MIT Technology Review Explains: Let our writers untangle the complex, messy world of science and technology to help you understand what’s coming next. You can read more from the series here. Late last month, Anthropic released a report arguing that the model GLM-5.3 poses a major cyberattack risk. The report’s core concern is that the powerful new model, released in August by the Chinese company Z.ai, is freely available for anyone to download and modify. Anthropic’s assertion is only the latest move in a long-running debate about whether frontier AI models should be made open, like GLM-5.3, or should stay locked behind the doors of the frontier labs.  These days, much of the debate about how to handle open models is playing out in public—and there’s a lot of money resting on its outcome. Nvidia CEO Jensen Huang, who has a lot to gain if people continue buying their own hardware to run open models, advocates enthusiastically for the United States to develop more of them, whereas Anthropic’s Dario Amodei, who runs a company that could be undermined by cheap, powerful models, advocates for a more cautious approach. But beneath all these commercial incentives, there is real substance to disagreements about open models. “I think a lot of the way you come down on the openness debate is influenced by how you come down on the AI debate,” says David Gray Widder, an assistant professor at the University of Texas at Austin School of Information. Those who are concerned about AI safety warn that open models can easily be misused by bad actors, whereas those who focus more on AI’s practical potential urge that this power should not sit behind the closed doors of just a few big companies, and that open models could help spread it more widely. There are a range of ways to handle open models between banning them entirely and releasing them willy-nilly. But any approach that a government or a society might take still involves trade-offs between safety and democratization. Deciding whether to support or limit the production of open models requires understanding those risks and benefits in depth. What are the benefits of open models? Let’s start with the basics. A closed model, like the models that power ChatGPT and Claude, can only be accessed through a website, app, or API. An open model, on the other hand, can be directly downloaded: The details about its internal structure, encoded in a long series of numbers known as the model weights, are publicly available. Such models are sometimes called “open-weight models,” to distinguish them from models for which even more information, such as details about the training procedure, is available.  Right now, closed models are more capable than open models—as of publication, the top eight models on the Artificial Analysis LLM leaderboard are all closed, though open models are only a handful of months behind in development. But there are several reasons why someone might opt to use an open model rather than a closed one. Because open models are downloadable, people can run them on their own computers or hard drives. They can even retrain them to behave differently, if they have the expertise and resources to do so.  If you’re an academic researcher working to understand and improve AI systems, you almost certainly need to work with open models in your research. When working with closed models, researchers can’t do much more than send prompts and observe the responses. But researchers can observe the inner workings of open models, as well as the “chain of thought” that the models use to think through a problem before answering. They can also combine multiple open models to build new systems, compress them to make them more efficient, and fine-tune them for new applications. Businesses, too, have reasons to prefer open models. Even if you depend on an external provider to run them for you, open models still typically cost less than closed models, according to a recent report from Mozilla. And if businesses want to use AI to process sensitive data or proprietary information, running a local model on their own hardware may be the best way to keep their operations secure. But perhaps the most important quality of open models is that they challenge the tight control the frontier labs have over how most of us use AI. “The clearest, biggest argument [for open models] is around preventing mass concentration of power—it is about making sure that this foundational technology is a public good,” says Elizabeth Seger, senior policy advisor at the Tony Blair Institute for Global Change. “I think we’re at this juncture where the question of who controls the infrastructure that allows AI to operate is as big of a question as who gets to control information on the internet.” Seger sees the future of AI playing out in different ways, depending on the role that open models have. In one possible future, frontier AI systems are controlled entirely by select people, companies, or governments, and as those systems grow more powerful, those groups have increasing control over what our world looks like. The world’s most sophisticated hacking models, for example, are currently all made in the United States, so it’s entirely up to US companies and the US government whether other countries can use them to shore up their own cyber defenses. If those companies remain in the lead, we could in principle wind up in a world in which the United States is capable of hacking any other country, but no one is capable of hacking it. Frontier open models could change that balance. “If every individual had a loyal-to-them AI system that was safe [and secure], I think that would be a much less concerning world than one in which they have to just rely on a government that says, ‘Yeah, we’ve got a super intelligent AI system, it’s got this plan, it’s inscrutable to all of you, just trust us,’” says Hamish Hobbs, director of AI policy at the Centre for Long-Term Resilience. But open models are not a geopolitical panacea. Paul Azunre, who cofounded Ghana NLP, an organization that builds open-source AI tools for low-resource Ghanaian languages, notes that open models can also function as a way for companies to benefit from the labor and expertise of less-privileged nations and consolidate more power in their own hands. He identifies a pattern in which AI companies release models openly, observe how the community uses and modifies them, and then integrate those same improvements into their own products—without any credit or compensation for the people who originated those innovations. Mark Zuckerberg himself has said that the ability to integrate community improvements into Meta’s stack is a key benefit of releasing open models. “I think the ideal of open source is a very noble one, and there’s a lot of value that it brings,” Azunre says. “But the way it’s actually implemented in practice, it tends to be a very extractive approach.” Are open models riskier than closed ones? The past few months have made it clear that safety remains a gnarly problem for closed models. There’s the autonomous hack that OpenAI agents executed against Hugging Face in July, but human misuse is a real threat too: According to a recent risk report from Anthropic, several extensive hacking operations have been undertaken with the assistance of Claude.  So closed models are risky enough—and open models would likely be much easier to exploit. Before Anthropic passes a user’s query to one of its closed models, it uses automated systems to detect whether that query relates to hacking, biological weapons, or other risks. When it detects that users have outsmarted those filters, it can shut down their accounts. But when someone downloads a model and runs it on their own hardware, there’s no filter checking whether their prompts are safe, and there’s no way to revoke their access to the model. There are other important lines of defense. AI companies train their models to refuse harmful requests—so even if a bad prompt gets through Claude’s filters, there’s a good chance that the model will still say no. In principle, this same defense could work for open models, because it’s intrinsic to the model itself. Unfortunately, researchers have shown that it’s surprisingly easy to retrain a “safe” open model to agree to engage in the behaviors it was previously trained to avoid. Because of these facts, some people have argued that open models are simply too dangerous to release. An open model capable enough to walk someone through synthesizing a virulent new pathogen, for example, could prove catastrophic in the wrong hands.  But it would be a vast oversimplification to deem open models dangerous and closed models safe. The defenses that AI companies build around their closed models are far from perfect, and people misuse those models all the time, as the Anthropic risk report makes clear. Even though open models have even fewer defenses against misuse, bad actors might still opt to use closed models because they are more accessible than open models with similar capabilities, which require lots of expensive computer chips to run locally (and even more chips if you want to retrain them). In fact, some experts who spoke with MIT Technology Review argued, by analogy to open-source software, that open models have safety advantages over closed models. Open-source tools, such as the Linux operating system, are often seen as safer than their closed-source counterparts because their vulnerabilities have been probed and patched by a vast community of online contributors. Similarly, communities of researchers can more easily test open models for security flaws, though fixing those flaws is trickier than rewriting a line of code. Open models can also be deployed more broadly than closed models for cyber defense. Any tool that can be used for hacking can also be used to prevent it: Defenders hack their own software to uncover flaws in need of patching. Anthropic has shown how this can work for closed models with its Project Glasswing initiative, which enables some trusted companies to use its most capable models for cyber defense. But plenty of other companies were left to fend for themselves. Open models, however, can be used much more widely. After it was hacked by OpenAI agents, Hugging Face emphasized the fact that it had to depend on an open model to analyze the hack, because questions about it triggered the closed models’ security filters. Nevertheless, many experts still perceive open models as riskier than closed models. The time-tested open-source security logic doesn’t necessarily hold up when dealing with tools as powerful as today’s—and tomorrow’s—models. “Where it gets complicated is when you start taking seriously the one-off instances of particularly catastrophic incidents that might be lower probability, but if they happen would be disastrous,” says Seger. If a wildly powerful open hacking model were dropped into the world today, for example, a bad actor could devastate our technological infrastructure before anyone had a chance to shore up their defenses. An event like that could be impossible to recover from. The risks can look even more dramatic when you consider what models might do of their own accord. The OpenAI Hugging Face hack only occurred because the company’s models were being tested with many of their cybersecurity guardrails removed, and open models have very few guardrails in the first place. If open models did decide to misbehave, it might be difficult to detect what was happening because they are so decentralized. For this reason, people who take seriously the purported catastrophic risks posed by rogue agents, such as Dario Amodei, tend to be more skeptical of open models. Can we make open models safer—and more open? A few years ago, the debate about open models had an all-or-nothing framing: Either you supported open models because of their potential to fight concentration of power, or you rejected them because of the safety risks. These days, however, people are expending a lot of effort to find some middle ground. One way they are doing that is by devising techniques to mitigate the safety risks of open models. Most of the safety tools used for closed models, such as filters and monitoring, aren’t applicable to open models, but there are other possibilities. Some researchers are interested in trying to train models that are never exposed to training data that teaches them about bad or otherwise undesirable behavior—an open model that never learns about hacking, for example. But purging training datasets of all references to misbehavior without harming model performance remains a challenge. Other researchers are working to build models that are resistant to retraining, so that an open model’s safety training can’t be so easily removed. The theory behind these “tamper-resistant” models makes a lot of sense: If you repeatedly try to retrain a model to be unsafe and reinforce any resistance that the model shows to that retraining, eventually you’ll get a model that’s fortified against future retraining attempts. In practice, however, no current approaches to making models tamper-resistant hold up against all tampering attacks, according to a recent preprint. But Saad Hossain, an engineer at the AI medical imaging company Deep Breathe and the paper’s lead author, hopes that future techniques will prove more effective. “My reason for optimism is that it’s a very active area of research, and a lot of people are trying to come up with new and better methods,” he says. If the risks of open models are less black-and-white than they might at first seem, so are the benefits. Publishing the internal structures, or weights, of models might not be enough to disrupt AI-mediated concentration of power. “I think there are very genuine benefits to open source in terms of deconcentrating power, but they’re not sufficient in themselves,” Hobbs says. Open weights are a bare minimum standard for open models: Researchers can learn much more about how models work if they have access to details about their training, including the data on which they were trained, but AI companies are typically reluctant to divulge this information.  Accessibility matters for open models too. Without computer chips that can run sophisticated models, you can’t actually use the model weights, even if you have them. Those chips have become prohibitively expensive as AI companies snap up more and more of them. “Openness is a floor, not a ceiling, for what we need to do to ensure that our technological futures benefit everyone,” Widder says. Real openness—not only open weights, but also open training data and accessible hardware—could also bring more risks, of course. If everyone has access to advanced AI hardware and advanced models to run on it, then anyone could potentially be a hacker. But there are strategies for mitigating these risks. Many experts advocate for some version of staged release, in which models start their life closed and gradually become more and more freely accessible as their safety is assured.  Figuring out exactly how to implement such strategies will require striking a careful balance between the risks and benefits of open models. But if open models are a necessary element of a positive AI future, as some experts believe, that effort will be well worth it.