OpenAI正倾尽全力打造一款全自动研究助手。
内容总结：
OpenAI近日宣布，将其未来数年的核心战略目标定为打造“全自动AI研究员”。该公司首席科学家雅库布·帕乔斯基在接受专访时透露，这一系统将能自主攻克庞大复杂的科学难题，成为公司未来发展的“北极星”。
根据规划，OpenAI计划在今年9月前率先推出“自主AI研究实习员”，专注于解决少量特定研究问题。以此为基石，公司目标在2028年推出完整的全自动多智能体研究系统。该系统预期将处理数学、物理、生命科学乃至商业政策等领域的复杂问题，其能力边界可覆盖任何能以文本、代码或草图形式表述的课题。
帕乔斯基指出，当前已部署的代码智能体Codex可视为该愿景的早期雏形。他表示：“当看到模型能完成原本需要人类耗时数周的工作时，我们相信这项技术将在近期带来更显著的加速。”但他同时坦言，实现完全自主的研究系统仍面临技术挑战，尤其是如何确保系统在长时间运行中保持连贯性与准确性。
面对自主AI可能带来的风险，OpenAI正通过“思维链监控”等技术提升系统透明度，使研究人员能实时追踪AI决策过程。帕乔斯基强调，强大模型应部署在“沙箱”环境中，并与政策制定者密切协作以管控潜在风险。
尽管业界对实现路径存在不同声音，但帕乔斯基认为，AI无需在所有维度达到人类智能水平即可产生变革性影响。他表示：“我们更关注与现实世界息息相关的研究。当代码智能体能够解决编程问题时，它就有潜力解决更广泛的科学难题。”
随着全球AI竞赛日趋激烈，OpenAI此次战略转向或将深刻影响人工智能技术的发展轨迹。
中文翻译：
OpenAI正全力打造全自动AI研究员
——与首席科学家雅库布·帕乔斯基独家对话，揭秘公司新宏伟蓝图与AI未来
OpenAI正重新聚焦研究方向，将资源倾注于一项全新的宏伟挑战。这家旧金山公司致力于打造名为“AI研究员”的全自动智能体系统，该系统能独立探索并解决庞大复杂的难题。OpenAI表示，这项新研究目标将成为未来几年的“北极星”，整合推理模型、智能体与可解释性等多个研究方向。
时间表已然明确：OpenAI计划在九月前构建“自主AI研究实习生”——一个能独立处理少量特定研究问题的系统。该AI实习生将成为2028年亮相的全自动多智能体研究系统的前奏。据OpenAI称，这款AI研究员将能应对人类难以处理的超大规模或超高复杂度问题。
其任务可能涉及数学与物理学（如提出新证明或猜想）、生命科学（如生物学与化学），甚至商业与政策困境。理论上，任何能以文本、代码或白板草图形式表述的问题皆可交由该工具处理——涵盖范围极其广泛。
多年来，OpenAI始终引领AI行业发展方向。其早期在大语言模型领域的统治地位，塑造了数亿人日常使用的技术形态。但如今它正面临Anthropic、谷歌DeepMind等竞争对手的激烈角逐。OpenAI的下一步布局，对自身乃至AI未来都至关重要。
这一决策的重任落在OpenAI首席科学家雅库布·帕乔斯基肩上，他负责制定公司的长期研究目标。帕乔斯基在2023年发布的革命性大模型GPT-4，以及2024年首次亮相、如今支撑所有主流聊天机器人与智能体系统的推理模型技术研发中，均扮演了关键角色。
在本周的独家专访中，帕乔斯基向我阐述了OpenAI的最新愿景。“我认为我们正接近这样一个临界点：模型将能像人类一样持续连贯地工作，”他表示，“当然，人类仍需负责设定目标。但我相信我们会实现‘将整个研究实验室置于数据中心’的构想。”
攻坚难题
此类宏大宣言并不新鲜。通过解决世界最棘手问题来拯救地球，是所有顶尖AI公司的公开使命。德米斯·哈萨比斯早在2022年就告诉我，这正是他创立DeepMind的初衷。Anthropic首席执行官达里奥·阿莫代宣称要在数据中心打造“天才国度”。帕乔斯基的上司萨姆·奥尔特曼则立志攻克癌症。但帕乔斯基认为，OpenAI已具备实现目标所需的大部分条件。
今年1月，OpenAI发布了基于智能体的应用Codex，它能即时生成代码在计算机上执行任务，包括分析文档、生成图表、整理邮箱与社交媒体每日摘要等（其他公司也推出了类似工具，如Anthropic的Claude Code与Claude Cowork）。
OpenAI声称其大部分技术人员已在工作中使用Codex。帕乔斯基将其视为AI研究员的早期雏形：“我预计Codex将实现质的飞跃。”关键在于构建能更长时间自主运行、减少人工干预的系统。“我们对自动化研究实习生的期待是：能委派耗时数日的人工任务。”帕乔斯基解释道。
艾伦人工智能研究所科学家道格·唐尼（未参与OpenAI项目）指出：“许多人对构建能进行长期科研的系统充满热情。这主要得益于编码智能体的成功——能将大量编码任务委派给Codex类工具，既实用又令人惊叹。这引发了一个问题：我们能否在编码之外的更广泛科学领域实现类似突破？”
对帕乔斯基而言，答案显然是肯定的。他认为只需沿现有路径持续推进即可。他指出，从2020年的GPT-3到2023年的GPT-4，模型无需专门训练就能更持久地处理问题。推理模型的出现带来了新突破：通过训练大模型逐步解决问题、在出错或遇阻时回溯，也提升了模型的长期工作能力。帕乔斯基坚信OpenAI的推理模型将持续改进。
同时，OpenAI正通过喂食复杂任务样本（如数学与编程竞赛中的难题）训练系统长期自主工作，迫使模型学习处理超长文本、拆分并管理多重子任务。帕乔斯基强调目标并非打造仅能赢比赛的模型：“这让我们在连接现实世界前验证技术可行性。若真有意，我们完全可以构建卓越的自动化数学家——我们拥有全部工具且相对容易实现。但这并非当前优先事项，因为当确信能做到时，总有更紧迫的任务待办。”
“我们更关注与现实世界相关的研究，”他补充道。当前重点是将Codex的编码能力拓展至通用问题解决领域。“变革正在发生，尤其在编程领域。我们的工作与一年前已截然不同——不再需要持续编辑代码，而是管理一组Codex智能体。”帕乔斯基指出，既然Codex能解决编码问题，理论上就能解决任何问题。
持续突破
过去数月，OpenAI确实取得了一系列显著成果。研究人员利用驱动Codex的大模型GPT-5，为多个未解数学问题发现新解法，并在生物、化学、物理领域的若干难题中突破僵局。帕乔斯基表示：“目睹这些模型提出多数博士生至少需数周才能构思的创意，让我预见这项技术将在近期带来更强劲的加速效应。”
但他承认尚未尘埃落定，也理解为何有人质疑技术的变革程度：“这取决于人们的工作方式与需求。我相信部分人尚未感受到其巨大实用性。”帕乔斯基坦言自己一年前甚至不使用生成式编码技术的最基础形态——自动补全功能：“我对代码极其严谨，偏爱在vim编辑器中手动输入全部代码（vim是许多硬核程序员青睐的文本编辑器，通过数十个键盘快捷键而非鼠标操作）。”
但最新模型的能力改变了他的习惯。他仍不会交付复杂设计任务，但在尝试新想法时，这项技术极大提升了效率：“过去需编码一周的实验，现在一个周末就能通过它完成。”他补充道：“虽然尚未达到让它全权设计整个系统的程度，但当你目睹它完成原本需耗时一周的工作时——这种说服力难以抗拒。”
帕乔斯基的战略是强化Codex等工具现有的问题解决能力，并将其应用于跨学科领域。唐尼认同自动化研究员的概念极具吸引力：“如果明早就能看到智能体完成大量工作并呈现新成果，那将令人振奋。”但他提醒构建此类系统可能比帕乔斯基描述的更困难。去年夏季，唐尼团队在多类科学任务中测试了数个顶尖大模型，OpenAI的最新模型GPT-5虽表现最佳，仍存在大量错误。
“若需串联多个任务，连续正确完成的概率往往会降低，”唐尼指出。他承认技术迭代迅速，尚未测试两周前发布的GPT-5.4版本：“因此现有结论可能已过时。”
悬而未决的重大问题
当我问及缺乏人类监督的自主解决复杂问题系统可能带来的风险时，帕乔斯基表示OpenAI团队始终在讨论这些风险。“若你相信AI将大幅加速研究进程（包括AI研究本身），这将是世界的重大变革，”他说，“随之而来的是某些尚未解答的严肃问题：如果它如此智能强大，能运行完整研究计划，万一做出有害行为怎么办？”
帕乔斯基认为风险可能以多种形式出现：系统失控、遭黑客攻击或单纯误解指令。OpenAI当前应对这些担忧的最佳技术是训练推理模型在工作时实时分享操作细节，这种对大模型的监控方法被称为“思维链监控”。简言之，大模型被训练在任务执行过程中于“草稿纸”上记录操作笔记，研究人员借此确保模型行为符合预期。昨日OpenAI发布了内部使用思维链监控研究Codex的新细节。
“当系统能在大型数据中心长期基本自主运行时，这项技术将成为我们的重要依赖，”帕乔斯基表示。其理念是通过其他大模型监控AI研究员的“草稿纸”，在问题发生前捕捉异常行为，而非试图从一开始阻止有害行为发生——目前人类对大模型的理解尚不足以实现完全控制。
“我认为距离真正解决问题还有很长的路要走，”他说，“在完全信任系统之前，必须设置严格限制。”帕乔斯基主张将强大模型部署于沙箱环境中，隔绝一切可能破坏或用于造成伤害的资源。
AI工具已被用于策划新型网络攻击，有人担忧其可能被用于设计合成病原体制造生物武器。帕乔斯基坦言：“我们确实能设想出令人担忧的场景。这将是一种极其奇特的事物——某种上前所未有的高度集中权力。想象这样一个世界：一个数据中心能完成OpenAI或谷歌的全部工作，过去需要庞大人类组织完成的任务，未来仅需几人即可实现。”
“我认为这是政府面临的重大挑战，”他补充道。然而有人认为政府本身就是问题的一部分。例如美国政府希望将AI用于战场，近期Anthropic与五角大楼的对峙暴露出社会对技术使用红线的划分远未达成共识——更遑论由谁划定。争议发生后，OpenAI迅速与五角大楼签署协议而非跟随竞争对手。局势依然混沌不明。
我向帕乔斯基追问：他是否真信任他人能解决这些问题？作为未来的关键构建者，他是否感到个人责任？“我确实感到个人责任，”他回答，“但我不认为仅靠OpenAI以特定方式推进技术或设计产品就能解决。我们绝对需要政策制定者的深度参与。”
前路何方？
我们是否真在迈向帕乔斯基设想的那种AI？当我询问艾伦研究所的唐尼时，他笑道：“我在这个领域耕耘数十年，已不再相信自己关于技术能力远近的预测。”
OpenAI的公开使命是确保人工通用智能（许多AI推崇者认为未来这项技术能在多数认知任务上比肩人类）造福全人类，其策略是成为首个实现者。但在我们的对话中，帕乔斯基唯一提及AGI时，立即改用“经济变革性技术”来澄清所指。
他强调大模型不同于人脑：“它们在某些方面与人表面相似，因为训练数据主要是人类对话。但它们并非通过进化形成，不具备真正的高效性。”他补充道：“即使到2028年，我也不认为系统能在所有方面达到人类智能水平。但这并非绝对必要——有趣之处在于，要成为变革性力量，无需在所有维度与人比肩。”
英文来源：
OpenAI is throwing everything into building a fully automated researcher
An exclusive conversation with OpenAI’s chief scientist, Jakub Pachocki, about his firm's new grand challenge and the future of AI.
OpenAI is refocusing its research efforts and throwing its resources into a new grand challenge. The San Francisco firm has set its sights on building what it calls an AI researcher, a fully automated agent-based system that will be able to go off and tackle large, complex problems by itself. OpenAI says that this new research goal will be its “North Star” for the next few years, pulling together multiple research strands, including work on reasoning models, agents, and interpretability.
There’s even a timeline. OpenAI plans to build “an autonomous AI research intern”—a system that can take on a small number of specific research problems by itself—by September. The AI intern will be the precursor to a fully automated multi-agent research system that the company plans to debut in 2028. This AI researcher (OpenAI says) will be able to tackle problems that are too large or complex for humans to cope with.
Those tasks might be related to math and physics—such as coming up with new proofs or conjectures—or life sciences like biology and chemistry, or even business and policy dilemmas. In theory, you would throw such a tool any kind of problem that can be formulated in text, code, or whiteboard scribbles—which covers a lot.
OpenAI has been setting the agenda for the AI industry for years. Its early dominance with large language models shaped the technology that hundreds of millions of people use every day. But it now faces fierce competition from rival model makers like Anthropic and Google DeepMind. What OpenAI decides to build next matters—for itself and for the future of AI.
A big part of that decision falls to Jakub Pachocki, OpenAI’s chief scientist, who sets the company’s long-term research goals. Pachocki played key roles in the development of both GPT-4, a game-changing LLM released in 2023, and so-called reasoning models, a technology that first appeared in 2024 and now underpins all major chatbots and agent-based systems.
In an exclusive interview this week, Pachocki talked me through OpenAI’s latest vision. “I think we are getting close to a point where we’ll have models capable of working indefinitely in a coherent way just like people do,” he says. “Of course, you still want people in charge and setting the goals. But I think we will get to a point where you kind of have a whole research lab in a data center.”
Solving hard problems
Such big claims aren’t new. Saving the world by solving its hardest problems is the stated mission of all the top AI firms. Demis Hassabis told me back in 2022 that it was why he started DeepMind. Anthropic CEO Dario Amodei says he is building the equivalent of a country of geniuses in a data center. Pachocki’s boss, Sam Altman, wants to cure cancer. But Pachocki says OpenAI now has most of what it needs to get there.
In January, OpenAI released Codex, an agent-based app that can spin up code on the fly to carry out tasks on your computer. It can analyze documents, generate charts, make you a daily digest of your inbox and social media, and much more. (Other firms have released similar tools, such as Anthropic’s Claude Code and Claude Cowork.)
OpenAI claims that most of its technical staffers now use Codex in their work. You can look at Codex as a very early version of the AI researcher, says Pachocki: “I expect Codex to get fundamentally better.”
The key is to make a system that can run for longer periods of time, with less human guidance. “What we’re really looking at for an automated research intern is a system that you can delegate tasks [to] that would take a person a few days,” says Pachocki.
“There are a lot of people excited about building systems that can do more long-running scientific research,” says Doug Downey, a research scientist at the Allen Institute for AI, who is not connected to OpenAI. “I think it’s largely driven by the success of these coding agents. The fact that you can delegate quite substantial coding tasks to tools like Codex is incredibly useful and incredibly impressive. And it raises the question: Can we do similar things outside coding, in broader areas of science?”
For Pachocki, that’s a clear Yes. In fact, he thinks it’s just a matter of pushing ahead on the path we’re already on. A simple boost in all-round capability also leads to models that can work longer without help, he says. He points to the leap from 2020’s GPT-3 to 2023’s GPT-4, two of OpenAI’s previous models. GPT-4 was able to work on a problem for far longer than its predecessor, even without specialized training, he says.
So-called reasoning models brought another bump. Training LLMs to work through problems step by step, backtracking when they make a mistake or hit a dead end, has also made models better at working for longer periods of time. And Pachocki is convinced that OpenAI’s reasoning models will continue to get better.
But OpenAI is also training its systems to work by themselves for longer by feeding them specific samples of complex tasks, such as hard puzzles taken from math and coding contests, which force the models to learn how to do things like keep track of very large chunks of text and split problems up into (and then manage) multiple subtasks.
The aim isn’t to build models that just win math competitions. “That lets you prove that the technology works before you connect it to the real world,” says Pachocki. “If we really wanted to, we could build an amazing automated mathematician. We have all the tools, and I think it would be relatively easy. But it’s not something we’re going to prioritize now because, you know, at the point where you believe you can do it, there’s much more urgent things to do.”
“We are much more focused now on research that’s relevant in the real world,” he adds.
Right now that means taking what Codex can do with coding and trying to apply that to problem-solving in general. “There’s a big change happening, especially in programming,” he says. “Our jobs are now totally different than they were even a year ago. Nobody really edits code all the time anymore. Instead, you manage a group of Codex agents.” If Codex can solve coding problems (the argument goes), it can solve any problem.
The line always goes up
It’s true that OpenAI has had a handful of remarkable successes in the last few months. Researchers have used GPT-5 (the LLM that powers Codex) to discover new solutions to a number of unsolved math problems and punch through apparent dead ends in a handful of biology, chemistry, and physics puzzles.
“Just looking at these models coming up with ideas that would take most PhD weeks, at least, makes me expect that we’ll see much more acceleration coming from this technology in the near future,” Pachocki says.
But Pachocki admits that it’s not a done deal. He also understands why some people still have doubts about how much of a game-changer the technology really is. He thinks it depends on how people like to work and what they need to do. “I can believe some people don’t find it very useful yet,” he says.
He tells me that he didn’t even use autocomplete—the most basic version of generative coding tech—a year ago. “I’m very pedantic about my code,” he says. “I like to type it all manually in vim if I can help it.” (Vim is a text editor favored by many hardcore programmers that you interact with via dozens of keyboard shortcuts instead of a mouse.)
But that changed when he saw what the latest models could do. He still wouldn’t hand over complex design tasks, but it’s a time-saver when he just wants to try out a few ideas. “I can have it run experiments in a weekend that previously would have taken me like a week to code,” he says.
“I don’t think it is at the level where I would just let it take the reins and design the whole thing,” he adds. “But once you see it do something that would take a week to do—I mean, that’s hard to argue with.”
Pachocki’s game plan is to supercharge the existing problem-solving abilities that tools like Codex have now and apply them across the sciences.
Downey agrees that the idea of an automated researcher is very cool: “It would be exciting if we could come back tomorrow morning and the agent’s done a bunch of work and there’s new results we can examine,” he says.
But he cautions that building such a system could be harder than Pachocki makes out. Last summer, Downey and his colleagues tested several top-tier LLMs on a range of scientific tasks. OpenAI’s latest model, GPT-5, came out on top but still made lots of errors.
“If you have to chain tasks together, then the odds that you get several of them right in succession tend to go down,” he says. Downey admits that things move fast, and he has not tested the latest versions of GPT-5 (OpenAI released GPT-5.4 two weeks ago). “So those results might already be stale,” he says.
Serious unanswered questions
I asked Pachocki about the risks that may come with a system that can solve large, complex problems by itself with little human oversight. Pachocki says people at OpenAI talk about those risks all the time.
“If you believe that AI is about to substantially accelerate research, including AI research, that’s a big change in the world. That’s a big thing,” he told me. “And it comes with some serious unanswered questions. If it’s so smart and capable, if it can run an entire research program, what if it does something bad?”
The way Pachocki sees it, that could happen in a number of ways. The system could go off the rails. It could get hacked. Or it could simply misunderstand its instructions.
The best technique OpenAI has right now to address these concerns is to train its reasoning models to share details about what they are doing as they work. This approach to keeping tabs on LLMs is known as chain-of-thought monitoring.
In short, LLMs are trained to jot down notes about what they are doing in a kind of scratch pad as they step through tasks. Researchers can then use those notes to make sure a model is behaving as expected. Yesterday OpenAI published new details on how it is using chain-of-thought monitoring in house to study Codex.
“Once we get to systems working mostly autonomously for a long time in a big data center, I think this will be something that we’re really going to depend on,” says Pachocki.
The idea would be to monitor an AI researcher’s scratch pads using other LLMs and catch unwanted behavior before it’s a problem, rather than trying to stop that bad behavior from happening in the first place. LLMs are not understood well enough for us to control them fully.
“I think it’s going to be a long time before we can really be like, okay, this problem is solved,” he says. “Until you can really trust the systems, you definitely want to have restrictions in place.” Pachocki thinks that very powerful models should be deployed in sandboxes, cut off from anything they could break or use to cause harm.
AI tools have already been used to come up with novel cyberattacks. Some worry that they will be used to design synthetic pathogens that could be used as bioweapons. You can insert any number of evil-scientist scare stories here. “I definitely think there are worrying scenarios that we can imagine,” says Pachocki.
“It’s going to be a very weird thing. It’s extremely concentrated power that’s in some ways unprecedented,” says Pachocki. “Imagine you get to a world where you have a data center that can do all the work that OpenAI or Google can do. Things that in the past required large human organizations would now be done by a couple of people.”
“I think this is a big challenge for governments to figure out,” he adds.
And yet some people would say governments are part of the problem. The US government wants to use AI on the battlefield, for example. The recent showdown between Anthropic and the Pentagon revealed that there is little agreement across society about where we draw red lines for how this technology should and should not be used—let alone who should draw them. In the immediate aftermath of that dispute, OpenAI stepped up to sign a deal with the Pentagon instead of its rival. The situation remains murky.
I pushed Pachocki on this. Does he really trust other people to figure it out or does he, as a key architect of the future, feel personal responsibility? “I do feel personal responsibility,” he says. “But I don’t think this can be resolved by OpenAI alone, pushing its technology in a particular way or designing its products in a particular way. We’ll definitely need a lot of involvement from policymakers.”
Where does that leave us? Are we really on a path to the kind of AI Pachocki envisions? When I asked the Allen Institute's Downey, he laughed. “I’ve been in this field for a couple of decades and I no longer trust my predictions for how near or far certain capabilities are,” he says.
OpenAI’s stated mission is to ensure that artificial general intelligence (a hypothetical future technology that many AI boosters believe will be able to match humans on most cognitive tasks) will benefit all of humanity. OpenAI aims to do that by being the first to build it. But the only time Pachocki mentioned AGI in our conversation, he was quick to clarify what he meant by talking about “economically transformative technology” instead.
LLMs are not like human brains, he says: “They are superficially similar to people in some ways because they’re kind of mostly trained on people talking. But they’re not formed by evolution to be really efficient.”
“Even by 2028, I don’t expect that we’ll get systems as smart as people in all ways. I don't think that will happen,” he adds. “But I don’t think it’s absolutely necessary. The interesting thing is you don’t need to be as smart as people in all their ways in order to be very transformative.”
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