OpenAI 正在为一切打造 AI Agent，所有人都会使用它们吗？

你愿意让 LLM 在多大程度上掌控你的数字生活？

要从模型中获得最大价值，就意味着把钥匙交给它。对于控制欲强的人或对 AI 持犹豫态度的人来说，这似乎要求很高。对于 OpenAI 桌面应用负责人、首席工程师 Andrew Ambrosino 而言，这是测试未来的唯一方式。因此，这款应用如今可以访问并控制他的收件箱、Slack 账户、手机、Notion 和 Figma 等应用，以及更多内容。

“如果我让它写一份文档，它有没有可能从与该主题相关的私密私信中提取内容，却不知道某些信息不该分享？有可能，”Ambrosino 告诉 TechCrunch，“但为了工作我会这么做。必要时，我愿意偶尔承担一点个人层面的代价。而且我至今还没有不得不承担这种代价。”

Ambrosino 所负责的是 OpenAI 最大的一项押注：ChatGPT Work。该产品于上月发布，在公司最低档订阅中即可使用，价格为每月 20 美元。该产品旨在让白领员工部署 AI Agent——将 LLM 接入会计师、投资人、医生，以及所有日常工作主要围绕电脑展开的人所使用的数字工作流。

OpenAI 的营销文案简明扼要地表述了这一目标：构建一个“[人工]智能不再止于回答问题，而是帮助每个人将最宏大的想法变为现实”的世界。

对于软件开发者而言，这种转变已经在发生，但向其他部门扩散的速度一直较慢。ChatGPT Work 是该公司 Codex 编程工具的改造版本。它旨在为非工程师提供与软件工程师已经从 Agent 获得的同类能力：一种不仅回答问题，还能自行完成多步骤项目的 AI 工具。

“在这个新形态中，ChatGPT 实际上能够以令人愉悦且安全的方式，完全自主地为你完成整个非常复杂的任务，”负责包括 Work 在内的 OpenAI 核心产品工作的 Thibault Sottiaux 告诉 TechCrunch，“这正是 OpenAI 的使命——让每个人都参与进来。”

从商业角度而言，这极其重要。工作时间更长的 Agent 会消耗更多 token，因此对 OpenAI 来说，每位用户带来的收入也更高。触达新的职业群体至关重要——不仅对 OpenAI 是如此，对整个行业也是如此。如果说编程已被证明是 AI 实验室颇具盈利潜力的领域，那么它仍只是 AI 工具需要赋能的专业工作中的一小部分；只有拓展至更广泛的工作领域，这些公司才可能证明其在训练和算力上的巨额投资是合理的。当实验室专注于软件工程师时，Harvey（面向法律）和 Clay（面向销售）等垂直领域竞争对手，则通过模型无关的方法争夺这些客户，这意味着它们会随时接入当下效果最好的 AI。

行业分析师认为，这是 OpenAI 及其竞争对手面临的重大挑战之一。“如果实验室无法迅速获得在市场中规模化 AI 所需的关键互补资产，价值将流向其他地方，”Christian Catalini 在 a16z 的“It’s time to build”博客上写道。

要让 AI 应用真正为非软件工程师服务，需要更多引导。OpenAI 的非工程岗位员工，例如传播和财务团队，在使用 Codex 时，“它当时对他们是明显不友好的——会问他们代码相关的问题，并向他们展示，‘哦，这个东西的 diff 是空的’，”Ambrosino 说。他所指的是一种面向软件变更的技术读出。“因此，从 2 月到现在，我们开始让它变得更通用。”

一张展示 OpenAI 订阅用户中 Codex 使用情况的图表，来自《向 Agentic AI 的转变：来自 Codex 的证据》。**图片来源：**Johnston 等人。

一项由 OpenAI 支持的研究发现，6 月时，98% 的 OpenAI 员工都在使用 Codex，但使用这款 Agentic 编程工具的组织订阅用户仅占 17%，个人订阅用户则不足 1%。公司内部近乎全面采用、公司外部却几乎无人使用之间的差距，既是该公司面临的挑战，也是其机会。

“我们为用户创造的价值和实用性越多，他们就越愿意为其中一部分实用性付费，而这也一直是我们看待 ChatGPT 的方式，”Sottiaux 表示。“你会坐在那里想，‘我当然愿意每月为这个付 20 美元’，因为你获得的价值要大得多。”

如何让 AI 变得直观

要理解这种脱节，首先需要理解 OpenAI 工程师正在构建什么。每一个 LLM 都需要工程师所说的“harness（框架层）”——即包裹在模型外的软件，它决定模型能够看到哪些信息、可以使用哪些工具，以及如何将回答呈现给你。

如果你希望模型做事——让它成为 Agent——这个框架层就会为它提供工具和使用这些工具执行长期任务的指令。对于开发者而言，一个让 LLM 能够编程的命令行界面（CLI），已经足以改变软件构建和部署的方式。但大多数人并不使用 CLI；Windows 取代 DOS 是有原因的。

“一个超越软件工程的 Agent 产品，将会与混乱的现实生活、你的各种工具，以及那些 1995 年构建后从未更新的网站打交道，”Ambrosino 告诉 TechCrunch，并解释称，其团队构建的这些体验对于扩大实用 AI 的可及性至关重要。

以 Claude Code 和 Codex 等应用为例：它们通过抽象掉所有实际的软件编写工作，并让用户只需告诉模型自己希望程序实现什么，从而释放了“vibe coding”的潜力。如今，OpenAI 希望将 OpenClaw 等工具中的能力变得像输入提示词一样简单；程序员目前正使用这类工具让 LLM 投入工作。

“如果没有这些位于模型前方的产品，专家也知道如何获得同样的结果，但你不可能让 10 亿人使用这个东西，”Ambrosino 说。高级用户所需的能力与主流采用所要求的产品设计之间的这种权衡，也体现在 OpenAI 的内部争论中：一些员工认为，如果用户可以直接向模型提问，那么按钮就是不必要的。

“我们会反驳[这种观点]——因为现在还非常早期，”Ambrosino 说。“在这个阶段，可发现性很重要；而在某个时候，我们将不再需要这个按钮。”

Work 提供了更多用于选择项目和插件的按钮，但它的目标仍是与其他 OpenAI 产品相同的“魔法盒子”式界面。他将其比作拟物化设计，即如今正在淡出的、让数字工具看起来像其所替代实体物品的做法，例如被设计成袖珍计算器外观的计算器应用。“那并不只是俗气的设计。它实际上帮助人们进入这个领域，并完成这种过渡，”Ambrosino 说。

OpenAI 没有透露使用 Work 与 Codex 的人数分别是多少，但这款联合应用的用户仅有 2000 万，而该公司称，有超过 10 亿用户在线使用 ChatGPT 进行提示。

让 ChatGPT 获得施展技能的许可

目前，OpenAI 将这款工具定位为最适合处理常规、数据密集型协调任务。其员工正设置每周指标报告，并将电子表格变成规划工具。

我曾与一些 VC 交流，他们使用 Agent 汇集与公司相关的通信内容和分析，形成投资备忘录；也见过运营团队快速搭建定制仪表盘和数据可视化。Sam Altman 正用它来规划假期。一位 OpenAI 工程师描述说，他要求该程序查看一段有关工程问题的 Slack 对话并“制作一些图表”，随后得到了一系列富有洞见的图形。

“包括我自己在内，任何一家公司的普通员工都会面对海量信息，”负责 OpenAI 产品工程团队的 Akshay Nathan 表示。“我们解析所有可获得的信息、并据此采取行动的能力实际上相当有限。这些信息存在于各种系统记录工具中[例如 Salesforce]……ChatGPT 的价值在于，你原本已经可以访问这些信息，但现在你_真正_可以使用它们。”

那么，这或许就是 AI 布道者梦寐以求的数字个人助理。与 Claude Cowork 或 Perplexity AI 浏览 Agent 一样，ChatGPT Work 将 Agent 连接到你现有的工作空间——电子邮件、网页浏览器和大量 SaaS 平台——并让这些上下文为你服务。

当系统顺利运行时，其表现相当令人印象深刻：我让 ChatGPT Work 从我的电子邮件中提取儿子格式奇怪的幼儿园日历，并将其录入 Google Calendar，它完成了任务，为我节省了大量重复的数据录入工作。希望这样一来，我不会忘记学校的聚餐，也不会忘记安排假期托管。

我不信任 OpenAI 能够访问我的收件箱、采访来源或稿件草稿（反 AI 的人请放心），也不会允许它访问我的银行账户；但我相信，如果我有这种信任，它会更有用。我让它对我所报道的上市公司进行财务分析，它为我制作了一个可自动更新的指标仪表盘；它还建立了一个可查询的太空发射数据库——此前我必须通过编写 Python 脚本来完成这项任务。它还会每周向我发送一封邮件，介绍学术资料库中发布的新 AI 研究。我会继续试用它。

向模型提出要求很直观，但向它提供采取行动所需的条件却并不简单。为 Agent 配置访问云盘等服务的权限时，过程既令人困惑又绕圈子——我多次尝试只授予它“读取”权限，却收到错误消息。模型本身也没能提供太多帮助；最终在移动端应用中，一个对话框弹出，告诉我只有授予完整访问权限才能让它正常工作。

许多重要设置只能在网页应用中使用，因此我经常发现自己必须同时操作两端。有时 ChatGPT Work 的限制令人费解——将它连接到 Google Calendar 后，它可以创建活动，但不能创建新的日历。如果不把 effort level 设置为高，最好别试图让它做任何事，否则你得到的将是你合作过的最糟糕的实习生。

这是 AI 早期采用者常给出的建议，他们担心受挫的新手会放弃。OpenAI 框架层的工程负责人 Joe Gershenson 承认，effort 设置对新用户而言尚不直观——“我们还可以做得更好，帮助他们获得合适程度的推理……”他说，并补充道：“请继续关注。”

OpenAI 在打入普通白领工作时还面临另一个重要挑战：大多数工作流不像代码那样容易衡量，或进行评估。软件要么能运行，要么不能运行，而即便这种区分也简化了判断代码优劣所涉及的细微差异。一份好的演示文稿、商业战略或销售话术，并不那么容易评估或追溯。

“这类产品的独特挑战之一是，它真的几乎什么都能做，”Ambrosino 说。当我询问团队围绕哪些具体问题进行设计、瞄准哪些工作流时，我采访的工程师都没有正面回答，称这是应向 OpenAI 研究团队提出的问题。

OpenAI 后来给出了答案，告诉 TechCrunch，该公司使用其基准测试 GDPval，该测试涵盖 44 种职业和数百项知识工作测试，并以用户反馈作为补充。另一个不那么正式的答案是，它来自 OpenAI 员工自身。Ambrosino 表示：“我们必须始终厘清……我们做的是不是其他所有人也会做的工作流，还是我们其实很奇怪？”

该应用的早期采用者将通过实际使用留下宝贵的轨迹——编程工具的大部分成功也建立在类似的数据收集之上——前提是他们没有选择退出、不让这些数据用于训练。（我选择了退出。）

推动 OpenAI 产品设计的竞争

尽管围绕模型界面的讨论很多，OpenAI 的工程师却不太愿意回答一个相当简单的问题：Codex 和 ChatGPT Work 与 Claude Cowork，或其他面向大众用户的竞争性 Agent 框架层相比，究竟有何不同？

“这对你来说会是一个非常令人失望的答案，我很抱歉，但诚实的答案是，我确实不怎么看他们构建的框架层，”Gershenson 给出了一个典型回答。“我想到的是《广告狂人》中‘我根本没想过你’这个梗。”

坦率地说，我不相信他们——仅从这些产品用户界面的极端相似性、任何企业对竞争情报的需求，以及我首次启动 ChatGPT Work 时它做的第一件事就是要求我迁移 Claude Cowork 数据来看，我都很难相信。

如果说 Claude Code 是 OpenAI 办公室里的敏感话题，这也可以理解。这家企业竞争对手定义了 AI 编程市场，并发起了一场改变软件工程师工作方式的革命。更令人沮丧的是，OpenAI 最先有了这个想法，却没有完全以正确方式将其实现为产品。

当 OpenAI 最初将 Codex 开发为网页应用时，工程师们有些操之过急——或者用 Ambrosino 的话说，“有点更被 AGI 冲昏了头脑”。简而言之，他们押注模型足够聪明，能够在极少用户输入的情况下，完全自行处理一项任务。

随后不久构建的 Claude Code 则围绕与用户的来回对话展开。如果你向它提出一个问题，它会审视各种可能性，并为后续推进提供三到四个选项。一旦你做出选择，它会再向前推进一些，然后再次确认，持续提供更新，并为模型和框架层留下更少的犯错空间。

Anthropic 的方法被证明更加

How much control are you willing to give an LLM over your digital life?

Getting the most value from a model means giving it the keys. For a control freak or the AI-hesitant, it seems like a lot. For Andrew Ambrosino, the lead engineer for OpenAI’s desktop app, it’s the only way to test the future, which is why that app now has access to, and control over, his inbox, his Slack account, his phone, apps like Notion and Figma, and more.

“If I’m asking it to write a document, is there a possibility that it’s going to pull from a private DM on that subject and not know that it’s not supposed to share some info? Yes,” Ambrosino told TechCrunch. “I’ll do it for the job. I will take the personal hit here and there if I have to. And I haven’t had to.”

Ambrosino works on OpenAI’s biggest bet, ChatGPT Work, which was released last month and is available on the company’s lowest subscription tier, for $20 a month. The product is intended to allow white-collar workers to field AI agents — hooking LLMs up to the digital workflows used by accountants, investors, doctors, and everyone else whose day-to-day is dominated by their computer.

OpenAI’s marketing copy puts the goal succinctly: A world where “where [artificial] intelligence goes beyond answering questions to helping everyone turn their biggest ideas into reality.”

For software developers, that shift is already happening, but it’s been slow to spread to other departments. ChatGPT Work is a modified version of the company’s Codex coding tool. It’s meant to give non-engineers a version of the same functionality that software engineers already get from agents: an AI tool that doesn’t just answer questions, but completes multistep projects on its own.

“In this new factor, ChatGPT can actually do entire, very complicated tasks for you all autonomously in a way that is delightful and safe,” Thibault Sottiaux, who leads OpenAI’s core product work, including Work, told TechCrunch. “It’s the very mission of OpenAI — to bring everyone along.”

Commercially, that matters a lot. Agents that work for longer stretches burn through more tokens, which makes them more lucrative for OpenAI on a per-user basis. Reaching new professions is crucial — not just for OpenAI, but for the industry at large. If coding has proven lucrative territory for AI labs, it’s still a tiny subset of the professional work AI tools need to enable if these companies are to justify their massive investment in training and computation. While labs have been focused on software engineers, vertical-specific competitors like Harvey (for law) and Clay (for sales) have been chasing those customers with a model-agnostic approach, meaning they’ll plug in whichever AI works best at the time.

Industry analysts see this as one of the major challenges facing OpenAI and its competitors. “If the labs cannot rapidly get ahold of the key complementary assets needed to scale AI in the market, value will accrue elsewhere,” Christian Catalini wrote on a16z’s “It’s time to build” blog.

Making the AI apps work for people who aren’t software engineers requires more hand-holding. OpenAI’s non-engineering workforce, like the communications and finance teams, started using Codex “at a time that it was actively hostile to them—asking them about code and showing them, ‘oh, you have an empty diff for this thing,’” Ambrosino said, referring to a technical readout meant for software changes. “So, we started to make it more general purpose between February and now.”

A chart showing Codex usage among OpenAI subscribers, from “The Shift to Agentic AI: Evidence from Codex.”Image Credits: Johnston et. al.

An OpenAI-backed study found that in June, 98% of OpenAI employees were using Codex, but just 17% of organizational subscribers and less than 1% of individual subscribers were using the agentic coding tool. That difference between near total adoption inside the company and negligible adoption outside it is the challenge and opportunity for the company.

“The more value and the more utility that we generate for users, the more they will be willing to also pay for some part of that utility, and that’s how we’ve always seen ChatGPT as well,” Sottiaux said. “You sit there and you’re like, ’of course I want to pay $20 bucks a month for this,’ because the value that you get is so much more.”

How to make AI intuitive

To understand that disconnect, it helps to understand what OpenAI’s engineers are building. Every LLM requires what engineers call a “harness” — the software wrapped around a model that decides what information it sees, which tools it can use, and how it presents its answers back to you.

If you want that model to do stuff — to become an agent — the harness gives it tools and instructions for using them on long-term tasks. For developers, a command-line interface (CLI) that enabled LLMs to code was enough to change the way software was built and deployed. But most people aren’t using CLIs; there’s a reason Windows replaced DOS.

An agentic product that goes beyond software engineering is “going to be something that plays with the messy world of your life and your tools and websites that were built in 1995 and never updated,” Ambrosino told TechCrunch, explaining that the experiences his team is building are vital to expanding access to useful AI.

Consider apps like Claude Code and Codex: They unleashed “vibe coding” by abstracting away all the actual software writing, and letting users just tell the model what they want in a program. Now, OpenAI wants to make functionality found in tools like OpenClaw, which coders use to put LLMs to work, as easy as prompting.

“Without these products in front of the model, experts would know how to get the same results, but you wouldn’t get to a billion people using the thing,” Ambrosino said. That trade-off between what power users need and what mainstream adoption requires plays out in internal debates at OpenAI, where some employees argue that a button is unnecessary if users can just ask the model directly.

“We push back on [that] — because it’s very early,” Ambrosino said. “Discoverability matters in this phase, and at some point we won’t have the button.”

Work has a few more buttons for selecting projects and plug-ins, but it aims for the same magic box interface as other OpenAI products. He compares it to skeuomorphism, the fading practice of making digital tools look like the physical objects they replaced, like a calculator app made to look like a pocket calculator. “That stuff wasn’t just cringe design. That actually helped get people into this [and] make the transition,” Ambrosino said.

OpenAI wouldn’t say how many people used Work versus Codex, but the joint app is used by just 20 million people, compared to more than a billion users the company says are prompting ChatGPT online.

Giving ChatGPT a license to skill

For now, OpenAI is pitching this tool as best suited for routine, data-intensive coordination tasks. Its employees are setting up weekly metrics reports, for example, and making spreadsheets into planning tools.

I’ve spoken to VCs using agents to assemble relevant communications and analysis about companies into investment memos, and ops teams spinning up bespoke dashboards and data visualizations. Sam Altman is using it to plan his vacations. One OpenAI engineer described asking the program to look at a Slack conversation about an engineering problem and “make some charts,” then receiving back a series of insightful plots.

“There is a deluge of information for the average worker or employee of any of these companies, including myself,” Akshay Nathan, who leads the product engineering team at OpenAI, said. “We’re actually quite limited by our ability to parse everything that’s available to us, and then take action on it. That information lives in all these system records tools [like, Salesforce]…the value of ChatGPT is you already have access to this, but now you truly have access to it.”

This, then, could be the digital personal assistant that AI evangelists dream about. As with Claude Cowork or Perplexity AI browsing agent, ChatGPT Work links agents to your existing workspace — email, web browser, a slew of SaaS platforms — and puts that context to work for you.

When the system works, it can be impressive: I asked ChatGPT Work to get my son’s weirdly-formatted preschool calendar out of my email and put it into my Google Calendar, and it did, saving me a lot of repetitive data entry. Hopefully now I won’t forget the school potluck or fail to arrange vacation childcare.

I didn’t trust OpenAI with access to my inbox, source interviews, or story drafts (fear not, AI haters) and wouldn’t let it have access to my bank account, but I believe it would be more useful had I the faith. I tasked it to do financial analysis on publicly traded companies that I cover, and it delivered an auto-updating dashboard of metrics for me; it made a queryable database of space launches, a task I’d previously had to accomplish by writing Python scripts. It also sends me a weekly email about new AI research posted at academic clearinghouses. I’ll keep experimenting with it.

While asking the model for something is intuitive, giving it what it needs to take action isn’t as simple. Setting up the permissions for agents to access, say, a cloud drive was confusing and circular — I tried multiple times to give it just “read” access and received error messages. The model itself wasn’t too helpful, but eventually on the mobile app, a dialog box popped up to tell me that only complete access would make it work.

Many important settings are only available on the web app, so I frequently found myself working in both at the same time. Sometimes ChatGPT Work’s limitations are baffling — link it to your Google calendar and it can create events, but not new calendars. And don’t bother trying to do anything unless the effort level is high, otherwise you’ve got the worst intern you’ve ever worked with.

That’s common advice from AI early adopters, who fear that frustrated newbies will give up. Joe Gershenson, the engineering lead for OpenAI’s harness, admitted that effort settings aren’t intuitive for new users yet — ”there are things that we can do better to help them get the right level of reasoning…” he said, adding, “Watch this space.”

OpenAI faces another important challenge breaking into normie white-collar work: Most workflows aren’t as measurable — or evaluable — as code. Software either works or it doesn’t, and even that distinction reduces the nuance about what makes code good or bad. A good presentation, business strategy, or sales pitch isn’t as easy to evaluate or trace.

“One of the unique challenges with a product like this is just that it can really do anything,” Ambrosino said. When I asked which specific problems the team designs around, and which workflows it targets, the engineers I spoke with demurred, saying that was a question for OpenAI’s research team.

OpenAI later provided an answer, telling TechCrunch that it uses its benchmark GDPval, drawn from 44 occupations and hundreds of knowledge work tests, and supplements that with user feedback. A less official answer is that it comes from OpenAI employees themselves. Said Ambrosino, “We have to always parse out … are we doing the workflow that everybody else will be doing, or are we weird?”

The early adopters of the app itself will create valuable traces with their actual usage — much of the success of coding tools is built on similar data collection — assuming they don’t opt out of making it available for training. (I did).

The rivalry that drove OpenAI’s product design

Despite all the attention on the model interface, OpenAI’s engineers were reluctant to answer a fairly simple question: What sets Codex and ChatGPT Work apart from Claude Cowork, or other competing agentic harnesses intended for a mass user base?

“It’s going to be a really disappointing answer for you, and I’m sorry, but the honest answer is that I really don’t look at the harnesses that they’re building,” Gershenson said in a typical answer. “The Mad Men ‘I don’t think about you at all’ meme comes to mind here.”

Frankly, I don’t believe them, if only based on the extreme similarities between the products’ user interfaces, the need for competitive intelligence at any business, and because the first thing ChatGPT Work asked me to do when I started it up was port over my Claude Cowork data.

It’s understandable if Claude Code is a sensitive topic around the OpenAI offices. Their corporate rival defined the market for AI coding and launched a revolution in how software engineers do their jobs. It’s additionally frustrating because OpenAI had the idea first, but didn’t quite harness it correctly.

When OpenAI first developed Codex as a web app, the engineers got a bit over their skis — or, as Ambrosino puts it, “a bit more AGI-pilled.” In short, they bet on the model being smart enough to handle a task entirely on its own, with minimal user input.

Built shortly afterward, Claude Code was oriented around a back-and-forth conversation with the user. If you gave it a problem, it would survey the possibilities and give you three or four options for proceeding. Once you chose, it would go a little further and then check back again, giving continual updates and leaving less room for the model and harness to make mistakes.

Anthropic’s approach proved more effective, even if it demanded more work from users. “[Our] product was a little ahead of where the model and harness was at the time,” Ambrosino says now.

OpenAI eventually followed suit by adding more opportunities for users to interact with the model. That became the Codex we know today, with desktop and mobile apps. Using download statistics as a proxy for interest in the programs, Claude Code was more in demand until April of this year, but now Codex has taken a slight lead; surveys of enterprise use also suggest OpenAI is catching up.

Part of that lead is getting the product-market fit right, and part comes from complaints about safety restrictions on Anthropic’s models and compute shortages. OpenAI’s steps toward more human-centric harness continue with ChatGPT Work, but the engineers I spoke to insisted the key differentiator is the strength of OpenAI’s latest powerful and cost-effective models.

“The frustrating answer is that a lot of times it is the model, and one thing that we have tried to do really well with this app is fully leverage the model,” Ambrosino said.

What makes a good harness, anyway?

That explanation returns to the “bitter lesson” learned by AI researchers that a better general model is more important than specific domain experience. For true believers, the harness is a temporary crutch, not the moat.

“You could get good results in the short term by adding a whole bunch of extras — if and thens and tools — but like, come on, the next model is going to come out in a couple of months and make that obsolete,” Gershenson told TechCrunch. His team focuses on the simplest ways to expose the model to the tools and context it needs — and no more.

“The goal of good harness engineering is to… be more precise about what information the model really needs to solve your problem, because the models are getting better and better at doing that if you simply let them do their thing,” Gershenson said.

There is an open question, though, if most people or models are ready for that. Ethan Mollick, the Wharton School of Business professor who studies AI tools in the workplace, still sees Claude as more user-friendly, writing that “ChatGPT tends to want to do magic & just do it for you, while Claude does comparisons & shows them, repeatedly asking for input & feedback and doing A & B tests.”

Sottiaux, and perhaps OpenAI at large, disagree, arguing that the conversational nature of the app is better than learning how to use an application. “We definitely see that the world seems to be ready,” he says of the app. ”This is why we’ve had incredible adoption.”

Still, it’s not clear that a model-specific harness is even the right bet for maximizing a model. Comparisons run by companies like Composio and Databricks show that different harness and model combinations deliver different performance on coding benchmarks. Databricks found that Pi, an open source harness published by the software company Earendil, outperformed Codex while using the same GPT 5.5 model. Pi has been used to build projects like OpenClaw and Cloudflare OS.

Pi’s creator, Mario Zechner, says his intentionally minimalist harness is evidence that an AGI-pilled approach can work, at least for software engineers and coding tasks. What it lacks in explicit features, he says, is made up for by its ability to modify itself and build its own interfaces. He sympathizes with the challenge that OpenAI’s engineers face in expanding their user base beyond engineers.

“Everything is coding agent shaped…the reason is that they only have training data for coding agent tasks,” he told TechCrunch. “Say I’m in management, I make a decision today, and the outcome happens months later. You cannot capture that in a simple trace of a user and agent back and forth, so all of these kinds of tasks and anything that you don’t digitize is inaccessible to a model to learn.”

Like other open source providers, he sees the big lab’s effort to push their harnesses as a way to lock-in users; ”They need to own the entire stack; otherwise, they just become a model provider and then need to compete with Chinese models.”

He and other engineers TechCrunch spoke to felt that the insight into token spend and agent behavior in frontier labs’ harnesses is too limited. In a sense, that’s less meaningful to non-technical workers, but as with the coding tools, uptake at the scale OpenAI hopes for will eventually force harder conversations about cost.

For example, messing around on a $20-a-month subscription, I used more than 80 million tokens in four days, which cost $65, according to the model’s analysis (there’s no dashboard in the app). That’s a subsidy of more than 3x the subscription price for four days of casual use alone.

“We are working every day to push the frontier on efficiency,” Sottiaux said, pointing to a recent 80% price cut for users of OpenAI’s Luna model. “If you wake up six months from now, you should be able to do all of the same [tasks] with less spend.”

The other relevant question is whether these apps create a lock-in effect on customers through data retention, or the sheer pain of configuring access to all the plug-ins and their permissions.

Inside OpenAI’s wood-paneled, plant-filled headquarters, which I visited in July, the atmosphere was calm but slightly tense; these are people with a lot to do. The engineers I spoke with were constantly monitoring their laptops as we talked, and rushing from meeting room to meeting room.

Nathan, the head of the product engineering team, said the focus remains on “the promise of the magic box, but I still think there’s too much complexity…I’m very optimistic that we can solve it, with the model and in a truly AI-native way.”