Story Rebrands as DATA Foundation, Integrates Kled in Push Into AI Data Infrastructure

Joint interview with CEO Andrea Muttoni and CDO Avi Patel

Story changes name to DATA Foundation

Integrates Kled to expand AI data business

"Proving data provenance will determine AI competitiveness"

Expands ecosystem with South Korea as a key hub

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Story, an AI-based intellectual property blockchain infrastructure project, has rebranded as the DATA Foundation and completed a broad integration with consumer data platform Kled as it pushes into the AI data infrastructure market.

The rebranding also reshapes the leadership team set to lead the ecosystem. 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 is described as the world's largest consent-based human data marketplace, has joined the DATA Foundation as chief data officer to lead development of its core data infrastructure. Story co-founder Lee Seung-yoon will move to affiliate Poseidon as chief strategy officer and chairman to focus on new business development.

The following is an edited Q&A with DATA Foundation CEO Muttoni and CDO Patel on the group's expansion strategy, with South Korea positioned as a key market.

Q. Story has changed its name to the DATA Foundation and integrated with Kled. What was behind the rebranding and merger, and what is the goal?

A. (Muttoni/Patel) From day one, Story's underlying technology has been about provenance: proving who created something and whether that person was fairly compensated. As AI advances, that question has become most critical in one area: training data. In the race toward artificial general intelligence, frontier labs are grappling with the same issue: whether they can prove data was collected with consent and that compensation was paid.

The shift to the DATA Foundation is meant to clarify what we have been building all along: a trust layer for AI data supply. Kled serves as the consumer-facing engine that gathers 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 core infrastructure for lawful AI data.

Q. Story had focused on an IP network. What drove the shift toward AI data infrastructure?

A. (Muttoni) We came to see that IP and AI training data ultimately present the same problem. Both revolve around proving origin, rights, usage and compensation. In AI, the hardest challenge is no longer the model itself. As copyright and privacy lawsuits mount and scraping approaches its limits, the market's most urgent issue has become a trusted, consented and auditable data supply. We narrowed our focus to AI data infrastructure because it is the defining IP issue of this era.

Q. What business areas will the DATA Foundation focus on going forward? And how severe is the data bottleneck facing frontier AI labs today?

A. (Muttoni) The data bottleneck is far worse than many assume, and a hard limit is drawing near. According to recent research from Epoch AI, the world is approaching a so-called data wall in which much of the global supply of high-quality human text data, about 300 trillion tokens, could be exhausted between 2026 and 2032.

Next-generation AI, including robotics and physical AI, also depends entirely on real-world human data that does not exist on the internet and cannot be scraped. The problem is no longer simply that there is not enough data. It is that specialized data that can be used legally and safely is running out. We plan to expand through Trace and ecosystem apps into a verifiable data infrastructure layer that addresses that bottleneck.

Q. You said indiscriminate scraping is reaching its limits. How does Trace, which was formally unveiled this time, solve that problem?

A. (Muttoni) Scraped data carries too much legal risk because it cannot prove rights or compensation. Trace removes that opacity by serving as a public audit layer. A lab that receives data can enter the unique hash ID of a single file and, within seconds, verify the user's acceptance of terms of service, compliance check results, anonymized know-your-customer proof and the full compensation record for contributors.

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Q. The DATA Foundation has integrated with Kled. What is Kled?

A. (Patel) Kled is a marketplace app that lets anyone upload personal data and license it to AI companies for uses including model training. More than 400,000 users currently upload over 5 million pieces of data each day, and the top earners make as much as $7,400 a month. Through the integration with the DATA Foundation, that large data pool now has a trust layer that allows it to be audited safely and transparently.

Q. Kled drew strong early traction, with 200,000 contributors in the first two weeks after launch and as many as 4.5 million files uploaded in a day. Why have people been so willing to contribute data voluntarily?

A. (Patel) The key is trust built on rigorous privacy protection and compliance. We built an advanced processing pipeline called Kled FD-0.1 so users can contribute data with confidence. Every upload passes through that pipeline before it is licensed to labs, and sensitive personal information, including faces, government-issued identification, financial account numbers and addresses, is fully anonymized. That assurance, that personal information is protected and compensation is paid through a transparent process, drove the strong participation.

Q. Kled's data is seeing particularly strong demand in robotics and physical AI. Why do global companies need it so badly?

A. (Patel) Humanoid robots and similar systems need first-person data showing how humans interact with the physical world while carrying out everyday tasks such as cooking, cleaning and driving. They also need multimodal data combining vision, audio and human behavior to understand context. AI companies want unique data that captures authentic human behavior and cannot be fully replicated with synthetic data. That has become one of the rarest and most expensive resources in the AI industry.

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

A. (Muttoni/Patel) 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 litigation. Individual users can remain anonymous while receiving transparent on-chain proof of fair compensation for voluntary contributions. The result is a fairer data economy in which value flows back to the actual creators.

Q. Story co-founder Lee Seung-yoon has moved to affiliate Poseidon. How will the DATA Foundation and Poseidon work together going forward?

A. (Muttoni) Poseidon, led by Lee as chief strategy officer, is building killer applications on top of the DATA network that generate real business value. Poseidon app Numo collects and refines data through massive user bases such as Toss's 30 million users.

The technology effort is led by Chief Scientist Sandeep Chinchali, who holds a PhD in computer science from Stanford University and studied robotics at NASA's Jet Propulsion Laboratory. He is also a professor at the University of Texas at Austin. His deep-tech team is processing real-world physical AI data using swarm robotics and edge computing. Our ecosystem aims to become real AI deep-tech infrastructure, not just a blockchain project. Demand for data collection and specialized processing will drive a sharp increase in on-chain network activity.

Q. You said suppliers that can clearly prove data provenance will have the edge over the next decade. How do you see the AI data market changing, and what role will blockchain play?

A. (Patel) Blockchain's disruptive potential in this market is not about running AI models on-chain. It is about preserving an immutable history of a dataset's origin, rights and any tampering at a scale where billions of pieces of private data are moving. Just as every financial institution today relies on rigorous standard accounting ledgers, major AI companies will soon rely on standardized systems for data provenance. Infrastructure that can mathematically prove data integrity is at the core of the paradigm the DATA Foundation aims to lead.

Q. You also announced that the existing IP token will be converted into the DATA token on a one-for-one basis. What should ecosystem participants be most excited about?

A. (Muttoni) The point of the migration is to align the token's utility with the network's real mission: enabling a verifiable data economy. The DATA token ties this broad ecosystem together. Participants should focus not on speculation but on actual network usage. As demand surges for legally collected AI data through Kled, Numo and other platforms, the volume of on-chain transactions generated by verification and settlement will rise in step. We want participants to look ahead to the launch of a large, practical AI infrastructure network that creates real economic value rather than speculation.