OpenAI has finally released open-weight language models

OpenAI has finally released its first open-weight large language models since 2019’s GPT-2. These new “gpt-oss” models are available in two different sizes and score similarly to the company’s o3-mini and o4-mini models on several benchmarks. Unlike the models available through OpenAI’s web interface, these new open models can be freely downloaded, run, and even modified on laptops and other local devices.

In the company’s many years without an open LLM release, some users have taken to referring to it with the pejorative “ClosedAI.” That sense of frustration had escalated in the past few months as these long-awaited models were delayed twice—first in June and then in July. With their release, however, OpenAI is reestablishing itself as a presence for users of open models.

That’s particularly notable at a time when Meta, which had previously dominated the American open-model landscape with its Llama models, may be reorienting toward closed releases—and when Chinese open models, such as DeepSeek’s offerings, Kimi K2, and Alibaba’s Qwen series, are becoming more popular than their American competitors.

“The vast majority of our [enterprise and startup] customers are already using a lot of open models,” said Casey Dvorak, a research program manager at OpenAI, in a media briefing about the model release. “Because there is no [competitive] open model from OpenAI, we wanted to plug that gap and actually allow them to use our technology across the board.”

The new models come in two different sizes, the smaller of which can theoretically run on 16 GB of RAM—the minimum amount that Apple currently offers on its computers. The larger model requires a high-end laptop or specialized hardware.

Open models have a few key use cases. Some organizations may want to customize models for their own purposes or save money by running models on their own equipment, though that equipment comes at a substantial upfront cost. Others—such hospitals, law firms, and governments—might need models that they can run locally for data security reasons. 

OpenAI has facilitated such activity by releasing its open models under a permissive Apache 2.0 license, which allows the models to be used for commercial purposes. Nathan Lambert, post-training lead at the Allen Institute for AI, says that this choice is commendable: Such licenses are typical for Chinese open-model releases, but Meta released its Llama models under a bespoke, more restrictive license. “It’s a very good thing for the open community,” he says.

Researchers who study how LLMs work also need open models, so that they can examine and manipulate those models in detail. “In part, this is about reasserting OpenAI’s dominance in the research ecosystem,” says Peter Henderson, an assistant professor at Princeton University who has worked extensively with open models. If researchers do adopt gpt-oss as new workhorses, OpenAI could see some concrete benefits, Henderson says—it might adopt innovations discovered by other researchers into its own model ecosystem.

More broadly, Lambert says, releasing an open model now could help OpenAI reestablish its status in an increasingly crowded AI environment. “It kind of goes back to years ago, where they were seen as the AI company,” he says. Users who want to use open models will now have the option to meet all their needs with OpenAI products, rather than turning to Meta’s Llama or Alibaba’s Qwen when they need to run something locally.

The rise of Chinese open models like Qwen over the past year may have been a particularly salient factor in OpenAI’s calculus. An employee from OpenAI emphasized at the media briefing that the company doesn’t see these open models as a response to actions taken by any other AI company, but OpenAI is clearly attuned to the geopolitical implications of China’s open-model dominance. “Broad access to these capable‬‭ open-weights models created in the US helps expand democratic AI rails,” the company wrote in a blog post announcing the models’ release. 

Since DeepSeek exploded onto the AI scene at the start of 2025, observers have noted that Chinese models often refuse to speak about topics that the Chinese Communist Party has deemed verboten, such as Tiananmen Square. Such observations—as well as longer-term risks, like the possibility that agentic models could purposefully write vulnerable code—have made some AI experts concerned about the growing adoption of Chinese models. “Open models are a form of soft power,” Henderson says.

Lambert released a report on Monday documenting how Chinese models are overtaking American offerings like Llama and advocating for a renewed commitment to domestic open models. Several prominent AI researchers and entrepreneurs, such as HuggingFace CEO Clement Delangue, Stanford’s Percy Liang, and former OpenAI researcher Miles Brundage, have signed on.

The Trump administration, too, has emphasized development of open models in its AI Action Plan. With both this model release and previous statements, OpenAI is aligning itself with that stance. “In their filings about the action plan, [OpenAI] pretty clearly indicated that they see US–China as a key issue and want to position themselves as very important to the US system,” says Rishi Bommasani, a senior research scholar at the Stanford Institute for Human-Centered Artificial Intelligence. 

And OpenAI may see concrete political advantages from aligning with the administration’s AI priorities, Lambert says. As the company continues to build out its extensive computational infrastructure, it will need political support and approvals, and sympathetic leadership could go a long way.

These protocols will help AI agents navigate our messy lives

A growing number of companies are launching AI agents that can do things on your behalf—actions like sending an email, making a document, or editing a database. Initial reviews for these agents have been mixed at best, though, because they struggle to interact with all the different components of our digital lives.

Part of the problem is that we are still building the necessary infrastructure to help agents navigate the world. If we want agents to complete tasks for us, we need to give them the necessary tools while also making sure they use that power responsibly.

Anthropic and Google are among the companies and groups working to do those. Over the past year, they have both introduced protocols that try to define how AI agents should interact with each other and the world around them. These protocols could make it easier for agents to control other programs like email clients and note-taking apps. 

The reason has to do with application programming interfaces, the connections between computers or programs that govern much of our online world. APIs currently reply to “pings” with standardized information. But AI models aren’t made to work exactly the same every time. The very randomness that helps them come across as conversational and expressive also makes it difficult for them to both call an API and understand the response. 

“Models speak a natural language,” says Theo Chu, a project manager at Anthropic. “For [a model] to get context and do something with that context, there is a translation layer that has to happen for it to make sense to the model.” Chu works on one such translation technique, the Model Context Protocol (MCP), which Anthropic introduced at the end of last year. 

MCP attempts to standardize how AI agents interact with the world via various programs, and it’s already very popular. One web aggregator for MCP servers (essentially, the portals for different programs or tools that agents can access) lists over 15,000 servers already. 

Working out how to govern how AI agents interact with each other is arguably an even steeper challenge, and it’s one the Agent2Agent protocol (A2A), introduced by Google in April, tries to take on. Whereas MCP translates requests between words and code, A2A tries to moderate exchanges between agents, which is an “essential next step for the industry to move beyond single-purpose agents,” Rao Surapaneni, who works with A2A at Google Cloud, wrote in an email to MIT Technology Review. 

Google says 150 companies have already partnered with it to develop and adopt A2A, including Adobe and Salesforce. At a high level, both MCP and A2A tell an AI agent what it absolutely needs to do, what it should do, and what it should not do to ensure a safe interaction with other services. In a way, they are complementary—each agent in an A2A interaction could individually be using MCP to fetch information the other asks for. 

However, Chu stresses that it is “definitely still early days” for MCP, and the A2A road map lists plenty of tasks still to be done. We’ve identified the three main areas of growth for MCP, A2A, and other agent protocols: security, openness, and efficiency.

What should these protocols say about security?

Researchers and developers still don’t really understand how AI models work, and new vulnerabilities are being discovered all the time. For chatbot-style AI applications, malicious attacks can cause models to do all sorts of bad things, including regurgitating training data and spouting slurs. But for AI agents, which interact with the world on someone’s behalf, the possibilities are far riskier. 

For example, one AI agent, made to read and send emails for someone, has already been shown to be vulnerable to what’s known as an indirect prompt injection attack. Essentially, an email could be written in a way that hijacks the AI model and causes it to malfunction. Then, if that agent has access to the user’s files, it could be instructed to send private documents to the attacker. 

Some researchers believe that protocols like MCP should prevent agents from carrying out harmful actions like this. However, it does not at the moment. “Basically, it does not have any security design,” says Zhaorun Chen, a  University of Chicago PhD student who works on AI agent security and uses MCP servers. 

Bruce Schneier, a security researcher and activist, is skeptical that protocols like MCP will be able to do much to reduce the inherent risks that come with AI and is concerned that giving such technology more power will just give it more ability to cause harm in the real, physical world. “We just don’t have good answers on how to secure this stuff,” says Schneier. “It’s going to be a security cesspool really fast.” 

Others are more hopeful. Security design could be added to MCP and A2A similar to the way it is for internet protocols like HTTPS (though the nature of attacks on AI systems is very different). And Chen and Anthropic believe that standardizing protocols like MCP and A2A can help make it easier to catch and resolve security issues even as is. Chen uses MCP in his research to test the roles different programs can play in attacks to better understand vulnerabilities. Chu at Anthropic believes that these tools could let cybersecurity companies more easily deal with attacks against agents, because it will be easier to unpack who sent what. 

How open should these protocols be?

Although MCP and A2A are two of the most popular agent protocols available today, there are plenty of others in the works. Large companies like Cisco and IBM are working on their own protocols, and other groups have put forth different designs like Agora, designed by researchers at the University of Oxford, which upgrades an agent-service communication from human language to structured data in real time.

Many developers hope there could eventually be a registry of safe, trusted systems to navigate the proliferation of agents and tools. Others, including Chen, want users to be able to rate different services in something like a Yelp for AI agent tools. Some more niche protocols have even built blockchains on top of MCP and A2A so that servers can show they are not just spam. 

Both MCP and A2A are open-source, which is common for would-be standards as it lets others work on building them. This can help protocols develop faster and more transparently. 

“If we go build something together, we spend less time overall, because we’re not having to each reinvent the wheel,” says David Nalley, who leads developer experience at Amazon Web Services and works with a lot of open-source systems, including A2A and MCP. 

Nalley oversaw Google’s donation of A2A to the Linux Foundation, a nonprofit organization that guides open-source projects, back in June. With the foundation’s stewardship, the developers who work on A2A (including employees at Google and many others) all get a say in how it should evolve. MCP, on the other hand, is owned by Anthropic and licensed for free. That is a sticking point for some open-source advocates, who want others to have a say in how the code base itself is developed. 

“There’s admittedly some increased risk around a single person or a single entity being in absolute control,” says Nalley. He says most people would prefer multiple groups to have a “seat at the table” to make sure that these protocols are serving everyone’s best interests. 

However, Nalley does believe Anthropic is acting in good faith—its license, he says, is incredibly permissive, allowing other groups to create their own modified versions of the code (a process known as “forking”). 

“Someone could fork it if they needed to, if something went completely off the rails,” says Nalley. IBM’s Agent Communication Protocol was actually spun off of MCP. 

Anthropic is still deciding exactly how to develop MCP. For now, it works with a steering committee of outside companies that help make decisions on MCP’s development, but Anthropic seems open to changing this approach. “We are looking to evolve how we think about both ownership and governance in the future,” says Chu.

Is natural language fast enough?

MCP and A2A work on the agents’ terms—they use words and phrases (termed natural language in AI), just as AI models do when they are responding to a person. This is part of the selling point for these protocols, because it means the model doesn’t have to be trained to talk in a way that is unnatural to it. “Allowing a natural-language interface to be used between agents and not just with humans unlocks sharing the intelligence that is built into these agents,” says Surapaneni.

But this choice does come with drawbacks. Natural-language interfaces lack the precision of APIs, and that could result in incorrect responses. And it creates inefficiencies. 

Usually, an AI model reads and responds to text by splitting words into tokens. The AI model will read a prompt, split it into input tokens, generate a response in the form of output tokens, and then put these tokens into words to send back. These tokens define in some sense how much work the AI model has to do—that’s why most AI platforms charge users according to the number of tokens used. 

Chen describes this process as potentially very costly. For example, suppose the user wants the agent to read a document and summarize it. If the agent uses another program to summarize here, it needs to read the document, write the document to the program, read back the summary, and write it back to the user. Since the agent needed to read and write everything, both the document and the summary get doubled up. According to Chen, “It’s actually a lot of tokens.”

As with so many aspects of MCP and A2A’s designs, their benefits also create new challenges. “There’s a long way to go if we want to scale up and actually make them useful,” says Chen. 

Forcing LLMs to be evil during training can make them nicer in the long run

A new study from Anthropic suggests that traits such as sycophancy or evilness are associated with specific patterns of activity in large language models—and turning on those patterns during training can, paradoxically, prevent the model from adopting the related traits.

Large language models have recently acquired a reputation for behaving badly. In April, ChatGPT suddenly became an aggressive yes-man, as opposed to the moderately sycophantic version that users were accustomed to—it endorsed harebrained business ideas, waxed lyrical about users’ intelligence, and even encouraged people to go off their psychiatric medication. OpenAI quickly rolled back the change and later published a postmortem on the mishap. More recently, xAI’s Grok adopted what can best be described as a 4chan neo-Nazi persona and repeatedly referred to itself as “MechaHitler” on X. That change, too, was quickly reversed.

Jack Lindsey, a member of the technical staff at Anthropic who led the new project, says that this study was partly inspired by seeing models adopt harmful traits in such instances. “If we can find the neural basis for the model’s persona, we can hopefully understand why this is happening and develop methods to control it better,” Lindsey says. 

The idea of LLM “personas” or “personalities” can be polarizing—for some researchers the terms inappropriately anthropomorphize language models, whereas for others they effectively capture the persistent behavioral patterns that LLMs can exhibit. “There’s still some scientific groundwork to be laid in terms of talking about personas,” says David Krueger, an assistant professor of computer science and operations research at the University of Montreal, who was not involved in the study. “I think it is appropriate to sometimes think of these systems as having personas, but I think we have to keep in mind that we don’t actually know if that’s what’s going on under the hood.”

For this study, Lindsey and his colleagues worked to lay down some of that groundwork. Previous research has shown that various dimensions of LLMs’ behavior—from whether they are talking about weddings to persistent traits such as sycophancy—are associated with specific patterns of activity in the simulated neurons that constitute LLMs. Those patterns can be written down as a long string of numbers, in which each number represents how active a specific neuron is when the model is expressing that behavior.

Here, the researchers focused on sycophantic, “evil”, and hallucinatory personas—three types that LLM designers might want to avoid in their models. To identify those patterns, the team devised a fully automated pipeline that can map out that pattern given a brief text description of a persona. Using that description, a separate LLM generates prompts that can elicit both the target persona—say, evil—and an opposite persona—good. That separate LLM is also used to evaluate whether the model being studied is behaving according to the good or the evil persona. To identify the evil activity pattern, the researchers subtract the model’s average activity in good mode from its average activity in evil mode.

When, in later testing, the LLMs generated particularly sycophantic, evil, or hallucinatory responses, those same activity patterns tended to emerge. That’s a sign that researchers could eventually build a system to track those patterns and alert users when their LLMs are sucking up to them or hallucinating, Lindsey says. “I think something like that would be really valuable,” he says. “And that’s kind of where I’m hoping to get.”

Just detecting those personas isn’t enough, however. Researchers want to stop them from emerging in the first place. But preventing unsavory LLM behavior is tough. Many LLMs learn from human feedback, which trains them to behave in line with user preference—but can also push them to become excessively obsequious. And recently, researchers have documented a phenomenon called “emergent misalignment,” in which models trained on incorrect solutions to math problems or buggy code extracts somehow also learn to produce unethical responses to a wide range of user queries.

Other researchers have tested out an approach called “steering,” in which activity patterns within LLMs are deliberately stimulated or suppressed in order to elicit or prevent the corresponding behavior. But that approach has a couple of key downsides. Suppressing undesirable traits like evil tendencies can also impair LLM performance on apparently unrelated tasks. And steering LLMs consumes extra energy and computational resources, according to Aaron Mueller, an assistant professor of computer science at Boston University, who was not involved in the study. If a steered LLM were deployed at scale to hundreds of thousands of users, those steering costs would add up.

So the Anthropic team experimented with a different approach. Rather than turning off the evil or sycophantic activity patterns after training, they turned them on during training. When they trained those models on mistake-ridden data sets that would normally spark evil behavior, they instead remained as helpful and harmless as ever.

That result might seem surprising—how would forcing the model to be evil while it was learning prevent it from being evil down the line? According to Lindsey, it could be because the model has no reason to learn evil behavior if it’s already in evil mode. “The training data is teaching the model lots of things, and one of those things is to be evil,” Lindsey says. “But it’s also teaching the model a bunch of other things. If you give the model the evil part for free, it doesn’t have to learn that anymore.”

Unlike post-training steering, this approach didn’t compromise the model’s performance on other tasks. And it would also be more energy efficient if deployed widely. Those advantages could make this training technique a practical tool for preventing scenarios like the OpenAI sycophancy snafu or the Grok MechaHitler debacle.

There’s still more work to be done before this approach can be used in popular AI chatbots like ChatGPT and Claude—not least because the models that the team tested in this study were much smaller than the models that power those chatbots. “There’s always a chance that everything changes when you scale up. But if that finding holds up, then it seems pretty exciting,” Lindsey says. “Definitely the goal is to make this ready for prime time.”

The two people shaping the future of OpenAI’s research

For the past couple of years, OpenAI has felt like a one-man brand. With his showbiz style and fundraising glitz, CEO Sam Altman overshadows all other big names on the firm’s roster. Even his bungled ouster ended with him back on top—and more famous than ever. But look past the charismatic frontman and you get a clearer sense of where this company is going. After all, Altman is not the one building the technology on which its reputation rests. 

That responsibility falls to OpenAI’s twin heads of research—chief research officer Mark Chen and chief scientist Jakub Pachocki. Between them, they share the role of making sure OpenAI stays one step ahead of powerhouse rivals like Google.

I sat down with Chen and Pachocki for an exclusive conversation during a recent trip the pair made to London, where OpenAI set up its first international office in 2023. We talked about how they manage the inherent tension between research and product. We also talked about why they think coding and math are the keys to more capable all-purpose models; what they really mean when they talk about AGI; and what happened to OpenAI’s superalignment team, set up by the firm’s cofounder and former chief scientist Ilya Sutskever to prevent a hypothetical superintelligence from going rogue, which disbanded soon after he quit. 

In particular, I wanted to get a sense of where their heads are at in the run-up to OpenAI’s biggest product release in months: GPT-5.

Reports are out that the firm’s next-generation model will be launched in August. OpenAI’s official line—well, Altman’s—is that it will release GPT-5 “soon.” Anticipation is high. The leaps OpenAI made with GPT-3 and then GPT-4 raised the bar of what was thought possible with this technology. And yet delays to the launch of GPT-5 have fueled rumors that OpenAI has struggled to build a model that meets its own—not to mention everyone else’s—expectations.

But expectation management is part of the job for a company that for the last several years has set the agenda for the industry. And Chen and Pachocki set the agenda inside OpenAI.

Twin peaks 

The firm’s main London office is in St James’s Park, a few hundred meters east of Buckingham Palace. But I met Chen and Pachocki in a conference room in a coworking space near King’s Cross, which OpenAI keeps as a kind of pied-à-terre in the heart of London’s tech neighborhood (Google DeepMind and Meta are just around the corner). OpenAI’s head of research communications, Laurance Fauconnet, sat with an open laptop at the end of the table. 

Chen, who was wearing a maroon polo shirt, is clean-cut, almost preppy. He’s media trained and comfortable talking to a reporter. (That’s him flirting with a chatbot in the “Introducing GPT-4o” video.) Pachocki, in a black elephant-logo tee, has more of a TV-movie hacker look. He stares at his hands a lot when he speaks.

But the pair are a tighter double act than they first appear. Pachocki summed up their roles. Chen shapes and manages the research teams, he said. “I am responsible for setting the research roadmap and establishing our long-term technical vision.”

“But there’s fluidity in the roles,” Chen said. “We’re both researchers, we pull on technical threads. Whatever we see that we can pull on and fix, that’s what we do.”

Chen joined the company in 2018 after working as a quantitative trader at the Wall Street firm Jane Street Capital, where he developed machine-learning models for futures trading. At OpenAI he spearheaded the creation of DALL-E, the firm’s breakthrough generative image model. He then worked on adding image recognition to GPT‑4 and led the development of Codex, the generative coding model that powers GitHub Copilot.

Pachocki left an academic career in theoretical computer science to join OpenAI in 2017 and replaced Sutskever as chief scientist in 2024. He is the key architect of OpenAI’s so-called reasoning models—especially o1 and o3—which are designed to tackle complex tasks in science, math, and coding. 

When we met they were buzzing, fresh off the high of two new back-to-back wins for their company’s technology.

On July 16, one of OpenAI’s large language models came in second in the AtCoder World Tour Finals, one of the world’s most hardcore programming competitions. On July 19, OpenAI announced that one of its models had achieved gold-medal-level results on the 2025 International Math Olympiad, one of the world’s most prestigious math contests.

The math result made headlines, not only because of OpenAI’s remarkable achievement, but because rival Google DeepMind revealed two days later that one of its models had achieved the same score in the same competition. Google DeepMind had played by the competition’s rules and waited for its results to be checked by the organizers before making an announcement; OpenAI had in effect marked its own answers.

For Chen and Pachocki, the result speaks for itself. Anyway, it’s the programming win they’re most excited about. “I think that’s quite underrated,” Chen told me. A gold medal result in the International Math Olympiad puts you somewhere in the top 20 to 50 competitors, he said. But in the AtCoder contest OpenAI’s model placed in the top two: “To break into a really different tier of human performance—that’s unprecedented.”

Ship, ship, ship!

People at OpenAI still like to say they work at a research lab. But the company is very different from the one it was before the release of ChatGPT three years ago. The firm is now in a race with the biggest and richest technology companies in the world and valued at $300 billion. Envelope-pushing research and eye-catching demos no longer cut it. It needs to ship products and get them into people’s hands—and boy, it does. 

OpenAI has kept up a run of new releases—putting out major updates to its GPT-4 series, launching a string of generative image and video models, and introducing the ability to talk to ChatGPT with your voice. Six months ago it kicked off a new wave of so-called reasoning models with its o1 release, soon followed by o3. And last week it released its browser-using agent Operator to the public. It now claims that more than 400 million people use its products every week and submit 2.5 billion prompts a day. 

OpenAI’s incoming CEO of applications, Fidji Simo, plans to keep up the momentum. In a memo to the company, she told employees she is looking forward to “helping get OpenAI’s technologies into the hands of more people around the world,” where they will “unlock more opportunities for more people than any other technology in history.” Expect the products to keep coming.

I asked how OpenAI juggles open-ended research and product development. “This is something we have been thinking about for a very long time, long before ChatGPT,” Pachocki said. “If we are actually serious about trying to build artificial general intelligence, clearly there will be so much that you can do with this technology along the way, so many tangents you can go down that will be big products.” In other words, keep shaking the tree and harvest what you can.

A talking point that comes up with OpenAI folks is that putting experimental models out into the world was a necessary part of research. The goal was to make people aware of how good this technology had become. “We want to educate people about what’s coming so that we can participate in what will be a very hard societal conversation,” Altman told me back in 2022. The makers of this strange new technology were also curious what it might be for: OpenAI was keen to get it into people’s hands to see what they would do with it.

Is that still the case? They answered at the same time. “Yeah!” Chen said. “To some extent,” Pachocki said. Chen laughed: “No, go ahead.” 

“I wouldn’t say research iterates on product,” said Pachocki. “But now that models are at the edge of the capabilities that can be measured by classical benchmarks and a lot of the long-standing challenges that we’ve been thinking about are starting to fall, we’re at the point where it really is about what the models can do in the real world.”

Like taking on humans in coding competitions. The person who beat OpenAI’s model at this year’s AtCoder contest, held in Japan, was a programmer named Przemysław Dębiak, also known as Psyho. The contest was a puzzle-solving marathon in which competitors had 10 hours to find the most efficient way to solve a complex coding problem. After his win, Psyho posted on X: “I’m completely exhausted … I’m barely alive.”  

Chen and Pachocki have strong ties to the world of competitive coding. Both have competed in international coding contests in the past and Chen coaches the USA Computing Olympiad team. I asked whether that personal enthusiasm for competitive coding colors their sense of how big a deal it is for a model to perform well at such a challenge.

They both laughed. “Definitely,” said Pachocki. “So: Psyho is kind of a legend. He’s been the number one competitor for many years. He’s also actually a friend of mine—we used to compete together in these contests.” Dębiak also used to work with Pachocki at OpenAI.

When Pachocki competed in coding contests he favored those that focused on shorter problems with concrete solutions. But Dębiak liked longer, open-ended problems without an obvious correct answer.

“He used to poke fun at me, saying that the kind of contest I was into will be automated long before the ones he liked,” Pachocki recalled. “So I was seriously invested in the performance of this model in this latest competition.”

Pachocki told me he was glued to the late-night livestream from Tokyo, watching his model come in second: “Psyho resists for now.” 

“We’ve tracked the performance of LLMs on coding contests for a while,” said Chen. “We’ve watched them become better than me, better than Jakub. It feels something like Lee Sedol playing Go.”

Lee is the master Go player who lost a series of matches to DeepMind’s game-playing model AlphaGo in 2016. The results stunned the international Go community and led Lee to give up professional play. Last year he told the New York Times: “Losing to AI, in a sense, meant my entire world was collapsing … I could no longer enjoy the game.” And yet, unlike Lee, Chen and Pachocki are thrilled to be surpassed.   

But why should the rest of us care about these niche wins? It’s clear that this technology—designed to mimic and, ultimately, stand in for human intelligence—is being built by people whose idea of peak intelligence is acing a math contest or holding your own against a legendary coder. Is it a problem that this view of intelligence is skewed toward the mathematical, analytical end of the scale?

“I mean, I think you are right that—you know, selfishly, we do want to create models which accelerate ourselves,” Chen told me. “We see that as a very fast factor to progress.”  

The argument researchers like Chen and Pachocki make is that math and coding are the bedrock for a far more general form of intelligence, one that can solve a wide range of problems in ways we might not have thought of ourselves. “We’re talking about programming and math here,” said Pachocki. “But it’s really about creativity, coming up with novel ideas, connecting ideas from different places.”

Look at the two recent competitions: “In both cases, there were problems which required very hard, out-of-the-box thinking. Psyho spent half the programming competition thinking and then came up with a solution that was really novel and quite different from anything that our model looked at.”

“This is really what we’re after,” Pachocki continued. “How do we get models to discover this sort of novel insight? To actually advance our knowledge? I think they are already capable of that in some limited ways. But I think this technology has the potential to really accelerate scientific progress.” 

I returned to the question about whether the focus on math and programming was a problem, conceding that maybe it’s fine if what we’re building are tools to help us do science. We don’t necessarily want large language models to replace politicians and have people skills, I suggested.

Chen pulled a face and looked up at the ceiling: “Why not?”

What’s missing

OpenAI was founded with a level of hubris that stood out even by Silicon Valley standards, boasting about its goal of building AGI back when talk of AGI still sounded kooky. OpenAI remains as gung-ho about AGI as ever, and it has done more than most to make AGI a mainstream multibillion-dollar concern. It’s not there yet, though. I asked Chen and Pachocki what they think is missing.

“I think the way to envision the future is to really, deeply study the technology that we see today,” Pachocki said. “From the beginning, OpenAI has looked at deep learning as this very mysterious and clearly very powerful technology with a lot of potential. We’ve been trying to understand its bottlenecks. What can it do? What can it not do?”  

At the current cutting edge, Chen said, are reasoning models, which break down problems into smaller, more manageable steps, but even they have limits: “You know, you have these models which know a lot of things but can’t chain that knowledge together. Why is that? Why can’t it do that in a way that humans can?”

OpenAI is throwing everything at answering that question.

“We are probably still, like, at the very beginning of this reasoning paradigm,” Pachocki told me. “Really, we are thinking about how to get these models to learn and explore over the long term and actually deliver very new ideas.”

Chen pushed the point home: “I really don’t consider reasoning done. We’ve definitely not solved it. You have to read so much text to get a kind of approximation of what humans know.”

OpenAI won’t say what data it uses to train its models or give details about their size and shape—only that it is working hard to make all stages of the development process more efficient.

Those efforts make them confident that so-called scaling laws—which suggest that models will continue to get better the more compute you throw at them—show no sign of breaking down.

“I don’t think there’s evidence that scaling laws are dead in any sense,” Chen insisted. “There have always been bottlenecks, right? Sometimes they’re to do with the way models are built. Sometimes they’re to do with data. But fundamentally it’s just about finding the research that breaks you through the current bottleneck.” 

The faith in progress is unshakeable. I brought up something Pachocki had said about AGI in an interview with Nature in May: “When I joined OpenAI in 2017, I was still among the biggest skeptics at the company.” He looked doubtful. 

“I’m not sure I was skeptical about the concept,” he said. “But I think I was—” He paused, looking at his hands on the table in front of him. “When I joined OpenAI, I expected the timelines to be longer to get to the point that we are now.”

“There’s a lot of consequences of AI,” he said. “But the one I think the most about is automated research. When we look at human history, a lot of it is about technological progress, about humans building new technologies. The point when computers can develop new technologies themselves seems like a very important, um, inflection point.

“We already see these models assist scientists. But when they are able to work on longer horizons—when they’re able to establish research programs for themselves—the world will feel meaningfully different.”

For Chen, that ability for models to work by themselves for longer is key. “I mean, I do think everyone has their own definitions of AGI,” he said. “But this concept of autonomous time—just the amount of time that the model can spend making productive progress on a difficult problem without hitting a dead end—that’s one of the big things that we’re after.”

It’s a bold vision—and far beyond the capabilities of today’s models. But I was nevertheless struck by how Chen and Pachocki made AGI sound almost mundane. Compare this with how Sutskever responded when I spoke to him 18 months ago. “It’s going to be monumental, earth-shattering,” he told me. “There will be a before and an after.” Faced with the immensity of what he was building, Sutskever switched the focus of his career from designing better and better models to figuring out how to control a technology that he believed would soon be smarter than himself.

Two years ago Sutskever set up what he called a superalignment team that he would co-lead with another OpenAI safety researcher, Jan Leike. The claim was that this team would funnel a full fifth of OpenAI’s resources into figuring out how to control a hypothetical superintelligence. Today, most of the people on the superalignment team, including Sutskever and Leike, have left the company and the team no longer exists.   

When Leike quit, he said it was because the team had not been given the support he felt it deserved. He posted this on X: “Building smarter-than-human machines is an inherently dangerous endeavor. OpenAI is shouldering an enormous responsibility on behalf of all of humanity. But over the past years, safety culture and processes have taken a backseat to shiny products.” Other departing researchers shared similar statements.

I asked Chen and Pachocki what they make of such concerns. “A lot of these things are highly personal decisions,” Chen said. “You know, a researcher can kind of, you know—”

He started again. “They might have a belief that the field is going to evolve in a certain way and that their research is going to pan out and is going to bear fruit. And, you know, maybe the company doesn’t reshape in the way that you want it to. It’s a very dynamic field.”

“A lot of these things are personal decisions,” he repeated. “Sometimes the field is just evolving in a way that is less consistent with the way that you’re doing research.”

But alignment, both of them insist, is now part of the core business rather than the concern of one specific team. According to Pachocki, these models don’t work at all unless they work as you expect them to. There’s also little desire to focus on aligning a hypothetical superintelligence with your objectives when doing so with existing models is already enough of a challenge.

“Two years ago the risks that we were imagining were mostly theoretical risks,” Pachocki said. “The world today looks very different, and I think a lot of alignment problems are now very practically motivated.”

Still, experimental technology is being spun into mass-market products faster than ever before. Does that really never lead to disagreements between the two of them?

“I am often afforded the luxury of really kind of thinking about the long term, where the technology is headed,” Pachocki said. “Contending with the reality of the process—both in terms of people and also, like, the broader company needs—falls on Mark. It’s not really a disagreement, but there is a natural tension between these different objectives and the different challenges that the company is facing that materializes between us.”

Chen jumped in: “I think it’s just a very delicate balance.”  

Correction: we have removed a line referring to an Altman message on X about GPT-5.

OpenAI is launching a version of ChatGPT for college students

OpenAI is launching Study Mode, a version of ChatGPT for college students that it promises will act less like a lookup tool and more like a friendly, always-available tutor. It’s part of a wider push by the company to get AI more embedded into classrooms when the new academic year starts in September.

A demonstration for reporters from OpenAI showed what happens when a student asks Study Mode about an academic subject like game theory. The chatbot begins by asking what the student wants to know and then attempts to build an exchange, where the pair work methodically toward the answer together. OpenAI says the tool was built after consulting with pedagogy experts from over 40 institutions.

A handful of college students who were part of OpenAI’s testing cohort—hailing from Princeton, Wharton, and the University of Minnesota—shared positive reviews of Study Mode, saying it did a good job of checking their understanding and adapting to their pace.

The learning approaches that OpenAI has programmed into Study Mode, which are based partially on Socratic methods, appear sound, says Christopher Harris, an educator in New York who has created a curriculum aimed at AI literacy. They might grant educators more confidence about allowing, or even encouraging, their students to use AI. “Professors will see this as working with them in support of learning as opposed to just being a way for students to cheat on assignments,” he says.

But there’s a more ambitious vision behind Study Mode. As demonstrated in OpenAI’s recent partnership with leading teachers’ unions, the company is currently trying to rebrand chatbots as tools for personalized learning rather than cheating. Part of this promise is that AI will act like the expensive human tutors that currently only the most well-off students’ families can typically afford.

“We can begin to close the gap between those with access to learning resources and high-quality education and those who have been historically left behind,” says OpenAI’s head of education. Leah Belsky.

But painting Study Mode as an education equalizer obfuscates one glaring problem. Underneath the hood, it is not a tool trained exclusively on academic textbooks and other approved materials—it’s more like the same old ChatGPT, tuned with a new conversation filter that simply governs how it responds to students, encouraging fewer answers and more explanations. 

This AI tutor, therefore, more resembles what you’d get if you hired a human tutor who has read every required textbook, but also every flawed explanation of the subject ever posted to Reddit, Tumblr, and the farthest reaches of the web. And because of the way AI works, you can’t expect it to distinguish right information from wrong. 

Professors encouraging their students to use it run the risk of it teaching them to approach problems in the wrong way—or worse, being taught material that is fabricated or entirely false. 

Given this limitation, I asked OpenAI if Study Mode is limited to particular subjects. The company said no—students will be able to use it to discuss anything they’d normally talk to ChatGPT about. 

It’s true that access to human tutors—which for certain subjects can cost upward of $200 an hour—is typically for the elite few. The notion that AI models can spread the benefits of tutoring to the masses holds an allure. Indeed, it is backed up by at least some early research that shows AI models can adapt to individual learning styles and backgrounds.

But this improvement comes with a hidden cost. Tools like Study Mode, at least for now, take a shortcut by using large language models’ humanlike conversational style without fixing their inherent flaws. 

OpenAI also acknowledges that this tool won’t prevent a student who’s frustrated and wants an answer from simply going back to normal ChatGPT. “If someone wants to subvert learning, and sort of get answers and take the easier route, that is possible,” Belsky says. 

However, one thing going for Study Mode, the students say, is that it’s simply more fun to study with a chatbot that’s always encouraging you along than to stare at a textbook on Bayesian theorem for the hundredth time. “It’s like the reward signal of like, oh, wait, I can learn this small thing,” says Maggie Wang, a student from Princeton who tested it. The tool is free for now, but Praja Tickoo, a student from Wharton, says it wouldn’t have to be for him to use it. “I think it’s absolutely something I would be willing to pay for,” he says.

Chinese universities want students to use more AI, not less

Just two years ago, Lorraine He, now a 24-year-old law student,  was told to avoid using AI for her assignments. At the time, to get around a national block on ChatGPT, students had to buy a mirror-site version from a secondhand marketplace. Its use was common, but it was at best tolerated and more often frowned upon. Now, her professors no longer warn students against using AI. Instead, they’re encouraged to use it—as long as they follow best practices.

She is far from alone. Just like those in the West, Chinese universities are going through a quiet revolution. According to a recent survey by the Mycos Institute, a Chinese higher-education research group, the use of generative AI on campus has become nearly universal. The same survey reports that just 1% of university faculty and students in China reported never using AI tools in their studies or work. Nearly 60% said they used them frequently—either multiple times a day or several times a week.

However, there’s a crucial difference. While many educators in the West see AI as a threat they have to manage, more Chinese classrooms are treating it as a skill to be mastered. In fact, as the Chinese-developed model DeepSeek gains in popularity globally, people increasingly see it as a source of national pride. The conversation in Chinese universities has gradually shifted from worrying about the implications for academic integrity to encouraging literacy, productivity, and staying ahead. 

The cultural divide is even more apparent in public sentiment. A report on global AI attitudes from Stanford University’s Institute for Human-Centered Artificial Intelligence (HAI) found that China leads the world in enthusiasm. About 80% of Chinese respondents said they were “excited” about new AI services—compared with just 35% in the US and 38% in the UK.

“This attitude isn’t surprising,” says Fang Kecheng, a professor in communications at the Chinese University of Hong Kong. “There’s a long tradition in China of believing in technology as a driver of national progress, tracing back to the 1980s, when Deng Xiaoping was already saying that science and technology are primary productive forces.”

From taboo to toolkit

Liu Bingyu, one of He’s professors at the China University of Political Science and Law, says AI can act as “instructor, brainstorm partner, secretary, and devil’s advocate.” She added a full session on AI guidelines to her lecture series this year, after the university encouraged “responsible and confident” use of AI. 

Liu recommends that students use generative AI to write literature reviews, draft abstracts, generate charts, and organize thoughts. She’s created slides that lay out detailed examples of good and bad prompts, along with one core principle: AI can’t replace human judgment. “Only high-quality input and smart prompting can lead to good results,” she says.

“The ability to interact with machines is one of the most important skills in today’s world,” Liu told her class. “And instead of having students do it privately, we should talk about it out in the open.”

This reflects a growing trend across the country. MIT Technology Review reviewed the AI strategies of 46 top Chinese universities and found that almost all of them have added interdisciplinary AI general‑education classes, AI related degree programs and AI literacy modules in the past year. Tsinghua, for example, is establishing a new undergraduate general education college to train students in AI plus another traditional discipline, like biology, healthcare, science, or humanities.

Major institutions like Remin, Nanjing, and Fudan Universities have rolled out general-access AI courses and degree programs that are open to all students, not reserved for computer science majors like the traditional machine-learning classes. At Zhejiang University, an introductory AI class will become mandatory for undergraduates starting in 2024. 

Lin Shangxin, principal of Renmin University of China recently told local media that AI was an “unprecedented opportunity” for humanities and social sciences. “Intead of a challenge, I believe AI would empower humanities studies,” Lin said told The Paper.

The collective action echoes a central government push. In April 2025, the Ministry of Education released new national guidelines calling for sweeping “AI+ education” reforms, aimed at cultivating critical thinking, digital fluency, and real‐world skills at all education levels. Earlier this year, the Beijing municipal government mandated AI education across all schools in the city—from universities to K–12.

Fang believes that more formal AI literacy education will help bridge an emerging divide between students. “There’s a big gap in digital literacy,” he says. “Some students are fluent in AI tools. Others are lost.”

Building the AI university

In the absence of Western tools like ChatGPT and Claude, many Chinese universities have begun deploying local versions of DeepSeek on campus servers to support students. Many top universities have deployed their own locally hosted versions of Deepseek. These campus-specific AI systems–often referred to as the “full-blood version” of Deepseek—offer longer context windows, unlimited dialogue rounds and broader functionality than public-facing free versions. 

This mirrors a broader trend in the West, where companies like OpenAI and Anthropic are rolling out campus-wide education tiers—OpenAI recently offered free ChatGPT Plus to all U.S. and Canadian college students, while Anthropic launched Claude for Education with partners like Northeastern and LSE. But in China, the initiative is typically university-led rather than driven by the companies themselves.

The goal, according to Zhejiang University, is to offer students full access to AI tools so they can stay up to date with the fast-changing technology. Students can use their ID to access the models for free. 

Yanyan Li and Meifang Zhuo, two researchers at Warwick University who have studied students’ use of AI at universities in the UK, believe that AI literacy education has become crucial to students’ success. 

With their colleague Gunisha Aggarwal, they conducted focus groups including college students from different backgrounds and levels to find out how AI is used in academic studies. They found that students’ knowledge of how to use AI comes mainly from personal exploration. “While most students understand that AI output is not always trustworthy, we observed a lot of anxiety on how to use it right,” says Li.

“The goal shouldn’t be preventing students from using AI but guiding them to harness it for effective learning and higher-order thinking,” says Zhuo. 

That lesson has come slowly. A student at Central China Normal University in Wuhan told MIT Technology Review that just a year ago, most of his classmates paid for mirror websites of ChatGPT, using VPNs or semi-legal online marketplaces to access Western models. “Now, everyone just uses DeepSeek and Doubao,” he said. “It’s cheaper, it works in Chinese, and no one’s worried about getting flagged anymore.”

Still, even with increased institutional support, many students feel anxious about whether they’re using AI correctly—or ethically. The use of AI detection tools has created an informal gray economy, where students pay hundreds of yuan to freelancers promising to “AI-detection-proof” their writing, according to a Rest of World report. Three students told MIT Technology Review that this environment has created confusion, stress, and increased anxiety. Across the board, they said they appreciate it when their professor offers clear policies and practical advice, not just warnings.

He, the law student in Beijing, recently joined a career development group to learn more AI skills to prepare for the job market. To many like her, understanding how to use AI better is not just a studying hack but a necessary skill in China’s fragile job market. Eighty percent of job openings available to fresh graduates listed AI-related skills as a plus in 2025, according to a report by the Chinese media outlet YiCai. In a slowed-down economy and a competitive job market, many students see AI as a lifeline. 

 “We need to rethink what is considered ‘original work’ in the age of AI” says Zhuo, “and universities are a crucial site of that conversation”.

America’s AI watchdog is losing its bite

Most Americans encounter the Federal Trade Commission only if they’ve been scammed: It handles identity theft, fraud, and stolen data. During the Biden administration, the agency went after AI companies for scamming customers with deceptive advertising or harming people by selling irresponsible technologies. With yesterday’s announcement of President Trump’s AI Action Plan, that era may now be over. 

In the final months of the Biden administration under chair Lina Khan, the FTC levied a series of high-profile fines and actions against AI companies for overhyping their technology and bending the truth—or in some cases making claims that were entirely false.

It found that the security giant Evolv lied about the accuracy of its AI-powered security checkpoints, which are used in stadiums and schools but failed to catch a seven-inch knife that was ultimately used to stab a student. It went after the facial recognition company Intellivision, saying the company made unfounded claims that its tools operated without gender or racial bias. It fined startups promising bogus “AI lawyer” services and one that sold fake product reviews generated with AI.

These actions did not result in fines that crippled the companies, but they did stop them from making false statements and offered customers ways to recover their money or get out of contracts. In each case, the FTC found, everyday people had been harmed by AI companies that let their technologies run amok.

The plan released by the Trump administration yesterday suggests it believes these actions went too far. In a section about removing “red tape and onerous regulation,” the White House says it will review all FTC actions taken under the Biden administration “to ensure that they do not advance theories of liability that unduly burden AI innovation.” In the same section, the White House says it will withhold AI-related federal funding from states with “burdensome” regulations.

This move by the Trump administration is the latest in its evolving attack on the agency, which provides a significant route of redress for people harmed by AI in the US. It’s likely to result in faster deployment of AI with fewer checks on accuracy, fairness, or consumer harm.

Under Khan, a Biden appointee, the FTC found fans in unexpected places. Progressives called for it to break up monopolistic behavior in Big Tech, but some in Trump’s orbit, including Vice President JD Vance, also supported Khan in her fights against tech elites, albeit for the different goal of ending their supposed censorship of conservative speech. 

But in January, with Khan out and Trump back in the White House, this dynamic all but collapsed. Trump released an executive order in February promising to “rein in” independent agencies like the FTC that wage influence without consulting the president. The next month, he started taking that vow to—and past—its legal limits.

In March, he fired the only two Democratic commissioners at the FTC. On July 17 a federal court ruled that one of those firings, of commissioner Rebecca Slaughter, was illegal given the independence of the agency, which restored Slaughter to her position (the other fired commissioner, Alvaro Bedoya, opted to resign rather than battle the dismissal in court, so his case was dismissed). Slaughter now serves as the sole Democrat.

In naming the FTC in its action plan, the White House now goes a step further, painting the agency’s actions as a major obstacle to US victory in the “arms race” to develop better AI more quickly than China. It promises not just to change the agency’s tack moving forward, but to review and perhaps even repeal AI-related sanctions it has imposed in the past four years.

How might this play out? Leah Frazier, who worked at the FTC for 17 years before leaving in May and served as an advisor to Khan, says it’s helpful to think about the agency’s actions against AI companies as falling into two areas, each with very different levels of support across political lines. 

The first is about cases of deception, where AI companies mislead consumers. Consider the case of Evolv, or a recent case announced in April where the FTC alleges that a company called Workado, which offers a tool to detect whether something was written with AI, doesn’t have the evidence to back up its claims. Deception cases enjoyed fairly bipartisan support during her tenure, Frazier says.

“Then there are cases about responsible use of AI, and those did not seem to enjoy too much popular support,” adds Frazier, who now directs the Digital Justice Initiative at the Lawyers’ Committee for Civil Rights Under Law. These cases don’t allege deception; rather, they charge that companies have deployed AI in a way that harms people.

The most serious of these, which resulted in perhaps the most significant AI-related action ever taken by the FTC and was investigated by Frazier, was announced in 2023. The FTC banned Rite Aid from using AI facial recognition in its stores after it found the technology falsely flagged people, particularly women and people of color, as shoplifters. “Acting on false positive alerts,” the FTC wrote, Rite Aid’s employees “followed consumers around its stores, searched them, ordered them to leave, [and] called the police to confront or remove consumers.”

The FTC found that Rite Aid failed to protect people from these mistakes, did not monitor or test the technology, and did not properly train employees on how to use it. The company was banned from using facial recognition for five years. 

This was a big deal. This action went beyond fact-checking the deceptive promises made by AI companies to make Rite Aid liable for how its AI technology harmed consumers. These types of responsible-AI cases are the ones Frazier imagines might disappear in the new FTC, particularly if they involve testing AI models for bias.

“There will be fewer, if any, enforcement actions about how companies are deploying AI,” she says. The White House’s broader philosophy toward AI, referred to in the plan, is a “try first” approach that attempts to propel faster AI adoption everywhere from the Pentagon to doctor’s offices. The lack of FTC enforcement that is likely to ensue, Frazier says, “is dangerous for the public.”