Christina Stathopoulos, the data and AI evangelist behind Dare to Data, continued her run sorting the week’s most impactful stories into a handful of themes we’ve been watching play out over the past month: more firms investing in the compute AI runs on, more concerns about who controls a model’s borders, and more AI-generated code posing challenges to scaling AI enterprise-wide.“Dare to Data”的主理人、数据与 AI 布道者 Christina Stathopoulos，再次将本周最具影响力的故事梳理归纳。过去一月，几股暗流涌动：企业纷纷加码 AI 算力底座；模型边界的掌控权引发各方忧虑；而 AI 生成代码的泛滥，亦为企业级规模化部署平添了重重阻碍。
Christina also quickly shared two updates from the frontier labs that we won’t get into below. First, OpenAI finished rolling out GPT-5.6, its family of models tuned for different workloads with an option to dial reasoning up or down, and launched ChatGPT Work, an agent workspace that connects the model to Slack, calendars, documents, and other enterprise tools. Anthropic, meanwhile, published research describing a hidden internal workspace it’s calling the “J-space” that suggests that Claude organizes and manipulates ideas before producing a response. It isn’t proof of anything like consciousness, as Christina was quick to note, but it’s one of the clearer steps yet toward inspecting what a model is actually doing between input and output. That kind of visibility is critical for catching problems like deception or unsafe behavior before they show up in an answer.Christina 还简要分享了前沿实验室的两则消息。其一，OpenAI 完成了 GPT-5.6 模型的全线推送，该系列模型针对不同负载量身定制，用户可按需调节推理深度；同时，他们推出了 ChatGPT Work，这是一个连接 Slack、日程、文档及各类企业工具的智能体工作台。其二，Anthropic 发布研究报告，揭示了一个名为“J-space”的内部隐秘空间，暗示 Claude 在输出回答前，会先在内部梳理并操纵各类意念。正如 Christina 所言，这虽非意识存在的明证，却是人类窥探模型在输入与输出之间究竟“做了什么”的一大进步。这种透明度至关重要，唯有如此，方能在欺诈或不当行为酿成大祸前，将其及时拦截。
More AI labs are turning into chip companiesAI 实验室纷纷转行造芯
Last week, Christina covered the opening moves in an AI hardware race, with research from IBM and NVIDIA and a joint OpenAI and Broadcom project. Now there’s news that Chinese company DeepSeek is developing its own inference chips to cut its dependence on NVIDIA and Huawei, and Anthropic is in early talks with Samsung to build a custom AI chip. And as we saw with IBM’s sub-1 nanometer tech, chips are getting denser. Researchers in South Korea have developed a manufacturing technique that stacks more than 10 ultrathin memory chips, packing about four times the density of today’s commercial high-bandwidth memory into the same footprint. The layers align within about six micrometers, roughly a tenth the width of a human hair. The short distances between layers mean the signal doesn’t have to travel as far, making the whole stack run faster and more efficiently.上周，Christina 报道了 AI 硬件竞赛的开局，IBM 与 NVIDIA 的研究，以及 OpenAI 与 Broadcom 的联手项目皆在其中。今又有消息传来，中国 DeepSeek 公司正自研推理芯片，以求摆脱对 NVIDIA 与华为的依赖；Anthropic 亦在与三星接洽，谋求定制 AI 芯片。正如 IBM 那项亚纳米技术所展示的，芯片正变得愈发致密。韩国研究人员开发出一种堆叠技术，将十余层超薄内存芯片叠在一起，在方寸之间实现了四倍于当前商用高带宽内存的密度。层与层之间仅隔六微米，约莫仅及发丝的十分之一。层间距离缩短，信号传输路程随之减小，整套堆叠系统的运行效率自然更上一层楼。
For AI companies, owning more of the stack is a way to control the cost and performance of running models once they’re built. As chip access becomes a lever in trade and security policy, it’s also a way to circumvent obstructions related to a supplier’s roadmap or a rival’s export policy.对于 AI 公司而言，将底层技术握在手中，方能掌控模型运行的成本与性能。随着芯片获取渠道成为贸易与安全政策的博弈筹码，这也成了绕开供应商路线图限制或对手出口政策的一条妙计。
A new security threat underscores the broader geopolitical stakes新兴安全威胁凸显地缘政治之重
JADEPUFFER is the first documented ransomware attack in which an AI agent carried out the entire operation end to end. A human chose the target, then the agent took over, exploiting a known vulnerability, searching for passwords and API keys, moving into the production database, encrypting it, and even writing its own ransom note, all without a person directing each step. Security teams have been bracing for this kind of sophisticated AI-driven attacks. JADEPUFFER is likely the first of many.JADEPUFFER 是首个被记录在案的勒索软件攻击，其全程皆由 AI 智能体独立操纵。人类仅需选定目标，剩下的便交由智能体接管：它利用已知漏洞，搜寻密码与 API 密钥，潜入生产数据库并将其加密，甚至自行撰写勒索信，全程无需人手干预。安全团队早已严阵以待，防备此类高明的 AI 攻击，而 JADEPUFFER 恐怕只是冰山一角。
That growing threat surface was one reason why AI security took up so much of the conversation at the recent NATO summit in Ankara, where leaders discussed how AI is reshaping cyberattacks, drone warfare, disinformation, supply chain risk, and the speed at which leaders are expected to make high-stakes decisions. Paralleling US restrictions on who can access domestic models, China may also be moving to limit overseas access to its own frontier systems, and Alibaba is banning US-made models for its own employees. We’ve been tracking this story since May, when the US government’s on-again, off-again restrictions on Anthropic’s Fable and Mythos models offered an early sign that frontier model access was becoming of national interest. Christina shared findings from Our World in Data that show just how much the market share of Chinese models has grown from a year ago: Per data from OpenRouter, Chinese model usage at US-based companies, measured in tokens, is approaching parity with US model usage. For technical leaders, that’s a reminder that model choice is now as much a supply chain decision as a technical one, and it’s increasingly one with geopolitical repercussions.这种不断扩大的威胁面，正是近期安卡拉北约峰会上 AI 安全议题成为焦点的缘由。各国首脑探讨了 AI 如何重塑网络攻击、无人机战、虚假信息、供应链风险，以及决策者在重压之下做出决策的速度。正如美国限制国内模型访问权限一般，中国亦可能采取措施限制海外访问其前沿系统，阿里巴巴甚至已禁止员工使用美制模型。我们自五月起便在追踪此事，当时美国政府对 Anthropic 的 Fable 与 Mythos 模型时断时续的限制，便已显露出前沿模型访问权正上升为国家利益。Christina 分享了来自“Our World in Data”的数据，显示中国模型市场份额较去年增长惊人：根据 OpenRouter 数据，中国模型在美企中的使用量（以 Token 计）已直逼美国本土模型。对技术领袖而言，这无疑在提醒：选择模型不仅是技术决策，更是供应链决策，且愈发带有地缘政治的余波。
Two challenges to watch for as enterprises scale AI企业规模化 AI 时需警惕的两大挑战
Now that code is effortlessly simple to generate, the real engineering work is making sure that AI-created code is correct, secure, and safe to run in production. As many in the field are now realizing, that’s easier said than done. A recent study of nearly 200,000 pull requests across more than 800 developers found that AI nearly doubled coding productivity, and reviewers couldn’t keep pace. Each reviewer is now responsible for roughly twice as many pull requests as they were in the years before widespread AI adoption, and the share of pull requests getting human review fell from 89% to 68%, with automated reviews filling the gap. It’s part of the same story Matt Palmer told on the show a few weeks ago when he compared running a team of agents to managing a mid-size team of human developers: “You’re just sending messages all the time, and you’re checking in to make sure things are being done,” he explained. The increase in velocity sets up a real risk of cognitive fatigue and burnout.如今代码生成已是信手拈来，真正的工程难点在于确保这些 AI 生成的代码在生产环境中准确、安全且稳妥。正如业内许多人所察觉的，知易行难。一项针对 800 多名开发者、近 20 万个拉取请求（Pull Request）的研究发现，AI 将编码效率提升了近一倍，代码审核者却已跟不上节奏。每位审核者如今负责的拉取请求数比 AI 普及前翻了一番，而人工审核的比例从 89% 跌至 68%，其余皆由自动化工具代劳。这与 Matt Palmer 前几周在节目中所言如出一辙，他将管理智能体团队比作管理一支中型人类开发团队：“你只需不断发送指令，并核查进度。”这种速度的提升，实则埋下了认知疲劳与倦怠的隐患。
Here’s another challenge enterprises are facing as they scale AI: They’re connecting more and more of their data, workflows, content, and business processes to a single AI provider. As we already learned in the data space, the more attached you become to that provider, the harder it is to switch down the line. The solution to this vendor lock-in is to build an AI stack and the workflows around it that keep you in control of your data and ensure you can swap models as the technology evolves. Enterprises that treat model choice as a one-time decision are setting up the same dependency problem that OpenAI’s GPT-5.6 and Anthropic’s chip talks are trying to avoid, just one layer up the stack.企业在规模化 AI 时面临的另一挑战在于：他们正将越来越多的数据、工作流、内容与业务流程捆绑在单一 AI 提供商身上。正如我们在数据领域所见，对单一供应商依赖越深，日后想要抽身便越难。解决这种“供应商锁定”的良方，在于构建一套 AI 技术栈及配套工作流，确保自身对数据拥有绝对掌控权，并能在技术迭代时灵活替换模型。若将模型选择视为一锤子买卖，无疑是在重蹈覆辙，陷入 OpenAI GPT-5.6 或 Anthropic 芯片谈判所试图规避的依赖陷阱，只不过这次是在技术栈的更高层级。
What’s next后续展望
Christina will return next week with another sweep of AI news, including a first look at Apple’s lawsuit against OpenAI, New York’s pause on new hyperscale data centers, and a landmark ruling in Germany holding Google accountable for misinformation generated by AI Overviews, plus updates on DeepSeek’s IPO plans, OpenAI’s first AI hardware device, and Anthropic’s new enterprise deployment unit. Join her live on the O’Reilly learning platform or catch up after the fact on YouTube, Spotify, Apple, or wherever you get your podcasts.Christina 将于下周回归，带来新一轮 AI 新闻盘点，包括苹果起诉 OpenAI 的首度解析、纽约州暂停新建超大规模数据中心的消息，以及德国法院裁定 Google 需对 AI 概览生成的虚假信息负责的里程碑式判决。此外，还有 DeepSeek 的 IPO 计划、OpenAI 首款 AI 硬件设备，以及 Anthropic 新成立的企业部署部门的最新进展。欢迎通过 O’Reilly 学习平台参与直播，或在 YouTube、Spotify、Apple 等播客平台收听回放。
And if you want to keep learning between episodes, check out our new weekly show Zero to Agent in 30 Minutes, our AI Codecon live event on August 31, and The Agentic Enterprise now in early release on O’Reilly. Christina’s also hosting the AI Superstream on AI harnesses next week on July 23. Hope to see you there for this four-hour deep dive on turning models into agents and running them securely at scale.若想在节目间隙持续精进，请关注我们每周更新的节目《30 分钟从零到智能体》，以及 8 月 31 日举办的 AI Codecon 直播活动，或是阅读 O’Reilly 抢先版新书《智能体企业》（The Agentic Enterprise）。Christina 还将于 7 月 23 日主持一场关于 AI 智能体应用的 AI Superstream 直播，这场四小时的深度研讨将聚焦如何将模型转化为智能体并进行安全规模化部署，期待与你相见。
