2025年のトップストーリー！AI大手が人材を引き抜き、推論モデルが性能向上、エージェントがコード記述、データセンターがGDPを牽引、中国が逆転

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Another year of rapid AI advances has created more opportunities than ever for anyone — including those just entering the field — to build software. In fact, many companies just can’t find enough skilled AI talent. Every winter holiday, I spend some time learning and building, and I hope you will too. This helps me sharpen old skills and learn new ones, and it can help you grow your career in tech.

To be skilled at building AI systems, I recommend that you:

(Optionally) read research papers

Let me share why each of these is important.

I’ve heard some developers advise others to just plunge into building things without worrying about learning. This is bad advice! Unless you’re already surrounded by a community of experienced AI developers, plunging into building without understanding the foundations of AI means you’ll risk reinventing the wheel or — more likely — reinventing the wheel badly!

For example, during interviews with job candidates, I have spoken with developers who reinvented standard RAG document chunking strategies, duplicated existing evaluation techniques for Agentic AI, or ended up with messy LLM context management code. If they had taken a couple of relevant courses, they would have better understood the building blocks that already exist. They could still rebuild these blocks from scratch if they wished, or perhaps even invent something superior to existing solutions, but they would have avoided weeks of unnecessary work. So structured learning is important! Moreover, I find taking courses really fun. Rather than watching Netflix, I prefer watching a course by a knowledgeable AI instructor any day!

At the same time, taking courses alone isn’t enough. There are many lessons that you’ll gain only from hands-on practice. Learning the theory behind how an airplane works is very important to becoming a pilot, but no one has ever learned to be a pilot just by taking courses. At some point, jumping into the pilot's seat is critical! The good news is that by learning to use highly agentic coders, the process of building is the easiest it has ever been. And learning about AI building blocks might inspire you with new ideas for things to build. If I’m not feeling inspired about what projects to work on, I will usually either take courses or read research papers, and after doing this for a while, I always end up with many new ideas. Moreover, I find building really fun, and I hope you will too!

Finally, not everyone has to do this, but I find that many of the strongest candidates on the job market today at least occasionally read research papers. While I find research papers much harder to digest than courses, they contain a lot of knowledge that has not yet been translated to easier-to-understand formats. I put this much lower priority than either taking courses or practicing building, but if you have an opportunity to strengthen your ability to read papers, I urge you to do so too. (You can also watch an old video where I give advice on reading papers.) I find taking courses and building to be fun, and reading papers can be more of a grind, but the flashes of insight I get from reading papers are delightful.

Have a wonderful winter holiday and a Happy New Year. In addition to learning and building, I hope you'll also spend time with loved ones — that, too, is important!

2025 may be remembered as the dawn of AI’s industrial age. Innovations propelled model performance to new heights, AI-driven applications became indispensable, top companies battled over skilled practitioners, and construction of infrastructure drove the U.S. gross domestic product. As in past winter holiday seasons, this special issue of The Batch traces the major themes of the past 12 months. The coming year promises to consolidate these changes as we weave the technology more tightly into the fabric of daily life.

Thinking Models Solve Bigger Problems

Think step by step. Explain your reasoning. Work backwards from the answer. As 2025 began, models executed these reasoning strategies only when prompted. Now most new large language models do it as a matter of course, improving performance across a wide range of tasks.

What happened: Late last year, OpenAI introduced the first reasoning, or “thinking,” model, o1, which baked in an agentic reasoning workflow. In January, DeepSeek-R1 showed the rest of the world how to build such capabilities. The result: immediate improvements in math and coding performance, more accurate answers to questions, more capable robots, and rapid progress in AI agents.

Driving the story: An early form of reasoning took off with “Large Language Models Are Zero-Shot Reasoners,” the paper that introduced the prompt addendum, “let’s think step by step.” The authors found that manually adding these words to a prompt improved a model’s output. Researchers soon realized they could train this capability into models so they would