
Andrew Ng
Founder of DeepLearning.AI, Managing General Partner at AI Fund, and Executive Chairman at LandingAI
About
I'm Andrew Ng, and I've spent my career working to make AI accessible to everyone. Currently, I wear a few hats: I lead DeepLearning.AI to provide world-class education, run AI Fund to build new startups from the ground up, and serve as Executive Chairman of LandingAI. My journey has taken me from leading Google Brain and Baidu’s AI group to teaching at Stanford, where I still serve as an Adjunct Professor. I am deeply passionate about 'Agentic Workflows'—the idea that AI should move beyond simple prompts to iterative reasoning—and I'm currently focused on helping companies move past the 'Product Management bottleneck' to realize real value from AI. Whether you are a subject matter expert looking to build a company or a leader trying to navigate the shift to Sovereign AI, I’m always looking for ways to collaborate and let a thousand flowers bloom in the AI ecosystem.
Networking
What I can offer
- ›Venture building expertise via AI Fund
- ›Strategic AI advisory for CEOs
- ›Deep technical insights into LLMs and robotics
- ›Educational resources for scaling AI literacy
Looking for
- ›Subject Matter Experts (SMEs) in healthcare, logistics, and finance to build startups
- ›CEOs ready for top-down workflow redesign
- ›Partnerships to expand AI education
Best fit for
Current Interests
Background
Career
Academic leader at Stanford turned tech executive at Google Brain and Baidu, now a venture builder and educator.
Education
PhD in Computer Science from UC Berkeley (2003), MS in EECS from MIT (1998), BS in Math/CS from Carnegie Mellon (1997).
Achievements
- ›Co-founded Coursera, serving 100M+ learners.
- ›Founded Google Brain and led the 'Google Cat' milestone.
- ›Scaled Baidu's AI group to 1,300 people.
- ›Launched Context Hub (chub) with 6,000+ GitHub stars in one week.
- ›Developed the Robot Operating System (ROS).
Opinions
- AI won't replace workers, but workers who use AI will replace those who don't.
- AGI is hyped; we should focus on the 'Turing-AGI Test' of economically useful work.
- Data center environmental concerns are often overstated compared to their efficiency gains.
- Open-weight models are essential to prevent regulatory capture.