新たなオープンウェイトリーダー、巨大AIの政治的影響力、病気の予測、高速推論

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Will AI create new job opportunities? My daughter Nova loves cats, and her favorite color is yellow. For her 7th birthday, we got a cat-themed cake in yellow by first using Gemini’s Nano Banana to design it, and then asking a baker to create it using delicious sponge cake and icing. My daughter was delighted by this unique creation, and the process created additional work for the baker (which I feel privileged to have been able to afford).

Many people are worried about AI taking peoples’ jobs. As a society we have a moral responsibility to take care of people whose livelihoods are harmed. At the same time, I see many opportunities for people to take on new jobs and grow their areas of responsibility.

We are still early on the path of AI generating a lot of new jobs. I don't know if baking AI-designed cakes will grow into a large business. (AI Fund is not pursuing this opportunity, because if we do, I will gain a lot of weight.) But throughout history, when people have invented tools that unleashed human creativity, large amounts of new and meaningful work have resulted. For instance, according to one study, over the past 150 years, falling employment in agriculture and manufacturing has been “more than offset by rapid growth in the caring, creative, technology, and business services sectors.”

AI is also growing the demand for many digital services, which can translate into more work for people creating, maintaining, selling, and expanding upon these services. For example, I used to carry out a limited number of web searches every day. Today, my coding agents carry out dramatically more web searches. For example, the Agentic Reviewer, which I started as a weekend project and Yixing Jiang then helped make much better, automatically reviews research articles. It uses a web search API to search for related work, and this generates a vastly larger number of web search queries a day than I have ever entered by hand.

The evolution of AI and software continues to accelerate, and the set of opportunities for things we can build still grows every day. I’ve stopped writing code by hand. More controversially, I’ve long stopped reading generated code. I realize I’m in the minority here, but I feel like I can get built most of what I want without having to look directly at coding syntax, and I operate at a higher level of abstraction using coding agents to manipulate code for me. Will conventional programming languages like Python and TypeScript go the way of assembly — where it gets generated and used, but without direct examination by a human developer — or will models compile directly from English prompts to byte code?

Either way, if every developer becomes 10x more productive, I don't think we’ll end up with 1/10th as many developers, because the demand for custom software has no practical ceiling. Instead, the number of people who develop software will grow massively. In fact, I’m seeing early signs of “X Engineer” jobs, such as Recruiting Engineer or Marketing Engineer, which are people who sit in a certain business function X to create software for that function.

One thing I’m convinced of based on my experience with Nova’s birthday cake: AI will allow us to have a batter life!

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Z.ai more than doubled the size of its flagship large language model to deliver outstanding performance among open-weights competitors.

What’s new: GLM-5 is designed for long-running agentic tasks. It tops other open-weights models in Artificial Analysis’ Intelligence Index.

Input/output: Text in (up to 200,000 tokens), text out (up to 128,000 tokens)

Architecture: Mixture-of-experts transformer, 744 billion parameters, 40 billion active parameters per token

Features: Function calling, reasoning, context caching

Performance: Best among open-weights models on Artificial Analysis Intelligence Index, 𝜏²-Bench Telecom, Vending Bench 2, and Chatbot Arena Code

Availability/price: Web interface free, weights available via Hugging Face for commercial and noncommercial uses under MIT license, API $1.00/$0.20/$3.20 per million input/cached/output tokens, coding plans $27 to $216 per quarter

Undisclosed: Specific architecture, training data, and method

How it works: Z.ai disclosed few details about the GLM-5’s architecture and training.

The company pretrained GLM-5 on 28.5 trillion tokens, up from the 23 trillion tokens for GLM-4.5.

For post-training, the company used slime, open-source software for reinforcement learning, originated by Z.ai, in which data generation and training are independent processes. The company says this infrastructure improved training throughput, enabling more iterati