-founded Vana with her co-founder to help users benefit from the data they contribute.
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. Vana's technical infrastructure is based on a Layer 1 blockchain, ensuring data privacy and scalability.
The Impact of Quantitative Thinking on Anna: Anna has been sensitive to numbers and probabilistic modeling since childhood, and this quantitative thinking has helped her form a unique perspective on AI and data systems, driving her to explore how to commercialize data through AI models and decentralized platforms.
Challenges and Opportunities in Decentralized Data: Anna explained the main challenges in the field of decentralized data ownership, including how to help users understand the value of their data and how to address this issue through a unique token economic model while creating a self-sustaining data-driven ecosystem.
Vana's Business Model: Through data trading models and decentralized AI development, Vana views data as a financial asset, allowing users to gain ownership of AI models and profit from their contributions.
From Federal Reserve Internship to Y Combinator—How Did Anna's Entrepreneurial Journey Begin?
Deep Tide TechFlow: You interned at the Federal Reserve during high school and then entered the World Bank the following year. How did these experiences shape your subsequent entrepreneurial journey?
Anna:
Yes, it all started at Central High School in Minnesota. I was very interested in economics, and being able to intern at the Federal Reserve during high school was quite unusual for someone my age, but I was very determined. I remember having a poster of Janet Yellen in my room! Later, when I joined the World Bank, I found that many interns were doing repetitive work, and I wanted to simplify that through automation. I wrote machine learning software to help classify documents, and the impact was greater than I expected. It was then that I realized that data and automation could truly change the way we handle tasks in large organizations. So, I dropped out of high school and joined Y Combinator, and that's how I got into Silicon Valley.
Deep Tide TechFlow: What prompted you to drop out of high school and pursue entrepreneurship full-time?
Anna:
That was a significant decision for me at the time, but I realized that entrepreneurship is a huge commitment, almost like a journey lasting 5 to 10 years. I loved the idea of solving problems, but not just doing automation of documents forever. My true passion came from seeing how artificial intelligence was evolving, with data quality being at its core. I started thinking about how to apply decentralized systems like cryptocurrency to data ownership. This drove me and my co-founder Art Abal, who was then a graduate student at Harvard, to establish Vana.
The Intersection of AI and Blockchain—The Inspiration Anna Discovered at MIT Bitcoin Club
Deep Tide TechFlow: When did you first see the connection between artificial intelligence and decentralized data and cryptographic technology?
Anna:
My fascination with artificial intelligence began when I was at the MIT Bitcoin Club. I was very 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 that everything about artificial intelligence 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. Since 2018, my co-founder and I began exploring how to enable users to benefit from the AI models built with their data.
Why Choose Layer 1?—How Vana Focuses on Private Data Ownership
**Deep Tide TechFlow: I understand that Vana is built on an *EVM* Layer 1 blockchain. Can you explain why you chose to build your own Layer 1?**
Anna:
Vana is structured as a Layer 1 blockchain specifically designed for private data. This is a crucial decision because it allows us to have tokens for specific datasets and tokens for specific models, which are fully programmable and compatible with the EVM Ethereum Virtual Machine. This enables us to flexibly support any AI model or dataset while ensuring that users can control their data. The Layer 1 architecture also helps address scalability and privacy issues, which are essential for creating a sustainable decentralized data ownership ecosystem. Since private data is very valuable but difficult to monetize in traditional systems, building Vana as a Layer 1 allows us to tackle the data authorization challenges while providing infrastructure for large-scale AI applications.
AI Vision Driven by Quantitative Thinking and the Business Model of Decentralized Data
During the interview, Anna also shared her upbringing. Her father is a biochemistry professor, her mother is a writer, and she has been sensitive to numbers since childhood.
Deep Tide TechFlow: It sounds like you've viewed the world in a quantitative way since you were young?
Anna:
Absolutely correct! I have always been very interested in quantitative thinking. It's not about creating a perfect model, but about making the model useful—understanding what different outcomes can result from changing a single element. This is precisely why I have a unique perspective on artificial intelligence and data today. For example, I really enjoy probability modeling, which is often used in baseball analytics. Instead of predicting a certain outcome, it's better to model various probabilities—for instance, if the ball lands here, what is the probability of hitting a home run? This way of thinking has helped me form my thoughts on artificial intelligence and data systems.
Deep Tide TechFlow: Given your advantage in quantitative thinking, do you have any predictions on when Vana will achieve profitability?
Anna:
That's a good question! Given our business model, profitability depends on the scale of data contributions and the value of the AI models we build. From a quantitative perspective, we estimate that we will be in a favorable position to achieve profitability when we have about 100 million users contributing data and multiple high-value AI models generating revenue. The real challenge lies in ensuring a stable flow of data and developing AI models that have commercial value. I think the specific timing will depend on various factors such as user growth, adoption of AI models, and the overall market demand for AI-driven solutions.
Deep Tide TechFlow: What are the main challenges in building a company, especially in such a complex field as decentralized data ownership?
Anna:
One of the biggest challenges is helping people understand the value of their private data. In the early days, we tried to pay users for their data in cash or stablecoins, but they didn't resonate with that approach. It almost made their contributions feel devalued. Now we have shifted to offering ownership of the AI models created from the data, which has resonated more. People want to feel like they are part of something bigger, and the idea of owning a part of an AI model is much more appealing. We saw this with the Reddit Data DAO—nearly one million wallets registered, with about 140,000 passing the proof of contribution, meaning they provided valuable Reddit data. This is much more attractive than just offering cash.
**Deep Tide TechFlow: Can you explain Vana's *business model* and how it generates revenue?**
Anna:
Vana operates on a data trading model. Every time data is processed through our network, a small fee is generated—similar to the gas fees in Ethereum—to cover the costs of running the network. As more users contribute their data and more AI models are built using that data, the system will become self-sustaining. We have also patented our non-custodial data wallet and designed data liquidity pools and tokens for specific models. These tokens allow users to simultaneously own datasets and the AI models derived from those datasets, creating a system where users can profit from the value of their data. For example, through our Reddit dataset token, users can collectively own that dataset as well as any AI models built on it. As these models become increasingly valuable, users who contribute data will be able to benefit from it.
Deep Tide TechFlow: In such a complex system, how do you ensure that people are motivated to contribute their data?
Anna:
We have succeeded through our data liquidity pools and token system for specific models. For instance, through our Reddit Data DAO, users can contribute their Reddit data and, in return, receive tokens that represent their ownership in the dataset and any generated AI models. The key is to make this feel tangible to people—they are not just giving away data; they are gaining a part of the ownership of something bigger. We have shifted from cash rewards to something more meaningful: ownership of the AI models created from the data users contribute. This significantly increases the appeal for users.
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