In this edition of Behind the Startup, Ishan Pandey sits down with Sam Green, co-founder of Cambrian Network, to explore how verifiable blockchain data is becoming the lifeblood of agentic DeFi. From his early work at Sandia National Labs to building Odos and Semiotic Labs, Sam brings a rare blend of cryptographic research, protocol-level thinking, and AI expertise to the world of decentralized finance. In this candid conversation, he breaks down how Cambrian is building a verifiable data chain for AI agents and why this shift could reshape how financial decisions are made on-chain. Behind the Startup Cambrian Network Ishan Pandey: Hi Sam, it's a pleasure to welcome you to our "Behind the Startup" series. What inspired you to build Cambrian Network, and what specific challenges did you face in your journey? Ishan Pandey: Hi Sam, it's a pleasure to welcome you to our "Behind the Startup" series. What inspired you to build Cambrian Network, and what specific challenges did you face in your journey? Sam Green: Thank you, Ishan - it’s great to be chatting with you. My roots in data go back to when I worked at Sandia National Labs, where I used side-channel analysis to uncover cryptographic vulnerabilities in hardware. Watching data provide hidden insights was transformative. Later, I pursued a PhD focused on reinforcement learning, which is all about agents finding optimal behavior from large datasets. Sam Green: In 2021, I co-founded Semiotic Labs and worked on measuring arbitrage opportunities across different blockchains - an eye-opening project that led to the creation of Odos, a leading decentralized exchange aggregator that has processed over $90 billion in trading volume for over 3 million users. Around the same time, I was also working in The Graph ecosystem, exploring verifiable data and AI for on-chain information retrieval. Semiotic Labs Semiotic Labs Odos Odos The Graph The Graph By 2024, it became clear that decentralized finance (DeFi) was headed toward “agentic finance,” where AI-driven agents make real-time decisions on the blockchain. Recognizing how crucial fast, complete, and verifiable data would be for these agents, I spun off Cambrian to focus on exactly that. One of our initial, biggest challenges has been how to provide both real-time and historical blockchain data in a verifiable manner. Blockchains typically don’t store extensive historical data; it’s just not part of their core design. Another hurdle is making that data trustworthy enough for on-chain agents and applications to rely on autonomously. We tackled these challenges by combining specialized indexing, high-speed databases, and consensus-based verification. Ultimately, Cambrian is aimed at accelerating the future we see emerging: AI agents autonomously making financial decisions powered by robust, verifiable data. Ishan Pandey: Cambrian is described as the first verifiable data chain for agentic finance. Could you elaborate on what this entails and how it differentiates from existing solutions? Sam Green: Sure. By “verifiable data chain,” we mean that Cambrian ensures the blockchain and off-chain data we collect is both correct and tamper-resistant before it’s delivered to AI agents or smart contracts. In other words, we’re giving blockchains something akin to a database - one that’s trustworthy enough to feed real-time and historical data into financial applications. Sam Green: Blockchains themselves are fantastic at maintaining current state and transaction logic, but they weren’t designed to store or query large amounts of historical data. Traditional RPC endpoints can provide recent on-chain data, however, retrieving comprehensive historical context can be slow, cumbersome, or sometimes impossible. recent historical What we do at Cambrian is index, structure, and verify data from multiple chains (and select off-chain sources) at high speed. Then, we deliver it to agentic DeFi applications through easy-to-use APIs. Our focus on financial intelligence sets us apart: we’re specialized in combining on-chain metrics (like trading volumes, liquidity flows, cross-chain transactions) with off-chain insights (like social sentiment, news, and even code repository analysis). In contrast, existing indexing services are more general-purpose, or they’re not fast enough, or they’re not verifiable - and none of them are designed for agents from the outset, e.g., MCP support for every interface from the beginning. Cambrian specifically gears all of its data products toward quantitative trading, automated liquidity management, risk-adjusted portfolio rebalancing, data-driven sentiment analysis, verifiable information oracles, and other financial use cases. We’re building an infrastructure that agents can rely on to “sense” everything happening in DeFi, freeing them to make smarter, faster decisions. Ishan Pandey: Security and data integrity are paramount in financial systems. What measures does Cambrian implement to maintain the verifiability and reliability of its datachain? Sam Green : We use cryptography, consensus mechanisms, and economic security - the same core principles securing major blockchains - to guarantee data integrity. Here’s the basic flow: Data Ingestion: Cambrian validators pull in a complete raw feed of blockchain data (e.g., from Ethereum). We store this in a local “flat file” archive – in other words, a straightforward, tabular format.
Hashing & Agreement: Each validator computes a cryptographic hash of that raw data. They confirm these hashes match what the blockchain itself says they should be, then reach consensus among themselves that the data is correct.
Database Organization: We load verified data into a high-performance analytical database.
Query + Consensus (Pre-verified Mode): When an application requests a query, validators independently run that query, verify they reach the same result, and then collectively sign off on it. Data Ingestion: Cambrian validators pull in a complete raw feed of blockchain data (e.g., from Ethereum). We store this in a local “flat file” archive – in other words, a straightforward, tabular format. Hashing & Agreement: Each validator computes a cryptographic hash of that raw data. They confirm these hashes match what the blockchain itself says they should be, then reach consensus among themselves that the data is correct. Database Organization: We load verified data into a high-performance analytical database. Query + Consensus (Pre-verified Mode): When an application requests a query, validators independently run that query, verify they reach the same result, and then collectively sign off on it. For users who need faster returns, we also support an “optimistic” mode. If there’s a dispute about the correctness of an optimistic query, validators can re-check it. If a validator intentionally lies about the data, it faces “slashing,” which means forfeiting staked tokens as a penalty. This creates a powerful economic incentive to provide accurate data. By combining these methods, we make sure our data remains trustworthy for AI agents and smart contracts. Unlike traditional data providers, which rely on contractual or legal frameworks, Cambrian enforces correctness at the protocol level via cryptography, consensus, and slashing-based economic security. Ishan Pandey: In what ways does Cambrian's approach to data aggregation differ from traditional financial data providers? Sam Green: Traditional financial data providers have their place - they've achieved product-market fit and built trust over years through service level agreements and legal contracts. Their customers rely on these agreements for accuracy and security assurances, backed by the legal system. Sam Green: However, this approach relies on centralized points of control and trust, which are antithetical to how blockchains operate. Placing their data directly on-chain would introduce a single point of failure, undermining the decentralized nature of blockchain technology. Cambrian takes a different path. We ensure data verifiability using cryptography and consensus mechanisms, aligning with blockchain principles. By leveraging these technologies, we provide guarantees of correctness and tamper resistance that are compatible with decentralized networks. This means our data can be trusted by AI agents and smart contracts operating autonomously on-chain, without relying on centralized authorities. In essence, while traditional providers offer security through legal frameworks, Cambrian offers security through cryptography and decentralized consensus - making our approach uniquely suited for the decentralized, trust-free environment of blockchain technology. Ishan Pandey: Considering the competitive landscape of AI-driven financial platforms, what strategic partnerships or collaborations has Cambrian pursued to strengthen its market position? Sam Green: We see a whole new wave of agentic DeFi emerging, where AI agents manage everything from liquidity provisioning to automated arbitrage to portfolio rebalancing. These projects need fast, comprehensive, and trustworthy data. Currently, we’re in talks or early collaborations with teams like Eliza Labs, Virtuals, Theoriq, Morpheus, Franklin X, and TrueNorth - all of whom are pushing boundaries in AI-enhanced decentralized finance. We’re also getting interest from layer-1 protocols looking to integrate Cambrian data for training financial models, plus AI platforms like Pond and Open Gradient that want to feed Cambrian’s aggregated insights into their neural networks. Sam Green: Our goal is to be the data backbone for any developer creating AI-driven financial applications. By delivering real-time, historical, and off-chain insights under one umbrella, we enable these emerging players to build powerful, autonomous solutions quickly and confidently. Ishan Pandey: What are your predictions for the future of AI driven Financial solutions across the world? Where do you see the industry in the next 2-3 years? Sam Green: The short answer is that agents will dominate on-chain activity. We’re already seeing several major leaps in AI capabilities each year - what used to cost a fortune can now run off-chain with verifiable correctness. People will still set the goals and constraints (like risk tolerance or investment horizons), but agents will do real-time analytics and execute trades. Sam Green In the next two or three years, I believe most on-chain transactions will originate from AI agents making decisions at a granularity humans simply can’t match. These systems will analyze massive historical datasets, capture the latest market sentiment or news, and keep adjusting positions dynamically. It’s a fundamental shift: why rely on manual strategies when an agent can continuously optimize on your behalf? We also foresee more traditional financial data feeding into crypto, especially as tokenized real-world assets gain traction. That means bridging stock market data, corporate fundamentals, commodity prices, or other metrics onto the blockchain in a verifiable way. With Cambrian, we’re extending beyond on-chain sources to integrate all kinds of off-chain signals - enabling a holistic view of global markets in real-time. Ultimately, we’re heading toward a solarpunk future where AI and human creativity work together in harmony. Cambrian actively helps make these agentic systems more transparent and trustworthy. In two or three years, if you’re interacting with DeFi in any capacity, chances are good you’ll be leveraging an AI agent empowered by data from Cambrian. It’s an exciting time to be building in this space. any Don’t forget to like and share the story! Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYO Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYO Vested Interest Disclosure: Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYO business blogging program business blogging program

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