Letters from Andrew Ng

There are many great applications to be built on top of large language models, and the overhead of doing so may be lower than you think. Sometimes, I've spent all day on a weekend developing ideas only to find that I've spent less than $0.50.

The competitive landscape of large language models (LLMs) is evolving quickly. The ultimate winners are yet to be determined, and already the current dynamics are exciting. Let me share a few observations, focusing on direct-to-consumer...

Last week, the tech news site The Information reported an internal controversy at Google. Engineers were concerned that Google’s Bard large language model was trained in part on output from OpenAI’s ChatGPT, which would have violated OpenAI’s terms of use.

Generative AI is taking off, and along with it excitement and hype about the technology’s potential. I encourage you to think of it as a general-purpose technology (GPT, not to be confused with the other GPT: generative pretrained transformer).

Last week, Silicon Valley Bank (SVB), Signature Bank, and Silvergate Bank suddenly collapsed. If it passed uneventfully from your point of view, good for you! Many companies worked nonstop through the weekend scrambling to preserve funds so they could pay their employees.

ChatGPT has raised fears that students will harm their learning by using it to complete assignments. Voice cloning, another generative AI technology, has fooled people into giving large sums of money to scammers, as you can read in this issue of The Batch.

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.