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AI Is Moving Beyond “Generating Videos” — Toward “Generating Worlds” Over the past two years, AI video models have advanced at an astonishing pace. From Runway and Pika to Sora and Veo, AI-generated videos have become increasingly realistic and more consistent with the physical laws of the real world. Many people believe the next objective is simply to generate videos that are longer, sharper, and more lifelike. But if we take a step back, we can see that the real transformation is not happening in video itself. It is happening in world models. What Is a World Model? In 1943, psychologist Kenneth Craik proposed an idea that would influence artificial intelligence research for decades. He argued that the human brain does not merely react to the outside world. Instead, it maintains an internal model of how the world works. Because we have this internal model, we can predict the outcome of an action before we actually take it. Before crossing a road, we estimate whether a car will pass by. Before catching a ball, we predict its trajectory. These abilities come from continuously simulating the world in our minds, rather than relying entirely on trial and error. This idea later became known by a more formal term: World Model. A world model does not describe a single image or a fixed video clip. It is an internal representation capable of continuously simulating the rules and dynamics of the real world. Why Is AI Research Turning Toward World Models? Because predicting “what comes next” is becoming increasingly central to how AI systems work. Language models predict the next token. Image models predict the next step in the denoising process. Video models predict the next frame. A world model, however, attempts to predict something broader: What should the world look like in the next moment? In 2018, David Ha and Jürgen Schmidhuber proposed in their paper World Models that an intelligent agent could first learn a model of the world, and then use that internal model to plan its actions. The Dreamer series later demonstrated that many complex tasks could be learned by training agents inside an “imagined world.” At the same time, the development of video models such as Sora and Veo led researchers to another realization: A model capable of continuously generating video has already learned, at least implicitly, many of the rules governing the real world. As a result, these two research directions have gradually begun to converge. But Video Is Not Yet a World This is where the distinction is often misunderstood. For a world model to support meaningful real-time interaction, it must solve several critical problems. Most video models today are essentially answering one question: What should the next frame look like? A true world model needs to answer much more: What happens if I take one step forward? If I walk behind a building and then return, will the building still be there? If I suddenly change the camera angle, will the entire space remain consistent? If I enter a command such as: “Summon a dragon.” Will the world respond immediately? In other words, a world model must do more than generate content. It must understand space. It must understand time. It must understand causality. And it must understand interaction. Moving from watching to participating is where the real difficulty of world models begins. World Models Are Entering the Interactive Era One of the latest attempts in this direction is Alaya World, recently open-sourced by Alaya World, or @alayastd. Instead of generating a fixed video clip, it generates a world that users can explore in real time. Users can begin with text, an image, or a video, enter the generated scene, move freely through it, and introduce new prompts at any moment during generation. The world responds immediately. According to the publicly released information, Alaya World provides: Real-time streaming generation at 720p and 24 FPS Stable continuous exploration for more than one minute The ability to switch prompts and trigger skills or events during generation Model weights and inference code released under the Apache 2.0 License Training code and datasets planned for future release What makes these capabilities important is not simply the technical specifications. It is that the generated “world” can now support continuous interaction. The official demo shows that users can genuinely control, transform, and explore the generated environment. AI Is Evolving From a Tool Into an Environment Over the past few years, most discussions around AI have focused on content generation. Generating text. Generating images. Generating videos. But world models raise a fundamentally different question: Can AI generate an environment that people can inhabit, explore, and continuously evolve? If the answer is yes, the impact will extend far beyond video generation. Game development, robotics training, embodied intelligence, digital twins, virtual production, and many other fields could be transformed by the development of world models. World models are still at a very early stage. Yet from Craik’s proposal of an internal mental model more than eighty years ago to the emergence of today’s interactive world-generation systems, a clear evolutionary path is beginning to take shape. Perhaps what AI is ultimately learning has never been limited to images, videos, or language. Perhaps it is learning the world itself. References GitHub: Technical Report:

#2026漫畫博覽會# | 北門76事務所 草地園遊會 Coser公開 本次特別邀請 #小空Sora、##子涵，# 化身為海色水晶與亞彌奈， 與大家一起同樂！ 完成任務即可獲得1點園遊會點數 累積點數兌換各種實用周邊， 一起創造夏日的回憶吧！ 📍活動資訊 日期 | 2026/7/23（四）～2026/7/27（一） 地點 | 台北世貿一館

😳 NYARIS ROBOH DI DEPAN RIBUAN PENONTON! Detik-detik menegangkan terekam saat sebuah menara manusia raksasa dalam tradisi Dahi Handi terus menjulang semakin tinggi. Setiap lapisan yang bertambah membuat struktur bergoyang hebat hingga banyak orang menahan napas, yakin menara itu akan runtuh kapan saja. Namun yang terjadi justru luar biasa. Dengan kekuatan, keseimbangan, dan kepercayaan penuh satu sama lain, para peserta berhasil mempertahankan formasi hingga puncak. Sorak-sorai penonton pun langsung pecah saat menara manusia itu tetap berdiri kokoh. Tradisi Dahi Handi sendiri merupakan bagian dari perayaan Krishna Janmashtami di India, di mana para peserta membentuk piramida manusia untuk mencapai kendi yang digantung tinggi di udara. Tradisi ini menjadi simbol kerja sama, keberanian, dan semangat pantang menyerah. 🎥 Kalau kamu ada di lokasi, berani nggak berdiri tepat di bawah menara setinggi ini?

Meta发布了Muse Image和Muse Video Muse Image是图片生成模型，生成的图片看起来还不错，但是现在还测不到，我估计会弱于GPT Image 2和Banana Pro。 Muse Video是视频生成模型，中规中矩，肯定比不上Seedance 2.0，更不用说马上要上线的2.5，不过Meta有Instagram，视频模型有用武之地，比Sora有PMF。

Best accounts to follow from each frontier lab to stay constantly up to date Anthropic @karpathy - must-follow account for AI; recently joined Anthropic @bcherny - Claude Code creator, always shares great tips @trq212 - also a Claude Code developer; writes amazing articles on CC OpenAI @polynoamial - works on reasoning research, shares a lot of technical details @gabriel1 - Sora developer, great career path @jxnlco - works on dev experience, shares a lot about Codex Google AI @OfficialLoganK - all the major Google Gemini and AI Studio updates @ammaar - product and design; shares great things about vibe-coding in Google AI Studio @fofrAI - cool use cases for generative models Cursor @leerob - the loudest voice behind Cursor updates @ericzakariasson - shares great insights on using Cursor @mntruell - Cursor’s CEO; major releases and usage updates xAI @milichab - recently joined xAI, shares updates on Grok @skcd42 - also covers major Grok releases @ai_explorer25 - covers all ai content and free resources

I know the cute Sherlock penguin is stealing all the attention… but don’t forget to come take a photo with me too! 🐧💕 I’m waiting for you at the Stella Sora booth SH-2714 (South Hall) as Fuyuka from July 2–5. #StellaSora# #Yostar# #AX2026# #AnimeExpo# #AnimeExpo2026#