Data-driven research, guides, and insights for leaders building the infrastructure and intelligence to bring institutions on chain, by the data plumbers at Allium.
The CLARITY Act is closer than ever to passing into law. Just last week, the bill made it out of the Senate Banking Committee, a year after it was first introduced last May. Even with bipartisan discord, (and plenty of reservations still regarding the bill’s regulation of stablecoin yield
How you pick a supply denominator moves a crypto valuation more than most other assumptions. Using HYPE as the worked example. For HYPE, the same business value we estimate at $25.8B, divided across six candidate supply numbers, produces price targets from $26 (FDV) to $105 (circulating only) per token.
Key takeaways: * A stablecoin supply number without methodology context is meaningless. * Allium breaks it down into: total supply, issuer-reported supply, and adjusted supply * Payment providers, DeFi protocols, stablecoin issuers all need different supply numbers for their own unique reasons * Allium provides the flexibility to get the exact number you need
Key takeaways: * Compliance systems flag risk, but they rarely explain it * Passing a compliance check doesn’t mean understanding counterparty behavior * Intelligence systems turn activity into explainable patterns and decisions * Both layers depend entirely on the quality of the underlying data 💡What’s the difference between crypto compliance and crypto
Key takeaways: * Blockchain data needs a system with multiple layers to be compliant * Most companies are missing that data foundation unifying interpretation * Data without standardized interpretation can cause huge differences in a compliance stack * AI systems need normalized, standardized data to function without human interference * Crypto compliance data stacks now
Key takeaways: * APIs are simple and fast, but limited to predefined queries * SQL enables flexible, composable queries that align with how AI systems operate * Blockchain data is difficult to serve via APIs without heavy normalization and standardization across chains and protocols * Most meaningful blockchain metrics (balances, positions, flows) must be
Key takeaways: * More crypto analytics and investigations are being assisted by AI * AI systems can use MCP to turn prompts into defined operations * AI can still hallucinate with MCP, risks are still involved * The quality of the data layer will determine the quality of the crypto investigation or analysis 💡What
Key takeaways: * No crypto systems should rely on infrastructure that creates a single point of failure * But data accuracy matters just as much — blockchain data needs to be a system of record for projects to be fully protected * Verification didn’t fail in isolation. The issue was the state it
Key takeaways * Blockchains don’t provide data in the right format to easily build crypto wallets * Many blockchain data platforms have siloed data tools, complicating the wallet building process * PnL is the most difficult problem to solve when building a crypto wallet * Allium provides one platform that simplifies the wallet
Key takeaways: * Crypto wallets used to only need to send, receive, and show balances * The crypto industry’s growth means that users require more from their wallets — like portfolio views, trading capabilities, staking, strategic AI assistance * Traditional wallet infrastructure wasn’t built to support this level of execution * Allium’s
Key Takeaways: * Blockchains store data, but aren’t built for querying * Indexing turns raw data into structured, usable datasets * Without indexing, analytics doesn’t scale * Modern stacks separate ingestion, indexing, and querying * Batch = historical accuracy, streaming = real-time data * AI systems rely on indexed, model-ready data Blockchain indexing is required because
Key Takeaways: * Raw blockchain data can’t be used for high-level analysis * Onchain data needs interpretation * Data normalization is necessary to understanding onchain activity * Onchain data normalization is foundational infrastructure in every blockchain data stack * LLMs and AI systems require normalized data 💡What is onchain data normalization? Onchain data normalization