Yijia Shao
@EchoShao8899
CS Ph.D. student @StanfordNLP. Previous: undergraduate @PKU1898.
🚨 70 million US workers are about to face their biggest workplace transmission due to AI agents. But nobody asks them what they want. While AI races to automate everything, we took a different approach: auditing what workers want vs. what AI can do across the US workforce.🧵
Can AI actually code for us? 🧵 MIT research reveals there’s a "long way to go" due to bottlenecks like assessment, codebase scale, & incorrect retrievals. The work reflects a vision to let humans focus on high-level design while routine work is automated:…
After sharing our preprint on the Future of Work with AI Agents, we received strong interest in the WORKBank database. Today, we’re excited to release it publicly—along with a visualization tool to explore occupational and sector-level insights🧵
To complement the paper, authors have released an awesome visualization tool Check it out here: futureofwork.saltlab.stanford.edu/data-explorer
41% of YC AI startups are solving tasks workers don't need automated New Stanford study shows workers actually DO want AI, but for repetitive work that frees them up for higher value tasks Startups are chasing full automation where partnership would work better
Some updates 🚨 I finished my Ph.D at @uwcse in June 2025! After a year at AI2 as a Research Scientist, I am joining CMU @LTIatCMU & @mldcmu (courtesy) as an Assistant Professor in Fall 2026. The journey, acknowledgments & recruiting in 🧵
Thinking Machines Lab exists to empower humanity through advancing collaborative general intelligence. We're building multimodal AI that works with how you naturally interact with the world - through conversation, through sight, through the messy way we collaborate. We're…
AI agents are advancing fast, but how to generate real economic and societal value remains unclear. While decisions are often driven by tech capability, we center workers - they’re most impacted and the foundation of the economy. Thanks @StanfordHAI for writing about our work!
What do workers want from AI? Researchers from @StanfordHAI and @DigEconLab undertook a comprehensive study involving U.S. workers and AI experts. Here's what they found: stanford.io/3IsmHfg
come hang out at our poster tomorrow (tuesday) at 11am let's have some fun and savor the ai4code hype! 🎉 🥳 📌 East Exhibition Hall A-B #E-605
📢 Excited to share our new paper: Challenges and Paths Towards AI for SWE We discuss: 🛠️ 6 sub-tasks needed for SWE 🤖 9 challenges of today's AI in SWE 🔮 9 future directions to address the challenges w/ collaborators from MIT, Berkeley, Cornell, Stanford, and UPenn ⬇️ (1/n)
It's incredibly important to not only automate the jobs people actually want automated, but also to build benchmarks that show how well models are doing at that work. That’s why industry-specific benchmarks matter. If we can understand model accuracy on real tasks, we can…
🚨 70 million US workers are about to face their biggest workplace transmission due to AI agents. But nobody asks them what they want. While AI races to automate everything, we took a different approach: auditing what workers want vs. what AI can do across the US workforce.🧵
Extraordinary work from @EchoShao8899 and the kind of work that can help shape policy in an extremely fast moving AI world🚀🚀🚀. This are the kind of studies we need the most and huge congrats to Yijia and the team.
🚨 70 million US workers are about to face their biggest workplace transmission due to AI agents. But nobody asks them what they want. While AI races to automate everything, we took a different approach: auditing what workers want vs. what AI can do across the US workforce.🧵
Are AI scientists already better than human researchers? We recruited 43 PhD students to spend 3 months executing research ideas proposed by an LLM agent vs human experts. Main finding: LLM ideas result in worse projects than human ideas.
It’s released 1.5 years ago, prior to all Deep Research releases 🤣 We are also believers of open source which allows people to understand what agentic systems actually are and build & customize on!
Holy sh*t… Stanford just launched an AI tool that writes research papers like a PhD. • 99% factual accuracy • Cited sources • Completely free Here’s how to use it:
Verrrrry intriguing-looking and labor-intensive test of whether LLMs can come up with good scientific ideas. After implementing those ideas, the verdict seems to be "no, not really."
We @a16z just launched the third batch of Open Source AI Grants (cc @mbornstein) 🎉 This round includes projects focused on LLM evaluation, novel reasoning tests, infrastructure, and experimental research at the edge of capability and cognition: • SGLang: High-performance LLM…
Future of Work with AI Agents Stanford's new report analyzes what 1500 workers think about working with AI Agents. What types of AI Agents should we build? A few surprises! Let's take a closer look: