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智东西（公众号：zhidxcom） 作者 | 毕伟豪 编辑｜云鹏

智东西10月9日报道，在重置之神Tibo“ 连续28天新发布或者重置 ”的第四天，OpenAI改进了转向功能并发布了GPT-6.1 Sol的Ultrafast（极速）模式， 智能接近Astra，速度比标准版GPT-6.1 Sol快8倍 。

速度快了， 价格自然也是水涨船高 ，现阶段，GPT-6.1 Sol按照速度分三档售卖，标准版价格每百万Token输入2美元，输出10美元，Fast模式速度和价格翻倍。

Ultrafast模式每百万Token输入12美元，输出60美元， 足足是标准版的6倍 。

不过，拿它与旗舰模型对比，价格又没那么难接受了。GPT-6 Astra的标准API价格为每百万输入Token 10美元、输出Token 50美元，按照相同的6倍极速定价计算，Astra Ultrafast的输入、输出价格将达到60美元和300美元， 是Sol Ultrafast的5倍 。

接近旗舰级别的智能、相比标准版最高8倍的生成速度，以及仍低于旗舰模型极速版的价格，构成了OpenAI推出这一模式的核心卖点。

但有个问题，既然Sol的能力已经接近Astra，为什么还需要专门花钱买速度？显然极速模式下的Sol价格比Astra标准版还贵点。

与此同时，Tibo本人和OpenAI的官宣贴子下方评论区，简直是大型嘲讽现场，往下随便一划，几乎看不到称赞的声音，全是“没什么用”，或者“骗子”的吐槽声。

而这些问题，背后恰恰对应着 GPT-6.1 Sol这款模型的最大痛点——速度极慢，网友实际使用中甚至仅有可怜的30甚至20tokens/s。

一、速度升级了，社区为什么意见这么大？

Ultrafast上线后，社区的反馈几乎是清一色的不满。

有用户认为，Sol标准模式的基础速度本来就偏慢，那么即使极速模式提速8倍，用户需要为Sol支付更高的价格，却未必能获得相对于其他模型足够明显的速度优势。

对于速度，还有开发者提出最重要的事情是提高标准版的运行速度，而不是发布极速版本。

还有用户指出，Ultrafast没有向Pro 200美元套餐开放，也就是说这次更新和大部分用户并不相关。

用户的大多数不满实际上是针对速度和价格之间的关系，OpenAI发布了一个便宜的龟速模型，然后通过极速模式变相涨价，这种行为引起了争议。

有开发者直接指责这就是为什么现在基础模型都很慢，都是用低价高质量来骗人的，为推出高速模式做铺垫。

这些反馈指向了一个现状，也即用户愿不愿意为速度付费，不仅取决于速度提升了多少，也取决于原本的体验有多慢，以及现有订阅权益是否足以满足需求。

这种降低基础体验然后售卖补丁包的模式，让Ultrafast模式看起来更像是营销方式而非新发布。

除了提升速度，Codex的实时纠偏功能也是今天更新的重点之一，让用户能够在模型执行任务的过程中及时介入，从而更快调整方向。

如Tibo所说，这项改进与Ultrafast结合效果会更好，有机会减少无效执行，让Agent更快走向最终结果。

这也恰好回应了社区的一部分抱怨。此前有用户反映，Sol执行任务时会长时间反复尝试，却始终无法完成工作。如果用户能及时改变任务方向，模型又能更快响应，整个工作流的效率才可能得到实质性改善。

二、能力追上旗舰，速度成了巨大短板

要理解OpenAI推出GPT-6.1 Sol Ultrafast模式的目的，还得回到GPT-6.1 Sol这款模型本身。

9月30日，OpenAI在DevDay期间发布了GPT-6.1 Sol。这款模型距离上一代GPT-6 Sol仅相隔一周，主打复杂编程、计算机操作和专业工作场景，核心卖点是以约五分之一的标准Token价格，提供接近GPT-6 Astra的能力。

从官方公布的评测结果来看，Sol确实有不小的提升。

在DeepSWE v1.1编程评测中，GPT-6.1 Sol以最高75.2%的成绩，超过GPT-6 Sol最高推理强度下68.8%的表现。

在OSWorld 2.0计算机操作评测中，Sol最高推理强度下取得71.4%的成绩，与Astra的73.5%相差2.1个百分点，单项任务成本约为Astra的七分之一。

这两项评测分别考察模型在真实代码库...

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Turning a simulation idea into a working application means assembling assets, connecting physics and rendering, and checking that the scene behaves as intended. Developers are combining frontier AI models with NVIDIA Omniverse libraries to help carry out that work — building applications for exploring scenarios, investigating failures and improving designs.

Developers direct AI agents through natural-language instructions, review results and guide changes. Omniverse libraries provide GPU-accelerated physics, rendering and sensor simulation capabilities.

Explore the projects below to see frontier AI models such as GPT-6 Astra at work, and check back for new examples from NVIDIA teams and developers across the ecosystem.

Build a Humanoid Simulator for a Warehouse Environment 🔗

Explore a gamified, physics-based control of a humanoid robot in first- and third-person view.

Before automating warehouse tasks, developers need an interactive simulation environment to explore task behavior and evaluate how the work gets done. Frank DeLise, Omniverse product manager at NVIDIA, used Astra to turn a SimReady warehouse and humanoid robot into an interactive simulator with first- and third-person views.

DeLise directed Astra to connect NVIDIA Omniverse libraries for physics ( ovphysx ), scene updates ( ovstage ), rendering ( ovrtx ) and the user interface ( ovui ). He also used Astra with SimReady ( simready-foundation ) to create the physical scene in simulation. Astra then generated animation and application code to bring those capabilities together.

Learn how to prepare and validate SimReady robot assets with frontier AI models and NVIDIA Omniverse libraries.

Connect an Autonomous-Driving Testing Workflow 🔗

Changing a scene, sensor or driving model can significantly aff...

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When an AI agent runs, it often needs to buy something to finish a task: a model inference, an API response, access web content, or a call to another agent. These purchases are small and frequent, sometimes a fraction of a cent each, and they happen inside the agent’s loop with no person available to approve them.

Amazon Bedrock AgentCore payments removes that burden. It gives agents a managed way to pay for services on demand, with spending limits enforced by the infrastructure rather than by the model. In this post, we look at how Incarna used AgentCore payments to let its agents pay BlockRun for model inference one request at a time. BlockRun is a pay-as-you-go inference router serving more than 90 models from more than 15 providers over x402 , each call quoted and settled independently. AgentCore payments works with x402-compatible endpoints, including Amazon Bedrock inference endpoints. With the service, the Incarna team cut the work of adding x402 payment support from months to days, and put an end-to-end pay-per-inference flow into production.

The challenge: Paying for inference by the request

Paying per inference is a high-frequency, low-value pattern. An agent might make hundreds of small purchases in a single session, each worth a fraction of a cent. Card networks weren’t built for sub-cent payments. Building your own rails means solving several hard problems at once. You must decide where the money is held and how each payment is signed, support emerging payment protocols such as x402, and keep an autonomous agent from overspending.

What AgentCore payments provides

Amazon Bedrock AgentCore is a platform to build, connect, and optimize agents at scale, with any framework or model. AgentCore payments is a managed capability of Amazon Bedrock AgentCore t...

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Multiple teams within the same company increasingly need shared access to expensive GPU clusters for their generative AI operations, while maintaining isolation boundaries, resource fairness, and operational independence. Consider a data science team training large language models, a computer vision group running inference workloads, and a research team experimenting with new model architectures. All of them might need access to the same cluster. Without a well-designed multi-tenant (multi-team) architecture, organizations face uncontrolled resource consumption, weak isolation between teams, an inability to attribute shared GPU costs to the teams that incur them, and administrative overhead that slows down innovation.

Amazon SageMaker HyperPod is a purpose-built AI service that simplifies the management of large-scale compute clusters for gen AI workloads. It provides resilient, optimized clusters orchestrated by Amazon Elastic Kubernetes Service (Amazon EKS) or Slurm, so organizations can run distributed training, interactive development, and model inference at scale. At the same time, it automatically handles node health monitoring, fault recovery, and cluster lifecycle management.

In this post, we present a reference architecture for building a multi-tenant environment on Amazon SageMaker HyperPod with EKS. This architecture uses AWS IAM Identity Center for centralized authentication, per-team SageMaker AI domains for a tailored user experience, Kubernetes namespaces for workload isolation, HyperPod Task Governance for fair resource allocation, and namespace-level cost allocation for per-team spend visibility and chargeback. By the end of this post, you will have a clear blueprint for multiple teams to efficiently share a single HyperPod EKS cluster.

Architectu...

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Last day to exhibit your breakthrough to 10,000+ tech leaders at Disrupt is on Oct 2 . Book Exhibit Table Now.

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They cast the wrong lead in “Good Will Hunting,” apparently, because it’s Ben Affleck who is the secret genius, as it turns out. The actor who earlier this year sold his AI filmmaking startup to Netflix for a reported $587 million has gone viral this week for demonstrating his understanding of AI technology, neural networks, machine learning, and the like, across multiple video clips of his recent interviews.

He can even write a little Python, he says.

The movie star delved into his interest in technology most recently on an episode of GQ’s “One More Question” series this week, where he humbly started with “I’ve always been kind of into computers since I was young,” before nerding out on the development of AI as it relates to filmmaking, while using AI terminology like machine learning, neural networks, transformers, tensors, GPUs, inference, and more.

In a clip that’s making the rounds, Affleck explains to the impressed interviewer, Zach Baron, that he got into the space when analog film moved to digital. As he spoke, he kept defining terms so the interviewer could understand what he was talking about.

“I became more interested in that aspect of it, and the visual effects workflow for many years has included machine learning,” Affleck began. “So I can write, like, pretty shitty Python scripts and stuff like that. Because with convolutional neural networks — which were the sort of precursors to what the transformer can do — which is just much more computation simultaneously — you would do things like, look at what’s called a tensor — which is just the numeric...

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Today, we’re excited to announce the availability of Claude Haiku 5.5 on Amazon Bedrock and Claude Platform on AWS . According to Anthropic, Claude Haiku 5.5 is the fastest and most efficient model in the Claude 5.5 family, built for subagents and high-volume, cost-sensitive work. It also costs around 75 percent less than Claude Haiku 4.5 for most tasks.

Amazon Bedrock gives you Haiku 5.5 capabilities while keeping your data within AWS infrastructure with Regional data residency. It works with the AWS controls your team already uses, including AWS Identity and Access Management (IAM) for access, AWS CloudTrail for audit, Amazon CloudWatch for monitoring, and Amazon Bedrock Guardrails. Usage appears on your AWS bill.

Claude Platform on AWS gives you direct access to Anthropic’s native platform experience and capabilities through the AWS Management Console. Build, test, and deploy with the same APIs, features, and console experience you’d get working with Anthropic directly, unified with AWS billing and authentication.

This post covers Claude Haiku 5.5’s improvements, practical guidance on when to choose Haiku 5.5, and how to get started on Amazon Bedrock.

What makes Claude Haiku 5.5 different

Claude Haiku 5.5 is Anthropic’s most capable Haiku model, across coding, tool use, computer use, and agentic tasks. It’s also the first Haiku model with effort controls, so you can tune cost against intelligence for each task instead of picking one setting for an entire workload.

The improvements stand out on quick and repeatable work at scale. For coding tasks, it acts as a subagent routing requests, reviewing code and classifying long documents. For knowledge work, Haiku 5.5 pulls key information from small-to-medium documents, does initial scans, and answers quick questi...

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OpenAI is rolling out GPT-6 with a new feature called "Intelligent UI" for all ChatGPT users. The model now presents answers as interactive interfaces with graphics, buttons, charts, and forms instead of plain text. The format automatically adapts to the question, so comparisons show up side by side and explanations can turn into interactive charts. Users can also have small tools built right inside the chat, like a savings calculator or a game. Google already shipped a similar feature to its Gemini chatbot in May with "Neural Expressive."

GPT-6 can also respond while still "thinking." OpenAI says this cuts wait times by 44 percent and can produce better answers. In internal tests, GPT-6 scored higher than its predecessor GPT-5.6 on difficult web searches using this method.

Paying customers get GPT-6 Sol, free users get GPT-6 Luna. The rollout starts globally today for Plus, Pro, Business, and Enterprise users. Free and Go users follow one day later. Ad Ad

AI News Without the Hype – Curated by Humans

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I released ttok 0.4 , ran uv tool upgrade ttok , piped a file into the new version... and realized that it was defaulting to the GPT-4 tokenizer when it should very clearly default to GPT-5/GPT-6 instead!

I figured switching the default was a reasonable excuse to finally ship a 1.0.

OpenAI haven't actually confirmed that GPT-6 uses the same tokenizer as the GPT-5 family yet - there's an angry issue about it - but I found this commit by William Liu which reports on an experiment he ran confirming that the tokenizers are likely the same:

All seven GPT models (5.5, 5.6 Sol/Terra/Luna, 6 Astra/Sol/Luna) report 44,794 tokens and match each other on every one of the 31 fixtures. GPT-6 introduces no input-count change on this corpus.

Recent articles

Claude Haiku 5.5 - 7th October 2026

We're going to need default hard budget caps on pretty much everything - 3rd October 2026

OpenAI DevDay 2026 live blog - 29th September 2026

This is a beat by Simon Willison, posted on 9th October 2026 .

Monthly briefing

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Quick start · Tour · AI / MCP · Deploy · API

Most of us run our lives across a to-do app, a habit tracker, a budgeting spreadsheet, a Pomodoro timer and a notes app — and none of them know about each other. Edi Life OS puts all of it in one calm, private dashboard and connects the small things you do today to the direction you want your life to take.

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AI-ready. A built-in MCP server lets Claude and other assistants read your dashboard, plan goals, log habits and track expenses for you.

A quick tour

Growth — Six life dimensions, each linked to long-term goals, SMART goals, habits and tasks. Guided weekly, monthly and quarterly reviews.

Focus — Pomodoro sessions with breaks, a built-in focus soundtrack and a landscape that follows the time of day.

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Dates follow Asia/Tehran . The weather card shows Qeshm Island via Open-Meteo; its tide figure is a modeled estimate, not for navigation.

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Edi Life OS ships with an optional MCP server that exposes 26 tools across your dashboard, goals, tasks, habits, finances and notes. Connect it to Claude Desktop, Claude Code or any MCP client and as...

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By the time I got out of bed yesterday, I had three people ask me if I'd seen the OpenAI announcement that the Partition Principle does not imply the Axiom of Choice. The number kept increasing during the day, and I can see why. I am famously interested in this problem.

Indeed, at least two people asked if I plan on sending Sam Altman a bottle of whisky, as promised in my Problems page. The answer to that is no. And let me explain to you why, and why you should be very angry at OpenAI.

Let me clarify something first. I am not entirely against the use of AI. I understand that it is a tool, and many people find it useful, and I am happy to agree that it is merits. I think that we need to better understand how this technology changes our field and how we want to use it before we rush to throw our lot with it. This is why I generally avoid using AI for mathematics (I am happy to ask LLMs to consolidate information for me, or to generate a useful infographic, or to proof read an email, etc.), it's just the framework of using it that is currently missing in my view. Still, we are in the stage of discovery, people use it and we see the consequences unfold and we can decide as a community to adopt it further or restrict its use. For example, arXiv introduced rate limiting as a consequence of these tools, and almost all papers should include some AI statement at this point (even to state that no AI was used, I guess). We need to decide how we judge the contribution of the author, e.g., do we require the chat log be made public, or at least available to the reviewers and the editor? These are questions that we need to contend with and see where they take us.

But, back to the Partition Principle. I took a brief look at the preprint released by OpenAI (not at the Lean code, s...

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智东西（公众号：zhidxcom） 作者 | ZeR0 编辑 | 漠影

智东西10月9日报道，9月29日，亚马逊云科技宣布Amazon Bedrock AgentCore已在由光环新网运营的中国（北京）区域和由西云数据运营的中国（宁夏）区域正式可用。中国区域用户可登录亚马逊云科技来使用Amazon Bedrock AgentCore。

Amazon Bedrock AgentCore是一款全托管的AI Agent部署与运维平台，贯穿从构建、部署到运维Agent的完整开发生命周期，旨在帮助企业实现Agent的快速构建、安全连接与持续优化。

该平台上线即提供六项核心能力，包括运行时、网关、身份管理、浏览器工具、代码沙箱和可观测性。这些能力集中处理了企业如何让Agent高效、安全、持续行动的难题。

Runtime ：Serverless、基于microVM的安全运行时，按需自动扩展，秒级冷启动，仅按实际消耗的CPU与内存秒级付费，支持任意框架、兼容任意模型，会话间基于硬件级虚拟化隔离，数据安全无交叉污染风险。

Gateway ：将API、Lambda函数及MCP Server统一转化为Agent可用的工具入口，统一安全端点集中接入，让Agent轻松发现和调用各类工具；支持按用户设置请求数、Token和连接速率，轻松实现安全与成本控制。

Identity ：面向Agent的身份与访问管理，支持对接符合OIDC标准的身份提供商以控制Agent调用权限，并代替Agent安全托管访问下游服务的凭证，让Agent能代表用户或以自身身份安全访问各类资源。

Browser Tools ：提供快速、安全的云端浏览器运行时，让Agent能大规模地浏览网页、填写表单、抓取内容，与真实网站自然交互，无需自建浏览器基础设施。仅按实际消耗的CPU与内存秒级付费。

Code Interpreter ：代码沙箱让Agent能在安全的沙箱环境中编写并执行代码，从而完成数据分析、计算、文件处理等复杂的端到端任务，显著提升输出的准确性和解题能力。仅按实际消耗的CPU与内存秒级付费。

Observability ：基于Amazon CloudWatch提供全链路追踪、调试与监控能力。让企业对Agent的工作流拥有完整可见性，可对生产环境中Agent的每一步决策进行追踪、调试和性能监控，快速定位异常行为。

在中国区域，亚马逊云科技也正与来自汽车、医疗健康与生命科学、软件与服务以及零售与消费品等众多领域的行业客户展开紧密合作，共同推进企业级Agent的构建与创新应用，打造行业应用落地方案，将Agentic AI深度融入业务流程，助力企业加速释放AI业务价值。

一、提速Agent开发与部署，支持任意大模型及框架

通过提供全托管Serverless架构、开放兼容框架与全链条安全防护，Amazon Bedrock AgentCore让企业能以更低门槛、更高可靠性构建、托管与扩展Agent，助力企业消除自建Agent基础设施的复杂重负，迅速将Agent的技术验证推向生产部署。

Agent的成本优化从单次调用价格延伸到整条任务链路，少一次无效工具调用、缩短一段不必要的会话、避免重复执行，都可能影响最终整体成本。

Signal65性能分析师Mitch Lewis选取客服、运营与项目管理、市场研究三类示例Agent，分别使用AgentCore以及由独立工具和服务组合而成的自定义方案进行开发。

报告结果显示，使用AgentCore的端到端开发速度达到对照方案的2.1倍，云端部署速度达到5.2倍，基础设施配置与集成所用时间减少75%。

在Agent构建和部署方面，Amazon Bedrock AgentCore保持高度开放，支持任意大模型以及任意框架，通过解耦底层基础设施与上层应用逻辑，让企业能够轻松集成并统一托管多样化的Agent。

在运维方面，AgentCore能根据业务流量自动且即时地实现从零到数十万并发会话的弹性扩缩容，免去企业自建集群与多租户管理的繁重运维负担，并针对复杂的长时间运行任务提供行业领先的运行时支持，帮助企业快速将技术验证推向生产部署。

针对企业Agent任务不均情况， AgentCore Runtime 采用Serverless架构，...

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Computer programming is, fundamentally, about two things: Problem-solving using computers Learning to control complexity while solving these problems I have a hard time imagining a future where knowing how to solve problems with computers and how to control the complexity of those solutions is less valuable than it is today, so I think it will continue to be a viable career even with the advent of AI tools.

Recent articles

Claude Haiku 5.5 - 7th October 2026

We're going to need default hard budget caps on pretty much everything - 3rd October 2026

OpenAI DevDay 2026 live blog - 29th September 2026

This is a quotation collected by Simon Willison, posted on 8th October 2026 .

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智东西（公众号：zhidxcom） 编译 | 杨京丽 编辑 | 李水青

智东西10月9日消息，今天，苹果全球营销高级副总裁格雷格·乔斯维亚克（Greg Joswiak）在X上发布预热视频，证实苹果将于10月13日举行主题为 “欢迎回家”（Welcome home） 的特别活动， 多款智能家居新品 有望亮相。

▲苹果全球营销高级副总裁官宣智能家居新品发布活动（图源：X）

此次活动将成为苹果新任CEO约翰·特努斯（John Ternus）上任后，在 智能家居产品线上的首次重要动作 。

科技视频博主马克斯·布朗利（Marques Brownlee）也公开其收到的邀请函。此次“Apple Experience”体验活动将在纽约翠贝卡举行，时间为美国东部时间10月13日上午9点，即 北京时间10月13日21点 。

▲科技视频博主公开其收到的活动邀请函（图源：X）

据彭博社记者马克·古尔曼（Mark Gurman）等消息源此前报道，苹果计划在10月13日推出 全新产品 带屏智能家居中枢HomePad、新款智能音箱HomePod mini和新款电视盒子Apple TV 4K ，这三款产品有望支持新版Siri AI。

其中，外界暂称为“HomePad”的带屏家庭中枢，是苹果智能家居产品线中的全新产品。它预计配备 约6英寸触摸屏 ，可摆放在桌面或安装在墙上，支持 家居设备控制、视频通话和音乐播放 等功能，并能识别不同家庭成员、展示个性化内容。

▲HomePad概念图（图源：9To5Mac）

古尔曼还披露，苹果正在 与韩国消费电子巨头 LG共同开发智能门锁、视频门铃、恒温器和摄像头等家居配件 。这些产品预计以LG品牌推出，与苹果的新款家庭设备协同工作，但正式上市可能还需数月。

苹果本月的新品发布或许不止这一轮。据古尔曼和MacRumors等报道，苹果可能在10月27日前后推出 OLED版iPad mini、首款搭载OLED触控屏的MacBook Pro ，以及搭载 M6芯片的iMac和14英寸MacBook Pro 。

一、带屏家庭中枢有望登场，可识别不同家庭成员

此次最受关注的新品，是外界暂称为“HomePad”的智能家居中枢。

据古尔曼报道，该设备将采用约6英寸的方形屏幕，提供桌面和壁挂两种版本。其中，桌面版通过金属支臂连接圆形底座，屏幕可手动调整角度，设计类似苹果早年的iMac G4。设备需要持续接电使用，正面配备摄像头，底座内集成扬声器和麦克风。

它预计支持智能家居控制、FaceTime视频通话、音乐播放、照片展示，以及查看日历和提醒事项。在厨房场景下，用户还可以通过语音查询食谱。

针对家庭多人共用的情况，古尔曼称，设备能够通过声音或面部识别判断使用者，展示对应的个人内容，并提供隐藏私人信息的访客模式。如果识别失败，用户可以借助iPhone辅助验证。

二、HomePod mini和Apple TV有望换芯，支持新版Siri

据多方消息报道，与家庭中枢一起亮相的，还可能有HomePod mini 2和第四代Apple TV 4K。两款设备预计保留现有外观，通过更快的芯片支持Siri AI；HomePod mini还可能新增粉色、绿色等配色。

▲目前的HomePod mini（图源：彭博社）

古尔曼称，几款智能家居设备原本计划于2024年推出，发布延期与等待新版Siri有关。更大尺寸的HomePod则预计在之后更新。

另外，据MacRumors预测，新款HomePod mini和Apple TV 4K可能搭载苹果N1无线网络芯片，支持Wi-Fi 7、蓝牙6和Thread。Apple TV还可能增加内存、升级处理器。

▲目前的Apple TV（图源：彭博社）

三、苹果被曝与LG共同开发门锁、门铃和摄像头

除了上述三款设备外，古尔曼10月6日披露，苹果正在与LG电子共同开发一批智能家居配件，包括视频门铃、智能门锁、恒温器、温度传感器，以及室内、室外和泛光灯摄像头。

据古尔曼援引知情人士消息，这些配件将使用LG品牌，由LG负责制造和售后支持，并与苹果即将推出的家庭设备协同工作。

部分产品已出现在监管申报资料中，申报产品包括LG有线视频门铃、LG泛光灯摄像头、LG插电式室外摄像头、LG插电式室内摄像头、...

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REDWOOD CITY, CA, October 7, 2026 — Biohub, the U.S. Department of Energy, the National Institutes of Health, and new funding partners today announced a major expansion of an international effort to generate and make accessible the data enabling predictive AI models of biology. Together, the organizations are investing $1.8 billion in funding, data, computation, and new measurement technology, the largest coordinated commitment to generating AI-ready biological data to date. The result will be an open resource for the research community that provides the foundation for greater understanding and ultimately treatment of human diseases.

As part of this announcement, Biohub has partnered with the Department of Energy (DOE) Office of Science and the National Institutes of Health (NIH) to advance the frontier of artificial intelligence in biology. DOE will invest more than $500 million over five years in lab measurement, modeling and computation toward the international effort to build an AI-ready open data resource. NIH will coordinate the contribution of relevant datasets, repositories, and knowledge bases developed through more than $500 million in prior federal investment aligned to this initiative. Biohub will work with NIH to standardize these datasets for AI model training.

In addition, Google DeepMind, Isomorphic Labs, and Meta are collectively investing $300 million in the Virtual Biology Initiative to create the technologies and multi-modal datasets needed to build predictive models of life.

These datasets will enable the global scientific community to collectively build and use AI models that allow researchers to ask, predict, and answer biological questions digitally, accelerating the path to new ways of preventing and treating diseases. This initiative will...

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凭《怪物史莱克》获得奥斯卡最佳改编剧本提名的编剧 Joe Stillman、《冰雪奇缘》的选角导演 Scott Muller、《鬼妈妈》的艺术指导 John Lee，选择在一部由全球最大的独立 AI 原生影视公司 Utopai Studios 主导的动画长片里相聚。

据《Variety》报道，这部预计于 2027 年暑期上映的家庭动画长片《The Most Serious Fart》，围绕一个格外严肃的屁 Siegfried 展开，他希望改变外界对自己的看法，并在旅途中学习接纳自己、找到归属感。

过去三年，生成式 AI 讲给影视业听的故事大多关于替代，关键词是成本骤降、一人剧组、人人都是导演。

可下面要讲的是一个其作品即将登上院线的的 AI 影视公司——Utopai Studios 的故事。

它进了好莱坞工会，进了院线，巴西和德国的电视台想按普通剧集的价格买它的片子。

Utopai Studios 的首席运营官 Kevin Chong 坐在 TechRadar 记者对面时，遇到这样一个提问：「观众开始抵触 AI 电影了，购票之前会先查一部电影有没有用 AI，你们怎么办。」

他回答说：「Utopai 不把 AI 电影当作一个单独的片种，AI 只是一种工具，和视觉特效、虚拟制作没有本质区别。」

今年九月，Utopai 的定制视频模型 Utopai X，在 AA 的文生视频盲评榜上排到全球第二、全美第一，是榜单运行以来第一家凭自有模型跻身前列的影视公司。

一镜一镜，AI 如何进入长片制作

一种是工具生意，把模型做成订阅服务卖给创作者，按生成时长计费。

另一种是内容生意，公司自己承担开发和制作风险，把作品送进影视行业原有的交易系统，靠版权预售、电视播映权、流媒体、院线以及后续 IP 收入回收成本。

传统买家购买一部作品时，并不会因为技术路线新鲜就降低风险标准。他们仍然要确认版权是否清晰，制作能不能按时完成，角色和画面能不能在几百个镜头中保持稳定，演员权益有没有后患，内容能不能符合当地播出规范，最后还要判断，观众究竟愿不愿意看。

所以，过去不少 AI 内容的问题都是走不到采购桌前。

《Space Nation》是一部 8 集科幻剧集，每集约 45 至 60 分钟，由 Martin Weisz 执导全季。官方披露显示，项目已经进入制作，并计划按照好莱坞公会框架推进。

《Space Nation》也是 Utopai 第一次在工会体系内完整走完一部剧集的生产流程。

作为首家加入好莱坞相关工会的 AI 影视公司，Utopai 在推进这部剧集制作的过程中，就 AI 内容的应用规则、真人演员与工作人员的参与比例、AI 辅助环节的边界等一系列问题，与工会方面展开了多轮协商。

Forbes 披露，Utopai 已经把《Space Nation》和历史史诗电影《Cortés》的部分国际发行权预售给包括巴西 Globo 体系、德国电视台在内的传统买家，而且采用的，是非 AI 内容已有的正常市场价格。

如果项目按计划交付，《Space Nation》和《Cortés》的预售收入合计最高可能达到约 1.1 亿美元。

后者以 1519 年西班牙人进入阿兹特克帝国后的文明冲突为背景，剧本由 Nicholas Kazan 创作，预计在 2027 年全院线发行。Utopai 试图让真人表演与数字环境结合，用 AI 承担一部分过去需要大型置景、后期视效和现场执行完成的工作。

作为中国观众，惊喜的是，Utopai 决定与华策影视联合开发《西游记：消失的 500 年》，从孙悟空被压五行山的空白期重新寻找故事。

在 2027 年即将上映的作品中，还有由柏林银熊奖导演 Hyo-joo Yang 执导的亲情剧情片《Half Moon》。

影片聚焦一个生活在德国的跨文化家庭，在家中被忽视的少女 Yeri，与自我放逐、以护理工作为生并背负着过往身心创伤的姨妈 Ah-Jin，在一个夏天里学会了理解与尊重彼此。

主体拍摄已于 2026 年 8 月在德国开机，影片计划于 2027 年上映。PAI 将为部分视觉元素提供支持，比如一些游泳相关的实景段落，剧组先完成真人表演和实景拍摄，再用 AI 补全环境中那些依靠传统置景和特效也很难完成的部分。

PA...

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