在周二OpenAI开发者大会（Dev Day）上，最引人注目的公告之一来自首席执行官萨姆·阿尔特曼（Sam Altman）的随口提及，他透露了公司新的“Decisions API”。

该API似乎提供了与Jev类似的功能。Jev是TypeSafe AI本月早些时候发布的一个模型，专为软件自动化而设计。这是一种构建在大型语言模型（LLM）之上的超级分类器，开发者可以为其提供一组选项，它以低成本和高速度输出这些选项的概率。

OpenAI的Decisions API似乎属于同类产品。在活动中，阿尔特曼将这款API描述为一种让实验室的Luna模型在预定义的一组选项中做出选择的方法，例如对图像进行分类的类别或不同的智能体行为。

“通过让模型专注于这一选择，我们可以在保持图像理解、广泛语言支持和安全保护等能力的同时，使其速度极快。”阿尔特曼表示。

TypeSafe未就新产品向TechCrunch提出问题作出回应，但其首席执行官迪奥戈·阿尔梅达（Diogo Almeida）——一位曾发明强化学习的OpenAI前工程师——在X平台上开玩笑地提到了“克隆人战争”的开始。

他补充说，OpenAI的兴趣可能是一个信号，“表明以兼容System One的方式构建是未来。”（“System One”是TypeSafe的术语，指快速、直觉性的思维，而“System 2”则指深思熟虑的推理。）

这里的潜台词是，我们熟知的大型语言模型并不是许多软件的正确解决方案，因为它们相对较慢且昂贵。开发者一直在使用Jev来增强大型语言模型的能力，并在此过程中发现它们更快、更便宜。

目前尚不清楚Decisions API与Jev有多相似，因为OpenAI仅将其作为有限预览版发布，迄今为止，TechCrunch尚未看到开发者对其进行全面测试。然而，根据X平台上的讨论，显然存在兴趣。

Decisions API并非互联网上唯一的类Jev API——其他初创公司也在推出类似模型；OpenAI不会是最后一家推出此类产品的科技巨头。一个关键问题是，这些决策模型的输出与现实生活的校准程度如何。

阿尔梅达表示，其公司的护城河在于它创建的合成数据，以生成具有统计意义的输出。

“快速且便宜非常容易，你知道的。”阿尔梅达上周告诉TechCrunch，“如果你想要真正快速且便宜，就用骰子，对吧？智能才是难点，我的北极星始终是推动每美元智能的帕累托前沿曲线。”

仅仅几周时间，似乎就很清楚这些模型拥有广阔的未来前景，而一个可能的应用场景是监控和保护AI智能体。在发生了一系列其智能体在互联网上行为不端的事故后，OpenAI采取的一项新安全措施是使用单独的模型来监视不良行为，但这需要“巨大的计算成本”。

长期网络安全专家、初创公司QueryStory的领导者Shapor Naghibzadeh认为，像Jev这样的模型可以以远低于此的成本实现这一目标。

他在上周末举行的一次黑客松比赛中构建了一个演示程序，该程序使用Jev将每个智能体操作与其被分配的任务进行比对，阻止其高度确信为不良的操作，标记其他操作以供审查，并允许其余操作通过。

理论上，此类监控本可以阻止Hugging Face事件的发生——而使用Jev进行此类监控的成本为2.94美元，相比之下，使用前沿大型语言模型的成本为372美元。

一个关键的观察结果是，Jev便宜到足以在每个智能体操作上运行，这提供了一层审查机制，从而可以提高整体智能体的可靠性。这正是TypeSafe希望实现的目标——而现在OpenAI也看到了其中的价值。

One of the more intriguing announcements at OpenAI’s Dev Day event on Tuesday came in an aside from CEO Sam Altman, who revealed the company’s new “Decisions API.”

The API apparently provides similar functionality to Jev , a model released by TypeSafe AI earlier this month that’s explicitly designed for software automation. A kind of super-powered classifier built on an LLM, developers can give Jev a set of choices that it outputs as probabilities cheaply and at high speeds.

OpenAI’s Decisions API seems to be the same sort of product. At the event, Altman described the API as a way to give the lab’s Luna model a predefined set of options to choose between, such as categories in which to classify an image or different agent behaviors.

“By focusing the model on that choice, we can make it extremely fast while keeping capabilities like image understanding, broad language support, and safety protections,” Altman said.

TypeSafe didn’t respond to TechCrunch’s questions about the new product, but CEO Diogo Almeida, a former OpenAI engineer who co-invented reinforcement learning, joked on X about the beginning of the clone wars.

He added that OpenAI’s interest could be “a sign…that building in a System One compatible way is the future.” (“System One” is TypeSafe’s term of art for fast, intuitive thinking, versus “System 2,” which it applies to deliberate reasoning.)

The subtext here is that LLMs as we know them aren’t the right solution for a lot of software because they are comparatively slow and expensive. Developers have been using Jev to augment LLMs and, in doing so, have found that they’re faster and cheaper.

It’s not clear how similar Decisions API will be to Jev, since OpenAI released it as a limited preview and, thus far, TechCrunch hasn’t spotted developers running it through its paces. However, there is clearly interest , according to the conversations on X.

Decisions API isn’t the only Jev-like API on the internet — other startups are rolling out similar models; OpenAI won’t be the last tech giant to produce one. A key question is how well calibrated each of these decision models’ outputs will be to real life.

Almeida says his company’s moat is the synthetic data it creates to generate statistically useful outputs.

“Fast and cheap is very easy, you know,” Almeida told TechCrunch last week. “If you want it really fast and cheap, use dice, right? Intelligence is the hard part, and my North Star is always pushing the intelligence-per-dollar Pareto curve.”

After just weeks, it seems clear that these models have a future ahead of them, and one likely application is monitoring and securing AI agents. One of OpenAI’s new security measures following a series of incidents where its agents misbehaved on the open internet is using a separate model to watch for bad actions at “significant compute cost.”

Shapor Naghibzadeh, a long-time cybersecurity professional who leads the startup QueryStory, thinks that a model like Jev could make that possible far more cheaply.

He built a demo for a hackathon held last weekend that uses Jev to check each agentic action against the task it was given, blocking actions it had high confidence were bad, flagging others for review, and permitting the rest.

In theory, such monitoring could have stopped the Hugging Face incident — and monitoring of that kind costs $2.94 with Jev, versus $372 with a frontier LLM.

A key observation is that Jev is arguably cheap enough to run on every agentic action, which offers a layer of review that could improve the reliability of agents writ large. It’s the kind of thing TypeSafe was hoping to achieve — and now OpenAI has seen the value as well.