Aria at the House of Lords 🇬🇧 We’re joining a panel on AI and the Creative Industries next week in London hosted by @WIRED Summer Lab and @aifaventures. Aria’s @PalomarRo will speak alongside leaders from music, fashion, film, and art.

<Story, 새로운 동력을 얻다> 콘텐츠 산업을 IP 토큰화를 통해 재편하고 있는 @StoryProtocol 에 새로운 인물이 합류했습니다. 바로 UT Austin의 교수이자 AI·로보틱스·분산 시스템 전문가인 샌딥 친찰리 입니다. 샌딥 친찰리는 NASA 연구원 출신으로, 스탠퍼드에서 생성형 AI와 클라우드 로보틱스를 연구했으며, 현재는 자율주행 및 분산형 머신러닝 모델을 다루는 UT 오스틴에서 교수로 재직 중입니다. 연구 과정에서 차량에 직접 블랙박스를 설치해 현실 데이터를 수집하고, 그중에서도 ‘롱테일(long-tail)’로 불리는 희귀한 장면들을 분석해왔습니다. 이 데이터를 라벨링하고, 딥러닝용 TPU 하드웨어에서 경량 AI 모델을 직접 학습시키며, 데이터의 품질과 희소성이 얼마나 중요한지 깊이 체감했습니다. 그러던 중 그는 스스로에게 질문을 던졌다고 합니다. “AI가 현실에서 제대로 작동하려면, 단지 모델이 아닌 퀄리티 있는 데이터가 필요하다. 그리고 그 데이터를 자발적으로 모으기 위해선 효과적인 인센티브 구조가 필요하다.” 그리고 그 해답을 Story에서 찾았습니다. @StoryProtocol 는 데이터를 단순한 리소스가 아니라 IP로 정의하고, 온체인 보상 시스템을 구축 중입니다. 희귀한 데이터 수집 → 라벨링 → 합성 → 온체인 등록 → 로열티 분배 모든 과정을 체인 위에서 투명하게 추적합니다. 산디프 교수는 이를 다음과 같이 설명합니다. “내가 대시캠으로 찍은 희귀한 운전 장면을 Story에 등록하고, 친구가 라벨을 붙입니다. AI가 이를 바탕으로 합성 데이터를 만들면, 그 과정마다 연결된 IP가 생성되고, 로열티는 자동으로 모든 기여자에게 분배됩니다.” 샌딥 친찰리 교수는 Story의 Chief AI Officer로서 AI 전략 전반, 온체인 학습 데이터 인프라, 분산된 데이터 보상 시스템 설계를 주도하게 됩니다. 그리고 그는 데이터의 가치를 이렇게 정의합니다. “데이터야말로 새로운 IP입니다.”

We asked the @SPChinchali about the role of IP in the age of AI. "Essentially all public data sets have a non-commercial license. "Sometimes generative AI models that create imagery could have been trained on IP unsafe data and they cannot even use synthetic data." "What @StoryProtocol is really focusing on is the next generation of foundation models." "You can't just scrape that data on the internet."

A team of AI agent dev team

<The Next Chapter Begins: Welcoming Sandeep Chinchali as Chief AI Officer> Last week, we shared Story’s Chapter 2. Our vision is to build the AI-native infrastructure for the seventy trillion dollar IP economy. Today, that vision moves forward. I’m excited to welcome @SPChinchali as our Chief AI Officer. When I first saw Sandeep’s background (Stanford PhD, NASA JPL, UT Austin professor, early engineer at a startup acquired by VMware), I expected an interdeeply technical, academic profile. What stood out was how clearly he connected his expertise to what actually needs to be built. He has spent over a decade working on one of AI’s hardest problems: how to capture and structure the right real-world data to train intelligent systems. He has collected and labeled long-tail data, trained models on edge devices, and faced the same limitations AI labs now encounter. His belief in crypto came from firsthand experience. He saw it as the only viable foundation for incentives, provenance, and coordination at scale. Over time, that conviction became sharper. After years of working on these problems in robotics labs, in classrooms, and at startups, Sandeep came to a clear conclusion: solving them requires something purpose built. When he saw what we had built at Story, it clicked instantly. This was the missing piece. And he brings people with him. One of the things I noticed early on in our conversations is how often he talks about his students. Not just in passing, but with real pride and curiosity. He references what they’re building, what excites them, what questions they’re asking. And somehow, those stories always loop back to what we’re working on at Story. That habit of weaving ideas across people and disciplines isn’t performative. It’s just how he thinks. That curiosity and openness are part of what make him such a powerful communicator. He doesn’t reduce complexity for the sake of accessibility. He builds bridges. Whether he’s talking to researchers, builders, or partners, he meets people where they are and brings them forward. It’s exactly the kind of mindset that fits the moment. The direction of AI is increasingly clear. The competition is no longer about compute or model design. Compute is concentrated. Models are cloned within weeks. What remains scarce is data: especially IP-cleared, real-world data that cannot be scraped or simulated. In the past few months, leading AI teams have come to us looking for infrastructure that makes this kind of data usable. They want systems where provenance, permissions, and royalty flows are embedded from the start. Story is the only infrastructure specifically designed to enable this at scale. We register real-world data as IP, track its full lifecycle from capture to annotation to synthetic generation, and route royalties across all contributors. To lead this effort, we needed more than technical expertise. We needed someone who could match the scale and ambition of what we’re building: someone with the rare combination of deep systems thinking, hands-on experience, and genuine curiosity. That is Sandeep. He has built across robotics, machine learning, and decentralized infrastructure. Just as importantly, he brings openness and intellectual generosity that makes him a connector. He listens closely, draws insights across domains, and grounds everything in the bigger picture. That combination of rigor, clarity, and purpose is exactly what this moment calls for. Sandeep will lead Story’s AI strategy across research, product, and ecosystem development, including the launch of new primitives for decentralized data and training. This is an important step forward for Story. We believe data IP will define the next era of AI. With Sandeep on board, that future is within reach. Let’s build. More to come next week.

I’ve spent my career chasing one question: How do we gather the right data to make AI work in the real world? From Stanford labs to UT Austin classrooms, I searched everywhere. The answer isn’t another AI lab, but a blockchain built to treat data as IP. That’s why I am joining @StoryProtocol as their Chief AI Officer. At Stanford, I studied “cloud robotics,” how fleets of robots could use distributed compute to learn together. I even mounted a dashcam in my car to solve this: If robots could only upload 5–10% of what they see, how do we pick the most valuable data? Most of it was boring freeway footage. But <1% captured rare scenes: self-driving Waymos, construction sites, unpredictable humans. That “long-tail” data made models smarter. I hand-labeled it, even paid Google Cloud’s labeling service to annotate my footage with niche concepts like “LIDAR unit” and “autonomous vehicle”, and trained models that ran on a USB-sized TPU. But academia only goes so far. At UT Austin, my questions shifted: → How do we crowdsource rare data to improve ML? → What incentive systems actually work? That pulled me into crypto – blockchains, token economies, even DePIN. I blogged, wrote papers on decentralized ML, but still wondered: who’s actually building this infrastructure? By total chance, I met the Story team. I was invited to give a talk at their Palo Alto office. It was 6PM, room still packed. I rambled about “Neuro-Symbolic AI” and ended with a slide called “A Dash of Crypto.” That talk turned into an advisory role, which now turned into something much bigger. We’re at a pivotal moment. Compute is mostly solved. Model architectures are copied overnight. The real moat is data. Not scraped Reddit. Not endless language. But rights-cleared, long-tail, real-world data that trains embodied AI – robots, AVs, systems that navigate our messy world. Imagine this: I capture a rare driving scene on dashcam & register it on Story. A friend labels it. An AI agent creates synthetic variants. On Story’s graph-structured chain, each becomes linked IP. Royalties flow back automatically. Everyone gets paid, every step traceable on-chain. That’s why I’m now Chief AI Officer at Story building the rails for decentralized, IP-cleared training data. It’s time to make data the new IP. Story is the place to do it. Much more to come soon. Let’s go.

Robotics has been a rapidly evolving field, experiencing significant progress in recent years, particularly in conjunction with the recent advancements in VLMs. This makes me wonder how the ownership of robots should be handled.

Data is IP's most valuable asset. @StoryProtocol is rewriting IP infrastructure to accelerate the next wave of AI innovation. From training data to IP RWAs, Chapter 2 is here, and it’s just getting started. 🧵⤵️

Wait until what this partnership further brings

A company that is an infrastructure for compute just hit $4T market cap. What about an infrastructure that gives data property rights by registering and licensing them? Turning data into IP?