Next-Gen Crypto Data: From Visibility to Actionable Intelligence

Author: Story, IOSG Ventures

    <h2>TL;DR: The Need for Speed and Smarts in Crypto Data</h2>
    <p>The race for faster transaction speeds in high-throughput blockchains has pushed us into a sub-second era. This surge in speed, combined with the complexities of decentralized finance (DeFi), meme coin mania, and diverse blockchain ecosystems, demands a radical shift in data infrastructure. Traditional data processing methods, with their minutes-to-hours delay, are no longer adequate. Emerging solutions prioritize real-time, incremental data processing with dynamic scaling, shrinking delays to near-instantaneous levels.</p>

    <h2>The Data Competition Evolves: From Understanding to Profitability</h2>
    <p>The previous era of crypto data focused on making sense of blockchain information. Now, the emphasis is on extracting profit. Under models like Bonding Curves, even a one-minute delay can significantly impact profitability. The tools of the trade are evolving rapidly, from manual slippage settings to automated sniper bots and all-in-one trading terminals. As on-chain trading becomes more accessible, the real competitive edge lies in superior data—those who can identify and act on signals faster can generate higher returns for their users.</p>

    <h2>Transaction Data: Expanding Beyond the Basics</h2>
    <p>Meme coins represent the financialization of attention. Their success hinges on narrative, attention, and effective dissemination. Combining off-chain sentiment analysis with on-chain data is crucial. Tracking narrative trends and quantifying sentiment are becoming core components of successful trading strategies. Furthermore, analyzing "hidden data" – fund flows, user profiling, and identifying "smart money" addresses – reveals the underlying dynamics within the anonymous on-chain world. Future trading platforms will incorporate multi-dimensional signals from both on-chain and off-chain data to provide real-time insights for improved trade entry and risk management.</p>

    <h2>AI-Driven Actionable Signals: Turning Information into Profit</h2>
    <p>The next wave of innovation focuses on speed, automation, and generating excess returns. AI, leveraging large language models (LLMs) and multi-modal capabilities, can automatically extract decision signals and combine them with automated strategies like copy trading, take-profit orders, and stop-loss triggers. This approach faces challenges, including AI "hallucinations," short signal lifespans, execution delays, and risk management. Balancing speed and accuracy through reinforcement learning and simulation backtesting is key.</p>

    <h2>Data Dashboards: Evolve or Fade Away</h2>
    <p>Simple data aggregation dashboards are losing their competitive advantage. Their future survival depends on either deepening their underlying data processing capabilities or extending to the application layer to directly serve user needs. The future landscape will be dominated by either infrastructure providers for Web3 or comprehensive user platforms akin to a "Crypto Bloomberg."</p>

    <h2>The Future is in Actionable Insights and Underlying Data Power</h2>
    <p>The sustainable advantage is shifting towards "actionable signals" and robust "underlying data capabilities." The combination of long-tail assets and transaction data creates unique opportunities for crypto-native entrepreneurs. The key opportunity windows in the next 2-3 years are:</p>

    <ul>
        <li><b>Upstream Infrastructure:</b> Building Web3 data infrastructure that offers Web2-level processing power to meet Web3-specific demands, resulting in Web3 versions of Databricks or AWS.</li>
        <li><b>Downstream Execution Platforms:</b> Developing AI Agents combined with multi-dimensional data and seamless execution capabilities, evolving into a Crypto Bloomberg Terminal.</li>
    </ul>

    <p><i>Thanks to projects like Hubble AI, Space & Time, and OKX DEX for supporting this research.</i></p>

    <h2>Introduction: The Synergy of Memes, High-Performance Blockchains, and AI</h2>
    <p>While infrastructure improvements drove transaction growth in the previous cycle, super applications like Pump.fun are now the new growth drivers. This model, with its streamlined issuance and sophisticated liquidity design, fosters a trading environment characterized by fairness and the potential for significant returns. This is reshaping user expectations, requiring not just faster access to opportunities, but also the ability to quickly acquire, analyze, and execute on multi-dimensional data. Existing data infrastructure struggles to meet these demands.</p>

    <p>This translates to a greater demand for lower-friction trading environments with faster confirmations and deeper liquidity. Trading is rapidly migrating to high-performance blockchains and Layer 2 rollups like Solana and Base. These chains handle transaction volumes ten times greater than previous Ethereum levels, posing data performance challenges for current providers. As new high-performance chains launch, the demand for on-chain data processing will grow exponentially.</p>

    <p>The maturation of AI further accelerates the potential for intelligent equity. Advanced AI models can now understand complex trading signals and execute them, even for novice users. Traders are increasingly relying on AI for trading decisions, driving the need for real-time, interpretable, and scalable data. AI is shifting from an "auxiliary tool" to a "central hub," further amplifying the need for faster and more insightful data processing.</p>

    <p>This convergence of meme coin trading, high-performance blockchains, and AI commercialization creates an urgent need for a new data infrastructure in the on-chain ecosystem.</p>

    <h2>Addressing the Data Challenges of 100,000 TPS and Millisecond Block Times</h2>
    <p>The rise of high-performance blockchains has ushered in a new era of data scale and speed.</p>

    <p>With widespread adoption of high-concurrency and low-latency architectures, daily transaction volumes easily exceed ten million, with raw data measured in hundreds of gigabytes. For instance, Solana's average daily transactions per second (TPS) exceeds 1,200, with over 100 million daily transactions. Solana's ledger data is growing rapidly, adding 80-95 TB annually, equivalent to 210-260 GB per day.</p>

    <p><img src="placeholder_chainspect_tps.png" alt="Chainspect 30-day average TPS" /></p>
    <p><i>Chainspect, 30-day average TPS</i></p>

    <p><img src="placeholder_chainspect_volume.png" alt="Chainspect 30-day transaction volume" /></p>
    <p><i>Chainspect, 30-day transaction volume</i></p>

    <p>Beyond throughput, block times have also reached the millisecond level. BNB Chain's Maxwell upgrade reduced block times to 0.8 seconds, while Base Chain's Flashblocks technology compresses it to 200 milliseconds. Solana plans to replace PoH with Alpenglow, aiming for 150-millisecond confirmations, while MegaETH targets 10-millisecond block times. These technological breakthroughs require unprecedented block data synchronization and decoding capabilities.</p>

    <p>However, most data infrastructures still rely on batch processing ETL pipelines, resulting in delays. Dune reports Solana contract interaction events experience delays of around 5 minutes, while protocol-level data can take up to 1 hour. This means transactions confirmed within milliseconds are delayed hundreds of times before becoming visible, unacceptable for real-time applications.</p>

    <p><img src="placeholder_dune_freshness.png" alt="Dune Blockchain Freshness" /></p>
    <p><i>Dune, Blockchain Freshness</i></p>

    <p>To address these challenges, some platforms have shifted to streaming architectures. The Graph compresses delays using Substreams and Firehose. Nansen has improved Smart Alerts and real-time dashboards by introducing streaming processing technologies like ClickHouse. Pangea aggregates community-provided computing, storage, and bandwidth to offer real-time streaming data to B-end users, like market makers, with delays under 100 milliseconds.</p>

    <p><img src="placeholder_chainspect_example.png" alt="Chainspect Example" /></p>
    <p><i>Chainspect</i></p>

    <p>On-chain transactions also exhibit uneven traffic distribution. Pumpfun's weekly transaction volume has varied nearly 30-fold. GMGN experienced multiple server overloads, forcing a database migration to TiDB for improved scalability and computational elasticity. This improved business agility by about 30% and alleviated peak-time pressures.</p>

    <p><img src="placeholder_dune_pumpfun.png" alt="Dune Pumpfun Weekly Volume" /></p>
    <p><i>Dune, Pumpfun Weekly Volume</i></p>

    <p><img src="placeholder_odaily_tidb.png" alt="Odaily TiDB Web3 Service Case" /></p>
    <p><i>Odaily, TiDB's Web3 service case</i></p>

    <p>The multi-chain ecosystem further complicates matters. Differences in log formats, event structures, and transaction fields require custom parsing logic for each new chain, challenging scalability. Some providers adopt a "customer-first" strategy, prioritizing chains with active trading.</p>

    <p>Data processing based on fixed-interval ETL will face delays, bottlenecks, and lags on high-performance chains. On-chain data infrastructure must evolve towards streaming incremental processing and real-time computing. Load balancing is essential to handle peak trading periods. This technical evolution is crucial for real-time queries and forms the battleground for the next generation of data platforms.</p>

    <h2>Speed is Wealth: The Paradigm Shift in On-Chain Data Competition</h2>
    <p>The core value of on-chain data has shifted from "visualization" to "actionability." In the past, Dune Analytics was the standard for on-chain analysis, providing researchers and investors with "understandable" data to piece together on-chain narratives.</p>

    <p>GameFi and DeFi users relied on Dune to track fund flows and calculate returns.</p>
    <p>NFT players analyzed transaction trends and whale holdings using Dune to predict market trends.</p>

    <p>Today, meme coin players are the most active users, driving applications like Pump.fun to generate revenue nearly double that of Opensea from the previous cycle.</p>

    <p>In the meme coin space, time sensitivity is paramount. Speed is no longer a luxury, it's a core factor determining profit. In primary markets priced by Bonding Curves, speed equals cost. Token prices rise exponentially with demand, making even a one-minute delay costly. Players pay a 10% slippage to enter blocks ahead of their competitors. This drives the pursuit of second-level data, same-block execution engines, and one-stop decision panels.</p>

    <p><img src="placeholder_binance_example.png" alt="Binance Example" /></p>
    <p><i>Binance</i></p>

    <p>Trading tools have evolved from manual slippage settings to automated sniper bots like BananaGun and integrated terminals like GMGN. This democratizes access to sophisticated trading strategies, and the competitive edge inevitably shifts to superior data. Those who can capture and act on signals faster gain a trading advantage.</p>

    <h2>Dimensions are Advantages: The Truth Beyond Candlestick Charts</h2>
    <p>Memecoins are the financialization of attention. Narratives drive attention, pushing prices higher. For traders, real-time data is important, but understanding the narrative, identifying key influencers, and predicting future attention are crucial. These insights are not visible on candlestick charts, but rely on multi-dimensional data: off-chain sentiment, on-chain addresses, and their relationships.</p>

    <h2>On-Chain × Off-Chain: The Closed Loop from Attention to Transactions</h2>
    <p>Users attract attention off-chain and complete transactions on-chain. The closed-loop data between these is a core advantage.</p>

    <h3>Narrative Tracking and Propagation Chain Identification</h3>
    <p>Tools like XHunt analyze KOL attention lists to identify individuals behind projects and potential propagation chains. 6551 DEX aggregates Twitter, websites, and listing records to generate real-time AI reports for traders, helping them capture narratives.</p>

    <h3>Sentiment Indicator Quantification</h3>
    <p>Infofi tools like Kaito and Cookie.fun aggregate content and perform sentiment analysis on Crypto Twitter, providing quantifiable indicators of Mindshare, Sentiment, and Influence. Cookie.fun overlays these indicators directly onto price charts.</p>

    <p><img src="placeholder_cookie_fun.png" alt="Cookie.fun Example" /></p>
    <p><i>Cookie.fun</i></p>

    <h3>On-Chain and Off-Chain are Equally Important</h3>
    <p>OKX DEX displays Vibes analysis alongside market data, aggregating KOL shout-out timestamps, Narrative Summaries, and comprehensive scores, shortening information retrieval time. The Narrative Summary is a well-received AI product feature.</p>

    <h2>Underwater Data Display: Turning "Visible Ledgers" into "Usable Alpha"</h2>
    <p>In traditional finance, order flow data is controlled by brokers and access requires paying substantial fees. In contrast, crypto's open trading ledgers effectively "open-source" valuable intelligence.</p>

    <p>The value of underwater data lies in extracting hidden intentions from transactions, including fund flows, market maker activity, KOL sub-account addresses, bundled trades, and abnormal fund flows. This also includes address profiling, tagging addresses as "smart money," "KOL," or "phishing" and linking them to off-chain identities.</p>

    <p>These signals significantly influence short-term trends. By real-time parsing address tags, holding characteristics, and bundled trades, trading tools reveal hidden dynamics, helping traders avoid risks and find alpha.</p>

    <p>GMGN integrates smart money, KOL addresses, wash trading, phishing addresses, and bundled trading analysis with real-time trading data, mapping on-chain addresses to social media accounts to align fund flows and risk signals.</p>

    <p><img src="placeholder_gmgn_example.png" alt="GMGN Example" /></p>
    <p><i>GMGN</i></p>

    <h2>AI-Driven Executable Signals: From Information to Profit</h2>
    <p>"The next round of AI is not about selling tools but about selling profits." - Sequoia Capital</p>

    <p>This is true in Crypto trading. Once data speed and dimensions meet standards, the focus shifts to data decision-making: converting data into actionable signals, measured by speed, automation, and excess return.</p>

    <p><b>Speed:</b> Advanced AI can integrate vast amounts of data, establish semantic connections, and extract decisive conclusions. Given the short signal lifespan, speed directly impacts returns.</p>

    <p><b>Automation:</b> AI can monitor and trade 24/7. Users can place Copy Trading buy orders with take-profit and stop-loss conditions. AI performs real-time monitoring and automatically places orders based on detected signals.</p>

    <p><b>Return:</b> The effectiveness depends on generating excess returns. AI must incorporate risk control to maximize risk-return ratios, considering factors like slippage losses and execution delays.</p>

    <p>This reshapes data platform business logic: from selling "data access" to selling "profit-driven signals." The focus shifts from data coverage to signal actionability.</p>

    <p>Emerging projects are exploring this. Truenorth incorporates "decision execution rates" into information effectiveness, optimizing output through reinforcement learning to minimize noise.</p>

    <p><img src="placeholder_truenorth_example.png" alt="Truenorth Example" /></p>
    <p><i>Truenorth</i></p>

    <p>AI faces challenges in generating actionable signals:</p>

    <p><b>Hallucinations:</b> Noisy on-chain data can cause LLMs to "hallucinate" or overfit when parsing queries, impacting accuracy. AI often fails to find the correct contract address or incorrectly associates AI discussions.</p>

    <p><b>Signal Lifetime:</b> The environment changes rapidly. Delays erode returns. AI must complete data extraction, reasoning, and execution quickly. Copy Trading can quickly become unprofitable if not aligned with smart money.</p>

    <p><b>Risk Control:</b> AI failures or excessive slippage can deplete the principal quickly.</p>

    <p>Balancing speed and accuracy using reinforcement learning, transfer learning, and simulation backtesting is crucial for AI in this field.</p>

    <h2>Upward or Downward? The Survival Choices of Data Dashboards</h2>
    <p>Applications that solely rely on data aggregation are facing a crisis. Whether stitching on-chain data into dashboards or layering execution logic with trading bots, they lack a sustainable advantage.</p>

    <p>Data providers must choose: deepen data acquisition and processing infrastructure (go downward), or extend to the application layer, controlling user scenarios (go upward). The middle ground will be squeezed.</p>

    <p>Going downward means building an infrastructure moat. Hubble AI shifted to upstream data processing to create "Crypto Databricks" after realizing TG Bots alone couldn't provide a long-term advantage. They optimized Solana's data processing speed and are transitioning to an integrated data research platform.</p>

    <p>Going upward means extending to application scenarios. Space and Time initially focused on sub-second SQL indexing, but began exploring C-end consumption with Dream.Space on Ethereum, where users write smart contracts or generate dashboards in natural language. This increases data service calls and forms direct user engagement.</p>

    <p>Therefore, roles relying on selling data interfaces are losing ground. The future will be dominated by infrastructure companies that control pipelines or platforms close to user decision-making.</p>

    <h2>Conclusion</h2>
    <p>The on-chain data track is undergoing a structural shift. Trading speed, data dimensions, and actionable signals have made "visible charts" no longer the core competitive advantage. The true moat is shifting towards "actionable signals" and "underlying data capabilities."</p>

    <p>In the next 2-3 years, the most attractive opportunities will emerge at the intersection of Web2-level infrastructure and Web3 execution models.</p>

    <p>We are optimistic about projects in two areas:</p>

    <p><b>Upstream Infrastructure:</b> On-chain data companies with streaming data pipelines, ultra-low latency indexing, and cross-chain unified parsing frameworks matching Web2 capabilities. These could become Web3 Databricks/AWS, with exponential growth and long-term value.</p>

    <p><b>Downstream Execution Platforms:</b> Applications integrating multi-dimensional data, AI Agents, and seamless trading. These could become the crypto-native Bloomberg Terminal, monetizing through excess returns.</p>

    <p>We believe these will dominate the next generation of crypto data.</p>

    <p><i>Click to learn about job openings at ChainCatcher</i></p>

    <p>Recommended Reading:</p>
    <ul>
      <li><a href="#">Exchange Listing Strategy Shift: The Rise of DEX Issuance and the New Pattern Dominated by Secondary Listings</a></li>
      <li><a href="#">Backroom: Tokenization of Information, a Solution for Data Overload in the AI Era? | CryptoSeed</a></li>
      <li><a href="#">Dialogue with Saros CEO Lynn Nguyen: After completing a $38 million buyback, how to break through in the Solana DEX track?</a></li>
    </ul>

    <p><i>ChainCatcher reminds readers to view blockchain rationally, enhance risk awareness, and be cautious of various virtual token issuances and speculations. All content on this site is solely market information or related party opinions, and does not constitute any form of investment advice. If you find sensitive information in the content, please click "Report", and we will handle it promptly.</i></p>
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