软件与编程

这已经不再是简单的代码自动补全，而是真正的‘智能体优先’——智能体编写所有的代码，而你只是在管理并驱动一支智能体团队。

it's not code completion it really is agent first the agents write all of the code you're just managing a team of agents and and driving that

语境：披露 AWS 内部研发模式的剧变：前沿团队完全告别辅助补全逻辑，工程师工作转变为设定规格并管理自主写代码的 Agent 集群。

支持 → latent-space-richard-socher-20260914

支持 → the-every-sam-altman-20260930

我们所认为的语言模型——也就是我们向其发起查询的那个东西——未来很可能实际上是一个蜂群、一个脚手架，或者某种扩展性极佳的奇特外周框架设计，只不过用户根本感知不到底层细节。

what we think of as a language model like the thing that we query might actually be like a swarm or like a scaffold or like some weird harness design that scales very well, but the user just doesn't see it.

语境：Alex Zhang 预判大模型形态演变：单一 Transformer 前向传播终将被底层多智能体递归与抽象脚手架取代，对外则维持统一的模型接口封装。

支持 → latent-space-thariq-shihipar-20260929

支持 → the-every-sam-altman-20260930

从整体来看，现有软件在设计之初就根本不是为了与 AI 协同而生的。你可以自己使用它，或者让 AI 代表你操作它；但要真正实现紧密协作，尤其是多个人与多个人的 AI 全体协同工作时，我们现有的很多抽象概念就彻底崩溃了。

software was really not designed on the whole for collaborating with AI. And you can use it yourself or you can have AI use it on your behalf, but to really collaborate closely, especially when it's multiple people and multiple people's AIs all working together, like a lot of the current abstractions we have break down.

语境：Altman 剖析 OpenAI 推出 Space 等协同套件的深层动机：现有的协同软件架构无法承载多人类与多智能体交织的实时异步工作流，底层软件抽象必须被彻底重写。

支持 → sources-satya-nadella-20260925

承接 → tbpn-sam-altman-20260929

Computer Use 最强大、也是我认为最令人兴奋的地方在于，它让智能体能够做到你作为人类所能做到的一切事情。因为世界上所有的软件都是为人类设计的，而现在智能体可以使用完全相同的软件，你可以直接将这些工作委托给智能体。

the powerful thing about computer use and the reason why I think it's so uh exciting is because it makes it so that the agent can do anything you as a as a person can do. Um, because all the software in the world was designed for humans and now agents can use that same software uh and you can delegate to the agent.

语境：Ari Weinstein 阐释 Computer Use 的核心生产力价值：软件界面本质是人类的操作表面，智能体掌握此能力即可无需 API 改造而直接接入全量数字资产。

支持 → the-every-sam-altman-20260930

支持 → latent-space-thariq-shihipar-20260929

它实际上在编写并执行 JavaScript 代码，由计算机一次性执行代码来同时完成许多动作，这带来了巨大的速度提升和极强的能力跃升。

It's actually writing JavaScript code that it executes that that the computer executes to perform sometimes many actions at once, which is a great, you know, speed up and great capability.

语境：揭秘 Computer Use 底层提速的技术演进：智能体不再盲目模拟人类单次鼠标点击，而是动态生成批处理脚本在前端沙箱中批量调度动作。

支持 → latent-space-richard-socher-20260914

支持 → latent-space-alex-zhang-20261002

随着模型越来越强大，它们完成基础任务的底线能力大幅提高。因此我认为从极限来看，CLAUDE.md 这类规则文件最终会消亡……最棘手的是规则会随模型版本而变，如果你在上下文中塞满针对历史失败案例的长篇累赘日志，往往反而会过度束缚 Claude 的泛化推理。

as the models get better and better, the floor of how they accomplish the simpler task is better. And so I do think in the limit cloud.mmd goes away. […] The really tough thing is that this changes per model […] And if you keep this context with this running log of a bunch of different failure modes, they will probably overconstrain Claude

语境：反思开发者维护海量提示词规则的常见误区：随着模型基座智能的跃升，手工堆砌的静态负面规则（如 CLAUDE.md）不仅存在严重跨版本兼容问题，还会造成过度约束，降低模型的自由推理表现。

支持 → sequoia-sutton-20260818

支持 → the-every-sam-altman-20260930

如果你享受的乐趣在于敲代码本身，那么在座的每一位软件工程师未来编写的代码都会越来越少。但如果你的乐趣在于交付产品，那现在就是有史以来最好的时代。这正是我所热爱的：我享受构建并最终把东西交付出去。在座的每个人编写的代码都将减少，但交付的成果却会大大多于以往。

If your joy is in writing the code, I think you're everyone in here who is a software engineer is going to be writing less code. But if your joy is in shipping product, like it's never been a better time. And this is actually where I find my joy. It's like I like building and shipping the thing. So everyone in here is going to code less, but they're going to ship more.

语境：Zach Lloyd 分析程序员职业角色的重塑：代码编写逐渐被智能体流水线接管，工程价值彻底回归到端到端产品交付上。

支持 → latent-space-thariq-shihipar-20260929

支持 → the-knowledge-tobi-lutke-20260915

循环本质上是让智能体不断精进的机制，通过观察它们在做什么、在哪里犯错来实现。在你的软件工厂中，一种极具代表性的循环就是「技能循环」：执行具体任务的工人智能体负责运行技能，而观察者智能体则在旁边监督这些技能的应用效果、挑出瑕疵，并负责优化这些技能本身。

Loops are basically ways of, uh, having agents improve, um, by like observing what they're doing, where they're failing. Um, and so, a common kind of loop that you're going to want to put in your factory is like a skill loop. That means you're going to have your factory agents that are running skills, and then you'll have observer agents that are seeing how those skills are being applied, looking for issues, and trying to improve the skills.

语境：Zach Lloyd 详解智能体工厂的自改进机制：利用双层智能体架构（执行智能体 + 观察者智能体）动态迭代组织内的工程技能库。

支持 → latent-space-ari-weinstein-20260930

因此，我们系统中的数据安全正迎来一个巨大的升级关口。我确实认为，展望未来，我们必须拥有一套截然不同的访问与安全模型。我们现有的权限模型粒度不够细，性能也根本不足以应对这些智能体任务。

So so the the data security in our systems is a this is going to be a huge upgrade moment. I I actually Mart like I think that actually we're going to need a different access and security model. Yeah. Going forward. Where are we going to go on that? Because the model we have is not granular enough and it's not performance enough to handle this stuff.

语境：Aaron Levie 警告企业信息安全防线面临重写：智能体蜂群使传统的基于账号粗粒度鉴权失效，必须建立全新粒度与性能兼顾的动态安全模型。

支持 → latent-space-ari-weinstein-20260930

支持 → source-8afa853a350658b4

现有的操作系统在设计之初就不是为了向智能体开放那种精细度的工具控制权而生的。我们在这一领域做了大量探索，因为你显然不想直接把整台电脑的文件系统权限全盘交给智能体，多数情况下你需要细致入微的管控。

And like our OS probably wasn't built for the right level of of granular set of of tools you want to give the agent. Um we we've kind of done a lot of work work in this space because obviously like you know do you want to give an agent exact like your entire file system? Probably not right maybe in some cases you do but oftentimes you want to have granular controls

语境：Aaron Levie 分析智能体落地桌面与企业环境的瓶颈：操作系统层面的粗暴二元授权（要么全盘禁止要么完全放行）无法适配智能体的工作流。

支持 → latent-space-ari-weinstein-20260930

白线代表人类研究员的纪录演进，红线代表 Claude，蓝线代表 Codex；你会发现，在几乎所有时间节点上，Claude 和 Codex 取得的优化成绩都持续大幅优于人类研发人员的纪录。

in white you see that the human record progression right and in red you see cloud I mean it's supposed to be orange but whatever and in blue uh you see codeex right and you see that at almost every time uh cloud and codex are better than the human record

语境：Elie Bakouch 展示智能体在算法速通基准上的压倒性效率：在明确代码沙箱与自动评估环境下，前沿模型能以连续不停机的状态迅速超越人类专家。

支持 → all-in-satya-nadella-20260915

这赋予了我们捕捉一类全新错误的能力，我们称之为「行为缺陷」（Behavioral Bugs）。它既不是程序崩溃抛出异常，也不是系统性能问题，而是模型行为维度的偏离。任务基准与评测集（Evals）正在成为 AI 时代的全新持续集成（CI）范式。

This introduces an ability to catch a new class of bugs in our world called behavioral bugs. Not exceptions, not performance, but behavioral bugs. Next up, tasks and evals are the new world of CI. You've heard a lot about this. If you are a software engineer, you've written unit tests your whole life.

语境：Tim Sweeney 提出智能体质量保障新范式：传统单元测试只能验证代码逻辑无崩溃，唯有建立面向行为规范的自动化 Eval 流水线才能阻断恶性幻觉。

支持 → ai-engineer-tim-sweeney-wb-20260926

我们依然可以在预训练阶段针对神经网络架构和学习率等超参数执行自动化搜索；但在测试与应用阶段，所有冗长繁琐的调参过程全部消失了，数据科学家终于得到了梦寐以求的工具——一个毫秒级极速且真正开箱即用的建模方法。

So we can still do a search over hyperparameters of the architecture of our neural network. We can search over our learning rates etc for the pre-training but all of that at test time falls away and data scientists get what they always wanted namely a just really fast method that works out of the box.

语境：Frank Hutter 总结表格基础模型为数据科学带来的效率颠覆：繁重的调参成本被前置固化到基础模型预训练中，终端使用者无需任何特征工程即可获得最强性能。

支持 → machine-learning-frank-hutter-20260923

若按支出金额计算，开源模型仅占约 5%，比例非常小；但若按处理的 Token 总量计算，开源模型已经超过了 60%。

So by dollar open source is like 5%. It's very little, but by token count it's over 60%.

语境：Ali Ghodsi 披露企业级生产环境中的真实算力分布：企业用高价前沿模型做架构与审计，而将绝大部分海量常规推理任务下放给极低成本的开源模型。

支持 → source-23976cb2c5a96ae7

市场上仍有人试图强迫用户在他们自己的平台上构建智能体，或者把用户拉到他们的封闭领地里去完成工作；而另一方像 Salesforce 等则主张完全 Headless（无头化），你可以把能力带到任何你想要的地方。我坚信后者才是最终的制胜策略。

some folks that are still trying to be like ah we're going to force people to build agents on our own platform or we're gonna bring them over here and that's kind of where things are going to get done and then you see like the sales forces of the world that are like we're headless you can bring it wherever you want etc and I definitely think that ladder is the winning strategy

语境：Wade Foster 对比平台型封闭 AI 与 Headless 架构：用户绝不会为了特定 Agent 迁移工作主阵地，能通过 MCP 等协议嵌入用户日常主力工具的应用才能胜出。

支持 → dreamforce-2026-marc-benioff-20260916

在绝大多数情况下，按坐席收费的模式已经彻底死亡或正在消亡。它可能在某些狭窄领域还有微存空间，但大体而言，当智能本身已成为产品体验的核心支柱时，固定的按人头计费机制根本无法自圆其说。因此，必然会转向某种基于使用量的定价模式。

for the most part seatbased pricing is dead or dying. I think it may still make some sense in some small areas, but by and large when you're when intelligence is such a core part of these product experiences, I don't see how a fixed seatbased pricing mechanism really makes much sense for that at all. And so, inevitably, I think that lands you in some sort of usage

语境：Wade Foster 预判企业级软件商业模式的根本性变革：随着 AI 承担实际工作，坐席数量不再衡量价值，按智能调用与执行结果计费势在必行。

支持 → cognitive-revolution-wade-foster-20260917

随着我们走向我认为的第三阶段——它不再仅仅是你向聊天机器人询问某事，或者让编码代理完成任务，而是 AI 时时刻刻在为你后台持续运行：理解你的工作职责、理解你需要什么才能更成功、为你实时渲染新代码、主动监控你的 Slack 或电子邮件。

as we head towards what I think will be this third phase of it's not just like you ask AI for something like a chatbot or you ask AI to do something like a coding agent, but the AI is like running for you all of the time. Understanding your job, what you need to be more successful, rendering new code for you, proactively watching your Slack or your email.

语境：Sam Altman 展望 AI 发展的第三阶段：从响应式交互演进为时刻在后台主动协作、理解全局上下文并即时生成工具的持续伴随型智能体。

支持 → all-in-satya-nadella-20260915

他当时在电脑上运行着这个动态界面，看起来就像是在操作任务控制中心，图形与一切信息尽收眼底。我惊呼这太不可思议了。他对 Salesforce 正在发生的一切拥有绝对的指挥与控制权。但这与我所见过的任何传统应用程序截然不同，因为它是以智能、组合的方式在运行时动态即时渲染出来的。

he was running this dynamic interface on his computer and it looked like he was using, you know, mission control and the graphics, everything. I'm like, this is incredible. And he had total command and control over what was happening, you know, with Salesforce. But it looked radically different from any other application I'd ever seen because it was rendering itself on the fly dynamically, intelligently, in a composite fashion

语境：Marc Benioff 描述下一代企业级软件形态：传统静态操作界面正在消亡，取而代之的是由 AI 依据实时上下文和用户控制意图即时组合渲染的动态任务控制中心。

支持 → dreamforce-2026-marc-benioff-20260916

将 AI 研究本身——即人类构思、实现和验证想法的过程自动化……当 AI 开始辅助人类完成这一闭环时，根据定义它就变成了自我改进的 AI，因为它在对自己展开研究。这里存在许多概念误解，很多人以为自动代码研究就已经等同于递归自我改进了。

the ai research itself like the human uh process of ideulating implementing and validating ideas […] and when you have AI then help you with that it by almost definition becomes a self-improving AI because it now does research on itself and there are lots of different misnomers some people think auto research is already recursive self-improve improvement.

语境：定义自动化研究（Auto Research）与真正递归自我改进（RSI）的关键界限：AI 辅助人类编写训练代码只是初级自动化，真正的自我递归要求智能体在开放式环境中能够自主修改自身底层架构并完成闭环验证。

支持 → latent-space-john-platt-20260922

支持 → sequoia-sutton-20260818