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🚨 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.🧵

🚀 We’re making the WORKBank database public and building an interactive data explorer! 👇 To get notified when it’s live or request an occupation we missed (see Appendix D.1 in our paper), drop a comment below. forms.gle/ocDWGhRDS8y6Qw…

This paper is a must-read for understanding future of work. The authors introduced a new framework + dataset (WORKBank) capturing what U.S. workers want AI agents to automate vs. augment. They found mismatches between desires and tech capability across 844 tasks!!!

Some tasks are painful to do. But some are fulfilling and fun. How do they line up with the tasks that AI agents are set to automate? Not that well, based on our new paper "Future of Work with AI Agents: Auditing Automation and Augmentation Potential across the U.S. Workforce"

Thanks Ethan Mollick for the discussion! While aligning models with individual feedback is more of a technical problem, building effective AI agents requires alignment at the workforce level--accounting for real work and societal impact. Blog summary: futureofwork.saltlab.stanford.edu

These gaps between humans want AI to do and what AI can actually do today is very real in my recent professional experience.

Listening to Mehran Sahami’s talk at Stanford CS Commencement Ceremony right now. I resonated a lot as the talk is centered around the discussion of automation vs. augmentation and human-centered technology 🤖👩👨

Big congratulations to Eric on receiving the Stanford University Arthur Samuel dissertation award today! Work on model editing, factuality, and direct preference optimization (DPO) CC Chelsea Finn

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

BREAKING: Stanford just surveyed 1,500 workers and AI experts about which jobs AI will actually replace and automate. Turns out, we've been building AI for all the WRONG jobs. Here's what they discovered: (hint: the "AI takeover" is happening backwards)

AI companions aren’t science fiction anymore 🤖💬❤️ Thousands are turning to AI chatbots for emotional connection – finding comfort, sharing secrets, and even falling in love. But as AI companionship grows, the line between real and artificial relationships blurs. 📰 “Can A.I.

That research was done extensively in 2020-2024 and even deployed in open source to tens of thousands of users. Nothing fundamentally changed from the *sum total* of that literature. (Yes indeed, including self-training/RL and benchmarking.) It’s a bummer that we need to

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:

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 Mike Bornstein) 🎉 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

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!

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.

Extraordinary work from Yijia Shao 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.