Story Rebrands as DATA Foundation, Integrates Kled to Expand AI Data Infrastructure Push

Joint interview with CEO Andrea and CDO Avi

Story changes its name to DATA Foundation

Integrates with Kled to expand AI data business

"Proving data provenance will determine AI competitiveness"

Expands ecosystem with South Korea as a key market

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Story, an AI-based intellectual property blockchain infrastructure project, has rebranded as the DATA Foundation and integrated with consumer data platform Kled as it moves into the AI data infrastructure market.

The rebranding also reshapes the leadership team. Andrea Muttoni, formerly Story’s president and chief product officer, has been appointed chief executive officer of the DATA Foundation to oversee the business.

Avi Patel, founder and CEO of Kled, which the company describes as the world’s largest consent-based human data marketplace, has joined the DATA Foundation as chief data officer to lead the buildout of its core data infrastructure. Story co-founder Lee Seung-yoon has moved to affiliated incubator Poseidon as chief strategy officer and board chairman, where he will focus on new businesses.

The following is a Q&A with Andrea, the DATA Foundation’s new CEO, and Patel, its CDO, as the organization expands its ecosystem with South Korea as a strategic hub.

Q. Story has changed its name to the DATA Foundation and carried out a broad integration with Kled. What was the main reason for the rebranding and integration, and what is the goal?

A. (Andrea/Patel) Story’s underlying technology has always centered on provenance: proving who created something and whether they were compensated fairly. As AI has advanced, that issue has become most critical in AI training data. In the race toward artificial general intelligence, frontier labs are confronting the same question: Can they prove that data was collected with consent and that compensation was paid?

The rebranding to the DATA Foundation clarifies what the company says has always been its identity: a trust layer for AI data supply. Kled will serve as a consumer engine for collecting consented data at scale, while the DATA Foundation provides the verification and settlement network that makes that data legally defensible. The goal is to become the base infrastructure for lawful AI data.

Q. Story had previously focused on an intellectual property network. What prompted the shift toward AI data infrastructure?

A. (Andrea) The turning point was the realization that intellectual property and AI training data share the same core problem. Both come down to proving origin, rights, usage and compensation. In AI, the hardest challenge is no longer the model itself. As copyright and privacy lawsuits surge and scraping approaches its limits, trusted, consented and auditable data supply has become the market’s most urgent issue. That is why the focus has narrowed to AI data infrastructure, which he described as the most important IP issue of this era.

Q. What will be the DATA Foundation’s main business focus going forward? And how severe is the data bottleneck now facing frontier AI labs?

A. (Andrea) The data bottleneck is far more severe than many assume, and a concrete limit is close. Recent research from Epoch AI suggests the world is nearing a so-called data wall, with roughly 300 trillion tokens of high-quality human text data at risk of being largely exhausted between 2026 and 2032.

The next generation of AI, including robotics and physical AI, also depends entirely on real-world human data that does not exist on the internet and therefore cannot be scraped. The problem is no longer simply a shortage of data. Specialized data that can be used legally and safely is running out. The company plans to expand through Trace and ecosystem apps into a verifiable data infrastructure layer designed to ease that bottleneck.

Q. You said indiscriminate data scraping is reaching its limit. How can Trace, which was officially unveiled this time, address that problem?

A. (Andrea) Scraped data carries too much legal risk because rights and compensation cannot be proven. Trace is designed to remove that opacity by serving as a public audit layer. A lab that receives data can enter a unique hash ID for a single file and, within seconds, directly verify the user’s terms-of-service consent history, compliance check results, anonymized know-your-customer proof, and the full record of transparent compensation to contributors.

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Q. Kled has now been integrated with the DATA Foundation. Can you explain what Kled is?

A. (Avi) Kled is a marketplace app that lets anyone upload personal data and license it to AI companies for uses such as model training. It now has more than 400,000 users uploading more than 5 million data items a day, and its top earner makes as much as $7,400 a month. Through the integration with the DATA Foundation, that large volume of data now has a trust framework that allows it to be audited safely and transparently.

Q. Kled drew 200,000 contributors within two weeks of launch, and daily uploads have reached as many as 4.5 million files. What drove so many people to provide data voluntarily?

A. (Avi) The key was building trust through privacy protection and compliance. The company built an advanced processing pipeline called Kled FD-0.1 so users can contribute data with confidence. All uploaded data passes through that pipeline before it is licensed to labs, and sensitive personally identifiable information — including faces, government-issued IDs, bank account numbers and addresses — is fully anonymized. Participation surged because users believed their personal information would be protected and that they would receive fair compensation through a transparent process.

Q. Kled’s data is in especially high demand in robotics and physical AI. Why do global companies need it so badly?

A. (Avi) Humanoid robots and similar systems need first-person-view data of people performing everyday tasks such as cooking, cleaning and driving to understand how humans interact with the physical environment. They also need multimodal data that combines vision, audio and human behavior to understand context. AI companies want distinctive data that captures the authenticity of human behavior, something synthetic data cannot fully replicate. Patel said that has made it one of the rarest and most expensive resources in AI today.

Q. The DATA Foundation’s blockchain-based provenance technology is now paired with Kled’s vast pool of consented user data. What does each side gain?

A. (Joint answer) AI companies gain access to high-quality data that scraping cannot provide. They can verify the legality of that data within seconds and sharply reduce legal and reputational risks, including copyright lawsuits. Individual users can remain anonymous while receiving fair compensation for voluntary contributions, backed by transparent on-chain proof. The goal is a fair data economy in which value returns to the actual creators.

Q. Story co-founder Lee Seung-yoon has moved to affiliate Poseidon. How will the DATA Foundation and Poseidon generate synergies?

A. (Andrea) Poseidon, led by Lee as CSO, is building what he described as killer applications that create real business value on top of the DATA network. Poseidon’s app Numo collects and refines data through a very large user base, including the 30 million users of Toss.

He also pointed to chief scientist Sandeep Chinchali, who holds a PhD in computer science from Stanford University and researched robotics at NASA’s Jet Propulsion Laboratory. Chinchali, now a professor at the University of Texas at Austin, leads a deep-tech team that processes real physical AI data using swarm robotics and edge computing. Andrea said the ecosystem aims to become AI deep-tech infrastructure rather than a simple blockchain project. Demand for collection and specialized processing, he added, will combine to drive a sharp increase in the network’s on-chain activity.

Q. You said providers that can clearly prove data provenance will have the competitive edge over the next decade. How will the AI data market change, and what role will blockchain play?

A. (Avi) Blockchain’s disruptive power in this market is not about running AI models on-chain. It is about preserving, immutably and at scale, the history of a dataset’s origin, rights and whether it has been altered as billions of pieces of private data move through the system. Just as every financial institution today relies on rigorous standardized accounting ledgers, major AI companies will soon rely on standardized data provenance systems. The core of the paradigm the DATA Foundation wants to lead is infrastructure that can prove data integrity with mathematical certainty.

Q. You announced that the existing $IP token will be converted into the $DATA token on a 1:1 basis. What should participants be most excited about?

A. (Andrea) The core of the migration is aligning the token’s utility with the network’s real purpose: enabling a verifiable data economy. The DATA token ties together this large ecosystem. Participants should focus not on speculation but on actual network usage. As demand rises for AI data collected legally through Kled, Numo and other channels, the volume of on-chain transactions generated by verification and settlement will rise in parallel. The company is aiming to build a practical, large-scale AI infrastructure network that creates real economic value rather than speculative demand.