Why Data Becomes the Most Valuable Resource, and Who Can Truly Unlock It Will Define the Next Decade
The history of technological development always reaches such nodes where the speed of innovation far exceeds the carrying capacity of infrastructure. We have experienced the overloaded Web1 era of dial-up internet, witnessed how video streaming quickly replaced traditional cable TV, and personally seen how cloud computing completely transformed software deployment and development methods.
Today, this phenomenon of infrastructure lag is happening again. And this time, the protagonist isdata.
Data: A Trillion-Dollar Asset Still Dormant
From AI to IP, and to various Web3 applications, data is gradually becoming the core resource driving the global economy. It is an asset class, a means of production, and a new form of economic organization. Morgan Stanley predicts that by 2032, the high-quality AI training data market will exceed $17 billion; while the overall data market size has already surpassed $3 trillion.
But ironically, this resource of enormous value is still mostly dormant today:
Trapped in closed platforms, unable to be discovered and used;
Scattered across systems with different structures, unable to be combined or reused;
Lacking an effective market mechanism for authorization, pricing, and circulation.
This is like the oil resources of the early 20th century - gold everywhere, but without refineries, gas stations, and logistics networks to truly transform it into circulating economic value.
AI's Bottleneck is Not in Algorithms, But in Data
Today, AI models increasingly demand high-quality structured data. However, the most valuable data resources are controlled by a few tech giants. Globally, approximately 95% of training datais controlled by five companies, and open data is often the result of web scraping, with high noise, high redundancy, and increasing legal risks.
This not only limits AI model performance but also traps the entire industry in a "bad money drives out good" dilemma:
Open-source models are forced to rely on low-quality training data; the problem with low-quality data is the difficulty in verifying its accuracy, and it often contains bias. Expanding AI with low-quality datasets is almost impossible.
Data producers lack incentives, further exacerbating data scarcity;
Legal disputes are frequent, and AI companies face enormous risks of unclear copyrights. These lawsuits occur because some large AI systems acquire training data without permission. Most people don't even know their data is being used to train AI. This contains enormous value, yet is extracted and monopolized by large tech companies.
Blockchain Storage Solutions: Too Many "Patches", Too Little "System"
Facing these issues, many solutions have tried to compensate for data infrastructure deficiencies. However, most are still "patchwork" short-term responses, lacking systematicity, integration, and sustainability. For example:
Ethereum Blob Space (EIP-4844): Provides only 18 days of temporary storage, potentially exhausting capacity by 2025;
Celestia: Achieved a "data availability layer" but does not support structured data combination and long-term storage;
Filecoin: Slow data retrieval, non-permanent, and smart contracts cannot directly call its stored data;
Arweave: Highly volatile storage prices, weak performance and verifiability, with AO computing layer dependent on centralized bridging;
Story Protocol and other IP projects: Focus on on-chain IP asset management, but lack deep integration with data networks and do not support other application builds;
Walrus: Deployed on other blockchains, high cost, limited functionality, non-permanent storage, and weak adaptability.
They each solve certain stages, yet cannot provide astructured, natively composable, and executable data infrastructure.
Celestia Raises $100 Million: Data Availability Becomes the Biggest Bottleneck for Rollups;
Story Protocol Raises $140 Million: IP Chaining is Imminent;
Ethereum Storage Cost Remains as High as $900,000/GB: Unsustainable;
AI Training Set Demand Rises Sharply, Supply and Demand Imbalance Continues to Expand;
AI Content Infringement Cases Increase by Over 200% Year-on-Year: Lack of Creator Rights Protection Mechanism.
This means a huge technological "gap" is opening up—a trillion-dollar data infrastructure vacancy, waiting for a true solver.
AWS of Web3, Establishing an On-Chain Data Flywheel
Looking back at AWS's success, the key was not a single technological advantage, butunifying computing power, databases, and applications, creating a positive flywheel.
Irys is replicating this logic on-chain:
User-uploaded data → Database protocol indexing → Authorization protocol monetization → Application calls → Feedback to more data production → Network value enhancement → Attracting more developers to build
Each new protocol strengthens the previous protocol's function, and each data call enhances the entire network's value.
This is not just a tool for "storing data" on-chain, but an entire scalable, composabledata operating system.
Data is the Most Important Asset of the Future, and Infrastructure Will Determine Who Wins It
We are standing on the eve of data infrastructure reconstruction. On one hand, data is rapidly becoming the most critical asset driving AI, content economy, and smart contract ecosystems; on the other hand, existing systems can no longer support this change.
What Irys is building is not just a faster or cheaper data storage system, but a trulyfuture-orientedinfrastructure network:
Natively supports data storage and retrieval;
Automated authorization and revenue distribution;
Supports multiple data needs such as AI, IP, and Web3 applications;
Can be easily accessed and reused by developers, creators, and enterprises.
If AWS captured the historical opportunity of "computing as a service" in the cloud computing era, thenIrys is standing at the beginning of a massive transformation of "data as a service".The door to the data economy has been opened, and the real question is—who will build the network that supports all of this. The answer is emerging.
