Letters from Andrew Ng

Will the future of large language models limit users to cutting-edge models from a handful of companies, or will users be able to choose among powerful models from a large number of developers?

As the winter holiday approaches, it occurs to me that, instead of facing AI winter, we are in a boiling-hot summer of AI.

The economic downturn of the past six months has hit many individuals and companies hard, and I’ve written about the impact of rising interest rates on AI. The effects of high inflation, the Russian war in Ukraine, and an economic slowdown in China are rippling across the globe...

The rise of AI over the last decade has been powered by the increasing speed and decreasing cost of GPUs and other accelerator chips. How long will this continue? The past month saw several events that might affect how GPU prices evolve.

When I wrote recently about how to build a career in AI, several readers wrote to ask specifically about AI product management: the art and science of designing compelling AI products. I’ll share lessons I’ve learned about this here and in future letters.

Many AI systems have been built using data scraped from the internet. Indeed, even the cornerstone dataset for computer vision research, ImageNet, was built using images taken from the public internet.

Last week, I wrote about how rising interest rates are likely to lead investors and other finance professionals to focus on short-term returns rather than longer-term investments.

The United States Federal Reserve Bank has signaled that it will continue to raise interest rates. As one consequence, the stock market is significantly down, particularly tech stocks, relative to the beginning of the year.

Last week, I described trends that AI Fund, the venture studio I lead, has seen in building AI startups. I'd like to discuss another aspect of building companies that’s unique to AI businesses: the controversial topic of data moats.

AI businesses differ from traditional software startups in important ways. For instance, technical feasibility isn’t always clear, product specification is complex, and data is necessary to train and test the system.

Last week, Elon Musk launched a surprise attempt to acquire Twitter. The $43-billion bid was motivated, he said, by his desire to protect free speech endangered by the company’s practice of promoting some tweets while burying others.

AI Fund, the venture studio and investment firm that I lead, recently held a summit where CEOs and founders of portfolio companies shared ideas on topics from fundraising to building team culture.

Last week, DeepLearning.AI invited a group of learners to our Palo Alto office’s courtyard. We had a good time chatting about paths into AI, career trajectories, applications people were working on, and challenges they were facing.

I’m writing this in Orlando, Florida, where I just spoke at the A3 Business Forum, a group that works to advance industrial automation through AI, robotics, and other tools. This was my first large conference since the pandemic started...

On Monday, Landing AI (where I’m CEO) announced the close of a $57 million Series A funding round. The investment enables the company to continue building its data-centric MLOps platform for computer vision, with a focus on manufacturing visual inspection.