Letters from Andrew Ng

Last week, the United States Congress passed President Trump’s “Big Beautiful Bill.” I’m disappointed it didn’t include a proposed moratorium on U.S. state-level AI regulation.

I’d like to share a tip for getting more practice building with AI — that is, either using AI building blocks to build applications or using AI coding assistance to create powerful applications quickly.

On Monday, a United States District Court ruled that training LLMs on copyrighted books constitutes fair use.

One of the most effective things the U.S. or any other nation can do to ensure its competitiveness in AI is to welcome high-skilled immigration and international students who have the potential to become high-skilled.

There’s a new breed of GenAI Application Engineers who can build more-powerful applications faster than was possible before, thanks to generative AI.

Everyone can benefit by learning to code with AI! At AI Fund, the venture studio I lead, everyone — not just the engineers — can vibe code or use more sophisticated AI-assisted coding techniques.

I am alarmed by the proposed cuts to U.S. funding for basic research, analyzed here, and the impact this would have for U.S. competitiveness in AI and other areas.

In the age of AI, large corporations — not just startups — can move fast. I often speak with large companies’ C-suite and Boards about AI strategy and implementation, and would like to share some ideas that are applicable to big companies.

AI’s ability to make tasks not just cheaper, but also faster, is underrated in its importance in creating business value.

I’m delighted to announce that AI Fund has closed $190M for our new fund, in an oversubscribed round.

I hope we can empower everyone to build with AI. Starting from K-12, we should teach every student AI enabled coding, since this will enable them to become more productive and more empowered adults.

Even though I’m a much better Python than JavaScript developer, with AI assistance, I’ve been writing a lot of JavaScript code recently.

I’ve noticed that many GenAI application projects put in automated evaluations (evals) of the system’s output probably later — and rely on humans to manually examine and judge outputs longer — than they should.

I am so sorry that the U.S. is letting down our friends and allies.

Contrary to standard prompting advice that you should give LLMs the context they need to succeed, I find it’s sometimes faster to be lazy and dash off a quick, imprecise prompt and see what happens.