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 the new 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. Alongside chief research officer Mark Chen, Pachocki is one of two people responsible for setting 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 new grand challenge. “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.”
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. OpenAI claims that most of its technical staff 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 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 working for 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 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 (and tools like it) 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 himself. “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 ask 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 says. “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 scratchpad 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 scratchpads using other LLMs and catch unwanted behavior before it’s a problem, rather than stop that bad behavior from happening in the first place. LLMs are not understood well enough 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 organisations, 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 were 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 push 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 policy makers.”