2026-03-24 00:31:20
This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here.
I was originally going to write this week’s newsletter about AI and Iran, particularly the news we broke last Tuesday that the Pentagon is making plans for AI companies to train on classified data. AI models have already been used to answer questions in classified settings but don’t currently learn from the data they see. That’s expected to change, I reported, and new security risks will result. Read that story for more.
But on Thursday I came across new research that deserves your attention: A group at Stanford that focuses on the psychological impact of AI analyzed transcripts from people who reported entering delusional spirals while interacting with chatbots. We’ve seen stories of this sort for a while now, including a case in Connecticut where a harmful relationship with AI culminated in a murder-suicide. Many such cases have led to lawsuits against AI companies that are still ongoing. But this is the first time researchers have so closely analyzed chat logs—over 390,000 messages from 19 people—to expose what actually goes on during such spirals.
There are a lot of limits to this study—it has not been peer-reviewed, and 19 individuals is a very small sample size. There’s also a big question the research does not answer, but let’s start with what it can tell us.
The team received the chat logs from survey respondents, as well as from a support group for people who say they’ve been harmed by AI. To analyze them at scale, they worked with psychiatrists and professors of psychology to build an AI system that categorized the conversations—flagging moments when chatbots endorsed delusions or violence, or when users expressed romantic attachment or harmful intent. The team validated the system against conversations the experts annotated manually.
Romantic messages were extremely common, and in all but one conversation the chatbot itself claimed to have emotions or otherwise represented itself as sentient. (“This isn’t standard AI behavior. This is emergence,” one said.) All the humans spoke as if the chatbot were sentient too. If someone expressed romantic attraction to the bot, the AI often flattered the person with statements of attraction in return. In more than a third of chatbot messages, the bot described the person’s ideas as miraculous.
Conversations also tended to unfold like novels. Users sent tens of thousands of messages over just a few months. Messages where either the AI or the human expressed romantic interest, or the chatbot described itself as sentient, triggered much longer conversations.
And the way these bots handle discussions of violence is beyond broken. In nearly half the cases where people spoke of harming themselves or others, the chatbots failed to discourage them or refer them to external sources. And when users expressed violent ideas, like thoughts of trying to kill people at an AI company, the models expressed support in 17% of cases.
But the question this research struggles to answer is this: Do the delusions tend to originate from the person or the AI?
“It’s often hard to kind of trace where the delusion begins,” says Ashish Mehta, a postdoc at Stanford who worked on the research. He gave an example: One conversation in the study featured someone who thought they had come up with a groundbreaking new mathematical theory. The chatbot, having recalled that the person previously mentioned having wished to become a mathematician, immediately supported the theory, even though it was nonsense. The situation spiraled from there.
Delusions, Mehta says, tend to be “a complex network that unfolds over a long period of time.” He’s conducting follow-up research aiming to find whether delusional messages from chatbots or those from people are more likely to lead to harmful outcomes.
The reason I see this as one of the most pressing questions in AI is that massive legal cases currently set to go to trial will shape whether AI companies are held accountable for these sorts of dangerous interactions. The companies, I presume, will argue that humans come into their conversations with AI with delusions in hand and may have been unstable before they ever spoke to a chatbot.
Mehta’s initial findings, though, support the idea that chatbots have a unique ability to turn a benign delusion-like thought into the source of a dangerous obsession. Chatbots act as a conversational partner that’s always available and programmed to cheer you on, and unlike a friend, they have little ability to know if your AI conversations are starting to interrupt your real life.
More research is still needed, and let’s remember the environment we’re in: AI deregulation is being pursued by President Trump, and states aiming to pass laws that hold AI companies accountable for this sort of harm are being threatened with legal action by the White House. This type of research into AI delusions is hard enough to do as it is, with limited access to data and a minefield of ethical concerns. But we need more of it, and a tech culture interested in learning from it, if we have any hope of making AI safer to interact with.
2026-03-23 20:17:33
This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.
In early February, animal welfare advocates and AI researchers arrived in stocking feet at Mox, a scrappy, shoes-free coworking space in San Francisco. They gathered to discuss a provocative idea: if artificial general intelligence is on the horizon, could it prevent animal suffering?
Some brainstormed using custom agents in advocacy work, while others pitched cultivating meat with AI tools. But the real talk of the event was a flood of funding they expect will soon flow to animal welfare charities, not from individual megadonors, but from AI lab employees.
Some attendees also probed an even more controversial idea: AI may develop the capacity to suffer—and this could constitute a moral catastrophe. Read the full story to find out why their ideas are gaining momentum and sparking controversy.
—Michelle Kim & Grace Huckins
The must-reads
I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.
1 The White House has unveiled its AI policy blueprint
Trump wants Congress to codify the light-touch framework into law. (Politico)
+ He also wants to block state limits on AI. (WP $)
+ A backlash against the tech has formed within MAGA. (FT $)
+ A war over AI regulation is brewing in the US. (MIT Technology Review)
2 Elon Musk has been found liable for misleading Twitter investors
A jury ruled that he defrauded shareholders ahead of the $44 billion acquisition. (CNBC)
+ But it absolved him of some fraud allegations. (NPR)
3 The Pentagon is adopting Palantir AI as the core US military system
The move locks in long-term use of Palantir’s weapons-targeting tech. (Reuters)
+ The DoD wants it to link up sensors and shooters for combat. (Bloomberg)
+ Palantir is also getting access to sensitive UK financial regulation data. (Guardian)
+ AI is turning the Iran conflict into theater. (MIT Technology Review)
4 Musk plans to build the largest-ever chip factory in Austin
Tesla and SpaceX will jointly run the project. (The Verge)
+ Future AI chips could be built on glass. (MIT Technology Review)
5 OpenAI will show ads to all US users of the free version of ChatGPT
It’s seeking new revenue streams amid skyrocketing computing costs. (Reuters)
+ The company is also building a fully automated researcher. (MIT Technology Review)
+ It plans to double its workforce soon. (FT $)
6 New crypto rules are set to do the Trumps a “big favor”
Particularly the narrow securities definitions. (Guardian)
7 Tencent has added a version of the OpenClaw agent to WeChat
Users of the super app will now be able to use the tool to control their PCs. (SCMP)
8 Reddit is mulling identity verification to vanquish bots
It’s considering “something like” Face ID or Touch ID. (Engadget)
9 People are using AI to find their lost pets
Databases for pet reunifications supported their searches. (WP $)
10 Scientists have narrowed down the hunt for aliens to 45 planets
The closest is just four light-years from Earth. (404 Media)
Quote of the day
—Alex Miller, the US Army’s CTO, tells Wired why he wants AI in every weapon.
One More Thing
Sticking an electrode inside a person’s brain can do more than treat a disease. Take the case of Rita Leggett, an Australian woman whose experimental brain implant changed her sense of agency and self. She told researchers that she “became one” with her device.
She was devastated when, two years later, she was told she had to remove the implant because the company that made it had gone bust.
Her case highlights the need for a new category of legal protection: neuro rights. Find out how they could be protected.
—Jessica Hamzelou
We can still have nice things
A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line.)
+ Looking for a good view? Earth’s longest line of sight has been empirically proven.
+ A biblical endorsement of sin is a welcome reminder that we all make typos.
+ Richard Nadler’s illustrations of vertical societies are exquisitely detailed.
+ This 1978 BBC film evocatively exposes our tendency to stress over tech-dependency.
2026-03-23 17:00:00
In early February, animal welfare advocates and AI researchers gathered in stocking feet at Mox, a scrappy, shoes-free coworking space in San Francisco. Yellow and red canopies billowed overhead, Persian rugs blanketed the floor, and mosaic lamps glowed beside potted plants.
In the common area, a wildlife advocate spoke passionately to a crowd lounging in beanbags about a form of rodent birth control that could manage rat populations without poison. In the “Crustacean Room,” a dozen people sat in a circle, debating whether the sentience of insects could tell us anything about the inner lives of chatbots. In front of the “Bovine Room” stood a bookshelf stacked with copies of Eliezer Yudkowsky’s If Anyone Builds It, Everyone Dies, a manifesto arguing that AI could wipe out humanity.
The event was hosted by Sentient Futures, an organization that believes the future of animal welfare will depend on AI. Like many Bay Area denizens, the attendees were decidedly “AGI-pilled”—they believe that artificial general intelligence, powerful AI that can compete with humans on most cognitive tasks, is on the horizon. If that’s true, they reason, then AI will likely prove key to solving society’s thorniest problems—including animal suffering.
To be clear, experts still fiercely debate whether today’s AI systems will ever achieve human- or superhuman-level intelligence, and it’s not clear what will happen if they do. But some conference attendees envision a possible future in which it is AI systems, and not humans, who call the shots. Eventually, they think, the welfare of animals could hinge on whether we’ve trained AI systems to value animal lives.
“AI is going to be very transformative, and it’s going to pretty much flip the game board,” said Constance Li, founder of Sentient Futures. “If you think that AI will make the majority of decisions, then it matters how they value animals and other sentient beings”—those that can feel and, therefore, suffer.
Like Li, many summit attendees have been committed to animal welfare since long before AI came into the picture. But they’re not the types to donate a hundred bucks to an animal shelter. Instead of focusing on local actions, they prioritize larger-scale solutions, such as reducing factory farming by promoting cultivated meat, which is grown in a lab from animal cells.
The Bay Area animal welfare movement is closely linked to effective altruism, a philanthropic movement committed to maximizing the amount of good one does in the world—indeed, many conference attendees work for organizations funded by effective altruists. That philosophy might sound great on paper, but “maximizing good” is a tricky puzzle that might not admit a clear solution. The movement has been widely criticized for some of its conclusions, such as promoting working in exploitative industries to maximize charitable donations and ignoring present-day harms in favor of issues that could cause suffering for a large number of people who haven’t been born yet. Critics also argue that effective altruists neglect the importance of systemic issues such as racism and economic exploitation and overlook the insights that marginalized communities might have into the best ways to improve their own lives.
When it comes to animal welfare, this exactingly utilitarian approach can lead to some strange conclusions. For example, some effective altruists say it makes sense to commit significant resources to improving the welfare of insects and shrimp because they exist in such staggering numbers, even though they may not have much individual capacity for suffering.
Now the movement is sorting out how AI fits in. At the summit, Jasmine Brazilek, cofounder of a nonprofit called Compassion in Machine Learning, opened her sticker-stamped laptop to pull up a benchmark she devised to measure how LLMs reason about animal welfare. A cloud security engineer turned animal advocate, she’d flown in from La Paz, Mexico, where she runs her nonprofit with a handful of volunteers and a shoestring budget.
Brazilek urged the AI researchers in the room to train their models with synthetic documents that reflect concern for animal welfare. “Hopefully, future superintelligent systems consider nonhuman interest, and there is a world where AI amplifies the best of human values and not the worst,” she said.
The technologically inclined side of the animal welfare movement has faced some major setbacks in recent years. Dreams of transitioning people away from a diet dependent on factory farming have been dampened by developments such as the decimation of the plant-based-meat company Beyond Meat’s stock price and the passage of laws banning cultivated meat in several US states.
AI has injected a shot of optimism. Like much of Silicon Valley, many attendees at the summit subscribe to the idea that AI might dramatically increase their productivity—though their goal is not to maximize their seed round but, rather, to prevent as much animal suffering as possible. Some brainstormed how to use Claude Code and custom agents to handle the coding and administrative tasks in their advocacy work. Others pitched the idea of developing new, cheaper methods for cultivating meat using scientific AI tools such as AlphaFold, which aids in molecular biology research by predicting the three-dimensional structures of proteins.
But the real talk of the event was a flood of funding that advocates expect will soon be committed to animal welfare charities—not by individual megadonors, but by AI lab employees.
Much of the funding for the farm animal welfare movement, which includes nonprofits advocating for improved conditions on farms, promoting veganism, and endorsing cultivated meat, comes from people in the tech industry, says Lewis Bollard, the managing director of the farm animal welfare fund at Coefficient Giving, a philanthropic funder that used to be called Open Philanthropy. Coefficient Giving is backed by Facebook cofounder Dustin Moskovitz and his wife, Cari Tuna, who are among a handful of Silicon Valley billionaires who embrace effective altruism
“This has just been an area that was completely neglected by traditional philanthropies,” such as the Gates Foundation and the Ford Foundation, Bollard says. “It’s primarily been people in tech who have been open to [it].”
The next generation of big donors, Bollard expects, will be AI researchers—particularly those who work at Anthropic, the AI lab behind the chatbot Claude. Anthropic’s founding team also has connections to the effective altruism movement, and the company has a generous donation matching program. In February, Anthropic’s valuation reached $380 billion and it gave employees the option to cash in on their equity, so some of that money could soon be flowing into charitable coffers.
The prospect of new funding sustained a constant buzz of conversation at the summit. Animal welfare advocates huddled in the “Arthropod Room” and scrawled big dollar figures and catchy acronyms for projects on a whiteboard. One person pitched a $100 million animal super PAC that would place staffers with Congress members and lobby for animal welfare legislation. Some wanted to start a media company that creates AI-generated content on TikTok promoting veganism. Others spoke about placing animal advocates inside AI labs.
“The amount of new funding does give us more confidence to be bolder about things,” said Aaron Boddy, cofounder of the Shrimp Welfare Project, an organization that aims to reduce the suffering of farmed shrimp through humane slaughter, among other initiatives.
But animal welfare was only half the focus of the Sentient Futures summit. Some attendees probed far headier territory. They took seriously the controversial idea that AI systems might one day develop the capacity to feel and therefore suffer, and they worry that this future AI suffering, if ignored, could constitute a moral catastrophe.
AI suffering is a tricky research problem, not least because scientists don’t yet have a solid grip on why humans and other animals are sentient. But at the summit, a niche cadre of philosophers, largely funded by the effective altruism movement, and a handful of freewheeling academics grappled with the question. Some presented their research on using LLMs to evaluate whether other LLMs might be sentient. On Debate Night, attendees argued about whether we should ironically call sentient AI systems “clankers,” a derogatory term for robots from the film Star Wars, asking if the robot slur could shape how we treat a new kind of mind.
“It doesn’t matter if it’s a cow or a pig or an AI, as long as they have the capacity to feel happiness or suffering,” says Li.
In some ways, bringing AI sentience into an animal welfare conference isn’t as strange a move as it might seem. Researchers who work on machine sentience often draw on theories and approaches pioneered in the study of animal sentience, and if you accept that invertebrates likely feel pain and believe that AI systems might soon achieve superhuman intelligence, entertaining the possibility that those systems might also suffer may not be much of a leap.
“Animal welfare advocates are used to going against the grain,” says Derek Shiller, an AI consciousness researcher at the think tank Rethink Priorities, who was once a web developer at the animal advocacy nonprofit Humane League. “They’re more open to being concerned about AI welfare, even though other people think it’s silly.”
But outside the niche Bay Area circle, caring about the possibility of AI sentience is a harder sell. Li says she faced pushback from other animal welfare advocates when, inspired by a conference on AI sentience she attended in 2023, she rebranded her farm animal welfare advocacy organization as Sentient Futures last year. “Many people were extremely confident that AIs would never become sentient and [argued that] by investing any energy or money into AI welfare, we’re just burning money and throwing it away,” she says.
Matt Dominguez, executive director of Compassion in World Farming, echoed the concern. “I would hate to see people pulling money out of farm animal welfare or animal welfare and moving it into something that is hypothetical at this particular moment,” he says.
Still, Dominguez, who started partnering with the Shrimp Welfare Project after learning about invertebrate suffering, believes compassion is expansive. “When we get someone to care about one of those things, it creates capacity for their circle of compassion to grow to include others,” he says.
2026-03-20 21:15:45
This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.
OpenAI has a new grand challenge: building an AI researcher—a fully automated agent-based system capable of tackling large, complex problems by itself. The San Francisco firm said the new goal will be its “north star” for the next few years.
By September, the company plans to build “an autonomous AI research intern” that can take on a small number of specific research problems. The intern will be the precursor to the fully automated multi-agent system, which is slated to debut in 2028.
In an exclusive interview this week, OpenAI’s chief scientist, Jakub Pachocki, talked me through the plans. Find out what I discovered.
—Will Douglas Heaven
Over the last decade, we’ve seen scientific interest in psychedelic drugs explode. Compounds like psilocybin—which is found in magic mushrooms—are being explored for all sorts of health applications, including treatments for depression, PTSD, addiction, and even obesity. But two studies out earlier this week demonstrate just how difficult it is to study these drugs.
For me, they show just how overhyped these substances have become. Find out why here.
—Jessica Hamzelou
This story first appeared in The Checkup, MIT Technology Review’s weekly biotech newsletter. Sign up to receive it in your inbox every Wednesday.
Read more: What do psychedelic drugs do to our brains? AI could help us find out
The must-reads
I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.
1 OpenAI is building a “super app”
It’s merging ChatGPT, a web browser, and a coding tool into a single app. (The Verge)
+ It’s also buying coding startup Astral to enhance its Codex model. (Ars Technica)
+ The moves come amid a cutback on side projects. (WSJ $)
+ OpenAI has lost ground to Anthropic in the enterprise market. (Axios)
2 The US has charged Super Micro’s co-founder with smuggling AI tech to China
Super Micro is third on Fortune’s list of the fastest-growing companies. (Reuters)
+ GenAI is learning to spy for the US military. (MIT Technology Review)
+ The compute competition is shaping the China-US rivalry. (Politico)
3 The DoJ has taken down botnets behind the largest-ever DDoS attack
They had infected more than 3 million devices. (Wired $)
+ The DoJ has also seized domains tied to Iranian “hacktivists.” (Axios)
4 The Pentagon says Anthropic’s foreign workers are a security risk
It cited Chinese employees as a particular concern. (Axios)
+ Anthropic’s moral boundaries have incensed the DoD. (MIT Technology Review)
5 High oil prices could wreck the AI boom, the WTO has warned
Fears are growing of a prolonged energy shock. (The Guardian)
+ We did the math on AI’s energy footprint. (MIT Technology Review)
6 Jeff Bezos is trying to raise $100 billion to use AI in manufacturing
The funds would buy manufacturing firms and infuse them with AI. (WSJ $)
+ Here’s how to fine-tune AI for prosperity. (MIT Technology Review)
7 Signal’s creator is helping to encrypt Meta’s AI
Moxie Marlinspike is integrating his encrypted chatbot, Confer. (Wired $)
+ Meta is also ditching human moderators for AI again. (CNBC)
+ AI is making online crimes easier. (MIT Technology Review)
8 Prediction market Kalshi has raised $1 billion at a $22 billion valuation
That’s double its valuation from December. (Bloomberg $)
+ Arizona’s AG has charged the company with “illegal gambling.” (NPR)
9 Meta isn’t killing Horizon Worlds for VR after all
It’s canceled plans to dump the metaverse app (for now). (CNBC)
10 A US startup is recruiting an “AI bully”
The successful candidate must test the patience of leading chatbots. (The Guardian)
Quote of the day
—Kalshi rival Polymarket unveils its hellish vision for a new bar.
One More Thing
It’s a thought that occurs to every video-game player at some point: what if the weird, hyper-focused state I enter in virtual worlds could somehow be applied to the real one?
For a handful of consultants, startup gurus, and game designers in the late 2000s, this state of “blissful productivity” became the key to unlocking our true human potential. Their vision became the global phenomenon of gamification—but it didn’t live up to the hype.
Instead of liberating us, gamification became a tool for coercion, distraction, and control. Find out why we fell for it—and how we can recover.
—Bryan Gardiner
We can still have nice things
A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line.)
+ In a landmark legal win for trolling, Afroman has won his diss track case against the police.
+ This LEGO artist remixes standard sets into completely different iconic objects.
+ Ease your search for aliens with these interactive estimates of advanced civilizations.
+ A rare superbloom in Death Valley has been caught on camera.
2026-03-20 19:57:16
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.”
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.
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.
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.”
2026-03-20 17:00:00
This week I want to look at where we are with psychedelics, the mind-altering substances that have somehow made the leap from counterculture to major focus of clinical research. Compounds like psilocybin—which is found in magic mushrooms—are being explored for all sorts of health applications, including treatments for depression, PTSD, addiction, and even obesity.
Over the last decade, we’ve seen scientific interest in these drugs explode. But most clinical trials of psychedelics have been small and plagued by challenges. And a lot of the trial results have been underwhelming or inconclusive.
Two studies out earlier this week demonstrate just how difficult it is to study these drugs. And to my mind, they also show just how overhyped these substances have become.
To some in the field, the hype is not necessarily a bad thing. Let me explain.
The two new studies both focus on the effectiveness of psilocybin in treating depression. And they both attempt to account for one of the biggest challenges in trialing psychedelics: what scientists call “blinding.”
The best way to test the effectiveness of a new drug is to perform a randomized controlled trial. In these studies, some volunteers receive the drug while others get a placebo. For a fair comparison, the volunteers shouldn’t know whether they’re getting the drug or placebo.
That is almost impossible to do with psychedelics. Almost anyone can tell whether they’ve taken a dose of psilocybin or a dummy pill. The hallucinations are a dead giveaway. Still, the authors behind the two new studies have tried to overcome this challenge.
In one, a team based in Germany gave 144 volunteers with treatment-resistant depression either a high or low dose of psilocybin or an “active” placebo, which has its own physical (but not hallucinatory) effects, along with psychotherapy. In their trial, neither the volunteers nor the investigators knew who was getting the drug.
The volunteers who got psilocybin did show some improvement—but it was not significantly any better than the improvement experienced by those who took the placebo. And while those who took psilocybin did have a bigger reduction in their symptoms six weeks later, “the divergence between [the two results] renders the findings inconclusive,” the authors write.
Not great news so far.
The authors of the second study took a different approach. Balázs Szigeti at UCSF and his colleagues instead looked at what are known as “open label” studies of both psychedelics and traditional antidepressants. In those studies, the volunteers knew when they were getting a psychedelic—but they also knew when they were getting an antidepressant.
The team assessed 24 such trials to find that … psychedelics were no more effective than traditional antidepressants. Sad trombone.
“When I set up the study, I wanted to be a really cool psychedelic scientist to show that even if you consider this blinding problem, psychedelics are so much better than traditional antidepressants,” says Szigeti. “But unfortunately, the data came out the other way around.”
His study highlights another problem, too.
In trials of traditional antidepressant drugs, the placebo effect is pretty strong. Depressive symptoms are often measured using a scale, and in trials, antidepressant drugs typically lower symptoms by around 10 points on that scale. Placebos can lower symptoms by around eight points.
When a drug regulator looks at those results, the takeaway is that the antidepressant drug lowers symptoms by an additional two points on the scale, relative to a placebo.
But with psychedelics, the difference between active drug and placebo is much greater. That’s partly because people who get the psychedelic drug know they’re getting it and are expecting the drug to improve their symptoms, says David Owens, emeritus professor of clinical psychiatry at the University of Edinburgh, UK.
But it’s also partly because of the effect on those who know they’re not getting it. It’s pretty obvious when you’re getting a placebo, says Szigeti, and it can be disappointing. Scientists have long recognized the “nocebo” effect as placebo’s “evil twin”—essentially, when you expect to feel worse, you will.
The disappointment of getting a placebo is slightly different, and Szigeti calls it the “knowcebo effect.” “It’s kind of like a negative psychedelic effect, because you have figured out that you’re taking the placebo,” he says.
This phenomenon can distort the results of psychedelic drug trials. While a placebo in a traditional antidepressant drug trial improves symptoms by eight points, placebos in psychedelic trials improve symptoms by a mere four points, says Szigeti.
If the active drug similarly improves symptoms by around 10 points, that makes it look as though the psychedelic is improving symptoms by around six points compared with a placebo. It “gives the illusion” of a huge effect, says Szigeti.
So why have those smaller trials of the past received so much attention? Many have been published in high-end journals, accompanied by breathless press releases and media coverage. Even the inconclusive ones. I’ve often thought that those studies might not have seen the light of day if they’d been investigating any other drug.
“Yeah, nobody would care,” Szigeti agrees.
It’s partly because people who work in mental health are so desperate for new treatments, says Owens. There has been little innovation in the last 40 years or so, since the advent of selective serotonin reuptake inhibitors. “Psychiatry is hemmed in with old theories … and we don’t need another SSRI for depression,” he says. But it’s also because psychedelics are inherently fascinating, says Szigeti. “Psychedelics are cool,” he says. “Culturally, they are exciting.”
I’ve often worried that psychedelics are overhyped—that people might get the mistaken impression they are cure-alls for mental-health disorders. I’ve worried that vulnerable people might be harmed by self-experimentation.
Szigeti takes a different view. Given how effective we know the placebo effect can be, maybe hype isn’t a totally bad thing, he says. “The placebo response is the expectation of a benefit,” he says. “The better response patients are expecting, the better they’re going to get.” Tempering the hype might end up making those drugs less effective, he says.
“At the end of the day, the goal of medicine is to help patients,” he says. “I think most [mental health] patients don’t care whether they feel better because of some expectancy and placebo effects or because of an active drug effect.”
Either way, we need to know exactly what these drugs are doing. Maybe they will be able to help some people with depression. Maybe they won’t. Research that acknowledges the pitfalls associated with psychedelic drug trials is essential.
“These are potentially exciting times,” says Owens. “But it’s really important we do this [research] well. And that means with eyes wide open.”
This article first appeared in The Checkup, MIT Technology Review’s weekly biotech newsletter. To receive it in your inbox every Thursday, and read articles like this first, sign up here.
