Andrew Ng's year-end summary: 2025 is the dawn of the AI ind

Andrew Ng's year-end summary: 2025 is the dawn of the AI industrial era.
 

2025 is coming to an end.

Readers who follow the AI circle know that this year has been a year of intense competition among AI giants, a year of frequent talent wars and organizational restructurings, a year of an extremely fierce arms race in large models, and a year of rapid development in AI infrastructure construction...

At the end of this remarkable year, our old friend, Professor Andrew Ng, a visiting professor of computer science at Stanford University, former head of AI at Baidu, and former head of Google Brain, released his annual tradition: a letter and an annual summary of the artificial intelligence field in 2025.

 

Dear friends,

Once again, AI has advanced at an astonishing pace, creating unprecedented software development opportunities for everyone, including newcomers to the field. In fact, one of the biggest challenges many companies face today is finding enough engineers who truly understand AI.

Every winter holiday, I set aside some time to learn and build projects hands-on, and I hope you will do the same. This not only helps me refine my existing skills and acquire new knowledge but also significantly promotes your technical career development.

To truly have the ability to build AI systems, I suggest you do three things:

Systematically study AI courses

Continuously build AI systems hands-on

(Optional) Read research papers

Now, let me explain why these three points are so important.

I often hear some developers advise others, "Don't study; just start doing it." This is very bad advice! Unless you are already in a community of experienced AI developers, starting without understanding the basics of AI can easily lead you to reinvent the wheel or, even worse, make a mess of it. 

For example, in interviews, I've seen many candidates reinvent a standard RAG document segmentation strategy, re-implement mature Agentic AI evaluation methods, and write chaotic and hard-to-maintain LLM context management code. If they had taken a few relevant courses in advance, they would have a better understanding of which "building blocks" already exist in the industry. They could still choose to implement these modules from scratch or even invent better methods than the existing ones, but at least they could avoid wasting weeks going down the wrong path.

Therefore, structured learning is crucial.

To be honest, I personally find taking courses very interesting. Instead of watching Netflix, I'd rather open a course by an excellent AI instructor to learn at any time.

At the same time, just taking courses is not enough. There is a lot of important experience that can only be truly learned through hands-on practice. Learning the theory of how an airplane works is, of course, very important for becoming a pilot, but no one has ever learned to fly an airplane just by taking courses. At some point, actually sitting in the cockpit is essential! The good news is that with the emergence of highly intelligent programming assistants, the threshold for hands-on building has never been lower. And when you start learning about the various building blocks of AI, they often inspire new ideas about "what else can be done." If I can't find inspiration for a project for a while, I usually take a few courses or read some research papers. After persevering for some time, I always come up with a lot of new project ideas. And, to be honest, I find "making things" really fun, and I hope you can also experience this joy!

Finally, not everyone has to do this, but I've noticed that the strongest candidates in the job market these days almost always read research papers occasionally. Although in my opinion, papers are much harder to digest than courses, they contain a lot of cutting-edge knowledge that hasn't been translated into a more understandable form. I would prioritize reading papers after courses and practice, but if you have the opportunity to improve your paper-reading ability, I still strongly recommend that you do so. (You can also watch a video I made before about how to read papers.) Taking courses and building hands-on are fun for me, while reading papers is more like a "grind," but the occasional insights from papers are truly rewarding.

I wish you a wonderful winter vacation and a happy new year. Besides learning and creating, I also hope you can spend more time with your family - that's also very important!

Love,

Andrew