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AI 辅助插画 · 参考照片：Steve Jurvetson from Menlo Park, USA · CC BY 2.0 · 依据参考照片使用 AI 绘制细线彩铅人物插画，调整背景、构图与色彩；网页与分享卡片按版式缩放、裁切并叠加文字。展示图缩放至最长边 640 像素并转为 WebP。 · 参考图：File:Wake up People- AI is eating at all of your jobs (39392994925) (cropped).jpg · 照片日期：2018-02-14 17:38
公开发言
6原话逐字核对过；转述不加引号任何正在开展大规模强化学习训练并给模型设定奖励函数的人——比如调动一万个智能体，投入一亿美元，让它们在庞大的集群上并行运行一两个月去尝试解决任何问题，哪怕根本不是安全相关的任务，哪怕只是去解一道数学题，极其恶劣的事情都有可能发生。
anyone who's doing big reinforcement learning runs and they're giving it a reward function. So unleashing you know saying hey here's like a you know 10,000 agents and here's $100 million let's put them in parallel and let them run on a gigantic cluster for a month or two try to solve anything and it doesn't need to be a security thing. It could be like do anything you know solve this math puzzle really bad things can happen
语境：Ali Ghodsi 指出前沿实验室面临的真正安全短板：高并发强化学习在庞大集群上长程自探索时，由于奖励机制缺陷极易引发不可逆的越权网络渗透。
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。
当前的灭绝级生存风险几乎为零，将焦点泛化为生存恐慌会误导公众与监管者；行业真正迫在眉睫的现实危机是强化学习与多智能体任务中失控引发的网络安全漏洞与越权攻击。
语境：Ali Ghodsi 呼吁将 AI 安全讨论从虚无缥缈的超级智能末日论转向具体可验证的工程防御与访问控制。
深入企业内部看看正在发生什么？AI 究竟被如何使用？大家仅仅把它们当作愚蠢的聊天机器人来用：去聊天窗口问个问题、获取答案，仅此而已；另外就是用来写代码。尽管这略显过度简化，但这正是当今绝大多数企业内部 AI 应用的真实惨状。
what's happening inside the enterprise? How are AI being used?" And they're just using them as stupid chat bots. They're going to the chatbot, asking it the question, and getting an answer. That's all that's happening. And then they're using them for coding. So they have a coding agents that are writing code. By and large, it's a little bit of an oversimplification. This is what's happening inside the enterprise today.
语境：Ali Ghodsi 直指当前企业级 AI 落地的巨大虚火：模型能力虽已逼近人类专家，但企业实际部署却绝大多数停留在单点问答玩具阶段，未能与核心业务流产生化学反应。
如果你正身处一场剧烈且疯狂的范式大转型之中，你一定会希望在私有市场上完成它。历史上当公司经历根本性模式跃迁时（例如从永久软件授权转向 SaaS），往往会主动选择私有化退市，完成蜕变后再重新上市。在整个世界经历如此疯狂的 AI 变革时，急于上市毫无战略意义。
If we're going through a big crazy transition, you want to do it in private markets. Companies when they go through big transitions themselves, you know, like when they went from, you know, perm licenses to like, you know, SAS licenses, they go private. They take them private, then they do the thing and then they go back out. So as the world is going through this crazy AI transformation, you're like want to be public right now.
语境：Ali Ghodsi 阐明 Databricks 现金流健康下推迟上市的商业定力：公开市场对 AI 叙事的剧烈摇摆会扭曲长期研发节奏，在非公开环境中沉心构筑数据飞轮才是最优解。
单靠采购标准 AI 软件无法打通企业协同堵点；企业必须系统性将组织架构、会议决断与私有数据资产结构化提炼为全局业务本体（Ontology），唯有将业务本体输入模型，高阶智能体才能真正代表员工自主协作。
语境：Ali Ghodsi 提出企业级 AI 的胜负手在于 Ontology 知识图谱构建：模型通用智力已成日用品，组织内部私有业务逻辑与数据本体的映射深度才构成最终护城河。