Sandeep Chinchali
@SPChinchali
Chief AI Officer at Story Protocol. UT Austin Professor. Work on Generative AI and Networked Intelligence. Stanford CS PhD. All views my own.
From Stanford to UT Austin, I’ve spent my career chasing one question: how do we gather and value the right data to make AI work in the real world? There are three battlegrounds in AI: Compute – Solved by Nvidia. Models – Rapidly commoditizing. Data – The last, unpriced…
AI is moving beyond the browser and into the real world. The bottleneck? Data. Today we’re announcing a $15M seed round led by @a16zcrypto to build infra that collects, curates, and licenses high-quality data for physical AI. Incubated by and built on @StoryProtocol.
Learn more about what IP-cleared data unlocks for AI companies, builders, and contributors. New, functional markets for high-quality, real-world data. Powered by Poseidon 🔱
The leading AI teams don’t just need high-quality data. They need data that’s IP-cleared and ready for commercial use. That’s what @psdnai makes possible by building on @StoryProtocol – every dataset is ready for commercial use and structured for integration into training…
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…
AI’s next frontier is not compute or larger models, it’s better data. Today, we’re bringing on one of the few people who actually spent his life solving that problem. Welcome @SPChinchali, our new Chief AI Officer. The frontier of AI is no longer defined by models with more…
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…
<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…
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…
excellent work on multi agent RL in collaboration with Honda at ICML @hgoel1000
TL;DR on Story Chapter 2 1) data is IP, AI needs data → data is IP's most valuable asset → AI needs training data to accelerate → Story is the licensing layer for training data think of Story as the rails for AI enterprises to easily pay for their training data. 2)…
Time series data is everywhere – powering decisions in energy, finance/crypto, health, and more. Yet ironically, our most celebrated AI tools stumble on it. LLMs have captured the tech world’s imagination (and for good reason, they’re powerful and flexible). But they’re not a…
LLMs are amazing at a lot of things - but time series forecasting isn’t one of them. In Synthefy's latest blog post, we explain why token-based architectures aren’t inherently designed for real-world time series tasks - like forecasting energy demand, retail trends, or medical…
Super exciting work by the @synthefyinc team. Check out the basic experiment - LLMs fail spectacularly at time series. @ShubhankarAgar3
LLMs are amazing at a lot of things - but time series forecasting isn’t one of them. In Synthefy's latest blog post, we explain why token-based architectures aren’t inherently designed for real-world time series tasks - like forecasting energy demand, retail trends, or medical…
LLMs are amazing at a lot of things - but time series forecasting isn’t one of them. In Synthefy's latest blog post, we explain why token-based architectures aren’t inherently designed for real-world time series tasks - like forecasting energy demand, retail trends, or medical…
Really excited about our new #CVPR2025 paper on evaluating text-to-video models using neuro-symbolic AI. Great work @MinkyuChoi7 and team!
(1/7) Excited to share our new paper on evaluating Text-to-Video (T2V) models! Current methods focus on visual quality but struggle with temporal fidelity and text alignment, crucial for applications like autonomous driving and robotics.