IOSG Weekly Report｜Are Data Infrastructures Ready for the Era of Crypto Super Apps?

Chainfeeds Guide:

Amid the triple resonance of the Meme frenzy, the explosion of high-performance blockchains, and the commercialization of AI, the on-chain data sector is undergoing a structural turning point. The iteration of trading speed, data dimensions, and execution signals means that visible charts are no longer the core competitiveness. The real moat is shifting towards executable signals that help users make money and the underlying data capabilities that support all of this.

Source:

Author:

IOSG Ventures

Opinion:

IOSG Ventures: In the previous cycle, the growth of on-chain trading mainly relied on infrastructure iteration. However, as the underlying layers gradually mature in the new cycle, super applications represented by Pump.fun are becoming new growth engines. These applications, with unified issuance mechanisms and liquidity design, create fair, primitive, and frequent wealth-generating trading scenarios, significantly changing users' return expectations and trading habits. Meanwhile, market demands for trading environments are rapidly increasing: lower friction, faster confirmation, and deeper liquidity. Trading venues have largely migrated from Ethereum to high-performance blockchains such as Solana and Base, as well as Layer2 Rollups, with trading volumes now more than 10 times higher than in the previous cycle. For example, Solana’s average daily TPS in the past 30 days has exceeded 1,200, with daily transactions surpassing 100 millions, and ledger data expanding at a rate of 80-95 TB per year. In the second half of this year, Solana plans to reduce confirmation time to 150ms through Alpenglow, while the new generation blockchain MegaETH aims for 10ms real-time block production. However, downstream data infrastructure still mostly relies on batch ETL processing, resulting in significant delays. For instance, on Dune, Solana contract interaction events are typically delayed by 5 minutes, and protocol-level aggregated data may even require a 1-hour wait, which is completely out of sync with the sub-second confirmation speeds on-chain and cannot meet the needs of high-frequency trading. This latency issue is forcing data platforms to explore real-time streaming architectures and stronger decoding capabilities. During the Meme trading frenzy, the market’s sensitivity to speed has been amplified to the extreme. The core competition for traders is no longer just cognitive advantage, but millisecond-level execution efficiency. In the primary market with Bonding Curve pricing, token prices rise exponentially with buying demand, and even a one-minute delay can result in entry costs differing by several times. Research shows that the most profitable players are often willing to pay a 10% slippage to secure a spot in the first three blocks. This wealth effect drives continuous iteration of trading tools: from manual slippage and gas settings in the Uniswap era, to automated execution with BananaGun sniper bots, to real-time pool notifications from PepeBoost, and finally to GMGN, which integrates multi-dimensional market data, candlestick charts, and trade execution into a unified terminal, dubbed the Bloomberg Terminal for Meme trading. As tool barriers lower, the competitive frontier shifts to the data itself: whoever can capture signals faster can gain an edge in the rapidly changing market. The success of Pump.fun exemplifies this logic, with cumulative revenue surpassing $700 million, nearly double that of last cycle’s consumer leader OpenSea. Speed has shifted from an option to a matter of life and death, becoming the biggest dividend distribution mechanism in the Meme market. The essence of Memecoin is the financialization of attention, with narrative and traffic as its price drivers. Candlestick charts can only show the surface, while real trading advantages come from the integration of multi-dimensional data: off-chain sentiment, on-chain holdings, and their precise mapping. Today’s trading assistant tools are bridging “on-chain × off-chain” to form a closed loop. For example, XHunt can analyze which KOLs are following a certain token, while 6551 DEX integrates Twitter, official websites, and trading data to generate real-time AI reports. Sentiment indicators are also being quantified; Cookie.fun overlays sentiment data onto price charts, making off-chain sentiment a new technical indicator. A deeper layer is the analysis of “underwater data,” that is, deducing invisible intentions from publicly available ledger transactions: capital flows, whale accumulation, KOL alt account operations, chip concentration, etc. GMGN maps smart money, VC addresses, developer wallets, and sandwich attacks to social accounts, helping users avoid risks and find Alpha in second-level market moves. The openness of crypto ledgers makes them equivalent to open-source order flow data, providing huge space for real-time mining. As competition evolves, the proposition of on-chain data has shifted from visualization to executability. The real advantage lies in whether one can perceive the driving forces beneath the surface, forming the core barrier for traders.