Welcome back to The State of AI, a new collaboration between the Financial Times and MIT Technology Review. Every Monday for the next two weeks, writers from both publications will debate one aspect of the generative AI revolution reshaping global power. This week, Richard Waters, FT columnist and former West Coast editor, talks with MIT Technology Review’s editor at large David Rotman about the true impact of AI on the job market. Bonus: If you’re an MIT Technology Review subscriber, you can join David and Richard, alongside MIT Technology Review’s editor in chief, Mat Honan, for an exclusive conversation live on Tuesday, December 9 at 1pm ET about this topic. Sign up to be a part here. Richard Waters writes:
Any far-reaching new technology is always uneven in its adoption, but few have been more uneven than generative AI. That makes it hard to assess its likely impact on individual businesses, let alone on productivity across the economy as a whole. At one extreme, AI coding assistants have revolutionized the work of software developers. Mark Zuckerberg recently predicted that half of Meta’s code would be written by AI within a year. At the other extreme, most companies are seeing little if any benefit from their initial investments. A widely cited study from MIT found that so far, 95% of gen AI projects produce zero return.
That has provided fuel for the skeptics who maintain that—by its very nature as a probabilistic technology prone to hallucinating—generative AI will never have a deep impact on business. To many students of tech history, though, the lack of immediate impact is just the normal lag associated with transformative new technologies. Erik Brynjolfsson, then an assistant professor at MIT, first described what he called the “productivity paradox of IT” in the early 1990s. Despite plenty of anecdotal evidence that technology was changing the way people worked, it wasn’t showing up in the aggregate data in the form of higher productivity growth. Brynjolfsson’s conclusion was that it just took time for businesses to adapt. Big investments in IT finally showed through with a notable rebound in US productivity growth starting in the mid-1990s. But that tailed off a decade later and was followed by a second lull. FT/MIT TECHNOLOGY REVIEW | ADOBE STOCK In the case of AI, companies need to build new infrastructure (particularly data platforms), redesign core business processes, and retrain workers before they can expect to see results. If a lag effect explains the slow results, there may at least be reasons for optimism: Much of the cloud computing infrastructure needed to bring generative AI to a wider business audience is already in place. The opportunities and the challenges are both enormous. An executive at one Fortune 500 company says his organization has carried out a comprehensive review of its use of analytics and concluded that its workers, overall, add little or no value. Rooting out the old software and replacing that inefficient human labor with AI might yield significant results. But, as this person says, such an overhaul would require big changes to existing processes and take years to carry out. There are some early encouraging signs. US productivity growth, stuck at 1% to 1.5% for more than a decade and a half, rebounded to more than 2% last year. It probably hit the same level in the first nine months of this year, though the lack of official data due to the recent US government shutdown makes this impossible to confirm. It is impossible to tell, though, how durable this rebound will be or how much can be attributed to AI. The effects of new technologies are seldom felt in isolation. Instead, the benefits compound. AI is riding earlier investments in cloud and mobile computing. In the same way, the latest AI boom may only be the precursor to breakthroughs in fields that have a wider impact on the economy, such as robotics. ChatGPT might have caught the popular imagination, but OpenAI’s chatbot is unlikely to have the final word. David Rotman replies: 

This is my favorite discussion these days when it comes to artificial intelligence. How will AI affect overall economic productivity? Forget about the mesmerizing videos, the promise of companionship, and the prospect of agents to do tedious everyday tasks—the bottom line will be whether AI can grow the economy, and that means increasing productivity.  But, as you say, it’s hard to pin down just how AI is affecting such growth or how it will do so in the future. Erik Brynjolfsson predicts that, like other so-called general purpose technologies, AI will follow a J curve in which initially there is a slow, even negative, effect on productivity as companies invest heavily in the technology before finally reaping the rewards. And then the boom.  But there is a counterexample undermining the just-be-patient argument. Productivity growth from IT picked up in the mid-1990s but since the mid-2000s has been relatively dismal. Despite smartphones and social media and apps like Slack and Uber, digital technologies have done little to produce robust economic growth. A strong productivity boost never came. Ask AIWhy it matters to you?BETAHere’s why this story might matter to you, according to AI. This is a beta feature and AI hallucinates—it might get weirdTell me why it matters Daron Acemoglu, an economist at MIT and a 2024 Nobel Prize winner, argues that the productivity gains from generative AI will be far smaller and take far longer than AI optimists think. The reason is that though the technology is impressive in many ways, the field is too narrowly focused on products that have little relevance to the largest business sectors. The statistic you cite that 95% of AI projects lack business benefits is telling.  Take manufacturing. No question, some version of AI could help; imagine a worker on the factory floor snapping a picture of a problem and asking an AI agent for advice. The problem is that the big tech companies creating AI aren’t really interested in solving such mundane tasks, and their large foundation models, mostly trained on the internet, aren’t all that helpful.  It’s easy to blame the lack of productivity impact from AI so far on business practices and poorly trained workers. Your example of the executive of the Fortune 500 company sounds all too familiar. But it’s more useful to ask how AI can be trained and fine-tuned to give workers, like nurses and teachers and those on the factory floor, more capabilities and make them more productive at their jobs.  The distinction matters. Some companies announcing large layoffs recently cited AI as the reason. The worry, however, is that it’s just a short-term cost-saving scheme. As economists like Brynjolfsson and Acemoglu agree, the productivity boost from AI will come when it’s used to create new types of jobs and augment the abilities of workers, not when it is used just to slash jobs to reduce costs. 
Richard Waters responds :  I see we’re both feeling pretty cautious, David, so I’ll try to end on a positive note. 
Some analyses assume that a much greater share of existing work is within the reach of today’s AI. McKinsey reckons 60% (versus 20% for Acemoglu) and puts annual productivity gains across the economy at as much as 3.4%. Also, calculations like these are based on automation of existing tasks; any new uses of AI that enhance existing jobs would, as you suggest, be a bonus (and not just in economic terms). Cost-cutting always seems to be the first order of business with any new technology. But we’re still in the early stages and AI is moving fast, so we can always hope. Further reading FT chief economics commentator Martin Wolf has been skeptical about whether tech investment boosts productivity but says AI might prove him wrong. The downside: Job losses and wealth concentration might lead to “techno-feudalism.” The FT’s Robert Armstrong argues that the boom in data center investment need not turn to bust. The biggest risk is that debt financing will come to play too big a role in the buildout. Last year, David Rotman wrote for MIT Technology Review about how we can make sure AI works for us in boosting productivity, and what course corrections will be required.David also wrote this piece about how we can best measure the impact of basic R&D funding on economic growth, and why it can often be bigger than you might think.

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. An AI model trained on prison phone calls now looks for planned crimes in those calls A US telecom company trained an AI model on years of inmates’ phone and video calls and is now piloting that model to scan their calls, texts, and emails in the hope of predicting and preventing crimes.Securus Technologies president Kevin Elder told MIT Technology Review that the company began building its AI tools in 2023, using its massive database of recorded calls to train AI models to detect criminal activity. It created one model, for example, using seven years of calls made by inmates in the Texas prison system, but it has been working on models for other states and counties.However, prisoner rights advocates say that the new AI system enables a system of invasive surveillance, and courts have specified few limits to this power.  Read the full story. —James O’Donnell
Nominations are now open for our global 2026 Innovators Under 35 competition
We have some exciting news: Nominations are now open for MIT Technology Review’s 2026 Innovators Under 35 competition. This annual list recognizes 35 of the world’s best young scientists and inventors, and our newsroom has produced it for more than two decades.  It’s free to nominate yourself or someone you know, and it only takes a few moments. Here’s how to submit your nomination. The must-reads I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology. 1 New York is cracking down on personalized pricing algorithmsA new law forces retailers to declare if their pricing is informed by users’ data. (NYT $)+ The US National Retail Federation tried to block it from passing. (TechCrunch) 2 The White House has launched a media bias trackerComplete with a “media offender of the week” section and a Hall of Shame. (WP $)+ The Washington Post is currently listed as the site’s top offender. (The Guardian)+ Donald Trump has lashed out at several reporters in the past few weeks. (The Hill)3 American startups are hooked on open-source Chinese AI modelsThey’re cheap and customizable—what’s not to like? (NBC News)+ Americans also love China’s cheap goods, regardless of tariffs. (WP $)+ The State of AI: Is China about to win the race? (MIT Technology Review) 4 How police body cam footage became viral YouTube contentRecent arrestees live in fear of ending up on popular channels. (Vox)+ AI was supposed to make police bodycams better. What happened? (MIT Technology Review)5 Construction workers are cashing in on the data center boomMight as well enjoy it while it lasts. (WSJ $)+ The data center boom in the desert. (MIT Technology Review)

6 China isn’t convinced by cryptoEven though bitcoin mining is quietly making a (banned) comeback. (Reuters)+ The country’s central bank is no fan of stablecoins. (CoinDesk) 7 A startup is treating its AI companions like characters in a novelCould that approach make for better AI companions? (Fast Company $)+ Gemini is the most empathetic model, apparently. (Semafor)+ The looming crackdown on AI companionship. (MIT Technology Review)8 Ozempic is so yesterday 💉New weight-loss drugs are tailored to individual patients. (The Atlantic $)+ What we still don’t know about weight-loss drugs. (MIT Technology Review) 9 AI is upending how consultants workFor the third year in a row, big firms are freezing junior workers’ salaries. (FT $)10 Behind the scenes of Disney’s AI animation acceleratorWhat took five months to create has been whittled down to under five weeks. (CNET)+ Director supremo James Cameron appears to have changed his mind about AI. (TechCrunch)+ Why are people scrolling through weirdly-formatted TV clips? (WP $) Quote of the day “[I hope AI] comes to a point where it becomes sort of mental junk food and we feel sick and we don’t know why.” —Actor Jenna Ortega outlines her hopes for AI’s future role in filmmaking, Variety reports.
One more thing The weeds are winningSince the 1980s, more and more plants have evolved to become immune to the biochemical mechanisms that herbicides leverage to kill them. This herbicidal resistance threatens to decrease yields—out-of-control weeds can reduce them by 50% or more, and extreme cases can wipe out whole fields.
At worst, it can even drive farmers out of business. It’s the agricultural equivalent of antibiotic resistance, and it keeps getting worse. Weeds have evolved resistance to 168 different herbicides and 21 of the 31 known “modes of action,” which means the specific biochemical target or pathway a chemical is designed to disrupt. Agriculture needs to embrace a diversity of weed control practices. But that’s much easier said than done. Read the full story.

We have some exciting news: Nominations are now open for MIT Technology Review’s 2026 Innovators Under 35 competition. This annual list recognizes 35 of the world’s best young scientists and inventors, and our newsroom has produced it for more than two decades.  It’s free to nominate yourself or someone you know, and it only takes a few moments. Submit your nomination before 5 p.m. ET on Tuesday, January 20, 2026.  We’re looking for people who are making important scientific discoveries and applying that knowledge to build new technologies. Or those who are engineering new systems and algorithms that will aid our work or extend our abilities.  Each year, many honorees are focused on improving human health or solving major problems like climate change; others are charting the future path of artificial intelligence or developing the next generation of robots. 
The most successful candidates will have made a clear advance that is expected to have a positive impact beyond their own field. They should be the primary scientific or technical driver behind the work involved, and we like to see some signs that a candidate’s innovation is gaining real traction. You can look at last year’s list to get an idea of what we look out for. We encourage self-nominations, and if you previously nominated someone who wasn’t selected, feel free to put them forward again. Please note: To be eligible for the 2026 list, nominees must be under the age of 35 as of October 1, 2026.  Semifinalists will be notified by early March and asked to complete an application at that time. Winners are then chosen by the editorial staff of MIT Technology Review, with input from a panel of expert judges. (Here’s more info about our selection process and timelines.)  If you have any questions, please contact [email protected]. We look forward to reviewing your nominations. Good luck! 

A US telecom company trained an AI model on years of inmates’ phone and video calls and is now piloting that model to scan their calls, texts, and emails in the hope of predicting and preventing crimes.  Securus Technologies president Kevin Elder told MIT Technology Review that the company began building its AI tools in 2023, using its massive database of recorded calls to train AI models to detect criminal activity. It created one model, for example, using seven years of calls made by inmates in the Texas prison system, but it has been working on building other state- or county-specific models. Over the past year, Elder says, Securus has been piloting the AI tools to monitor inmate conversations in real time (the company declined to specify where this is taking place, but its customers include jails holding people awaiting trial, prisons for those serving sentences, and Immigrations and Customs Enforcement detention facilities). “We can point that large language model at an entire treasure trove [of data],” Elder says, “to detect and understand when crimes are being thought about or contemplated, so that you’re catching it much earlier in the cycle.”
As with its other monitoring tools, investigators at detention facilities can deploy the AI features to monitor randomly selected conversations or those of individuals suspected by facility investigators of criminal activity, according to Elder. The model will analyze phone and video calls, text messages, and emails and then flag sections for human agents to review. These agents then send them to investigators for follow-up.  In an interview, Elder said Securus’ monitoring efforts have helped disrupt human trafficking and gang activities organized from within prisons, among other crimes, and said its tools are also used to identify prison staff who are bringing in contraband. But the company did not provide MIT Technology Review with any cases specifically uncovered by its new AI models. 
People in prison, and those they call, are notified that their conversations are recorded. But this doesn’t mean they’re aware that those conversations could be used to train an AI model, says Bianca Tylek, executive director of the prison rights advocacy group Worth Rises.  “That’s coercive consent; there’s literally no other way you can communicate with your family,” Tylek says. And since inmates in the vast majority of states pay for these calls, she adds, “not only are you not compensating them for the use of their data, but you’re actually charging them while collecting their data.” A company spokesperson said that correctional facilities determine their own recording and monitoring policies, which Securus follows, and did not directly answer whether inmates can opt out of having their recordings used to train AI. Other advocates for inmates say Securus has a history of violating their civil liberties. For example, leaks of its recordings databases showed the company had improperly recorded thousands of calls between inmates and their attorneys. Corene Kendrick, the deputy director of the ACLU’s National Prison Project, says that the new AI system enables a system of invasive surveillance, and courts have specified few limits to this power. “[Are we] going to stop crime before it happens because we’re monitoring every utterance and thought of incarcerated people?” Kendrick says. “I think this is one of many situations where the technology is way far ahead of the law.” The Secrurus spokesperson said the tool “is not focused on surveilling or targeting specific individuals, but rather on identifying broader patterns, anomalies, and unlawful behaviors across the entire communication system.” They added that its function is to make monitoring more efficient amid staffing shortages, “not to surveil individuals without cause.” Securus will have an easier time funding its AI tool thanks to the company’s recent win in a battle with regulators over how telecom companies can spend the money they collect from inmates’ calls. In 2024, the Federal Communications Commission issued a major reform, shaped and lauded by advocates for prisoners’ rights, that forbade telecoms from passing the costs of recording and surveilling calls on to inmates. Companies were allowed to continue to charge inmates a capped rate for calls, but prisons and jails were ordered to pay for most security costs out of their own budgets.

Negative reactions to this change were swift. Associations of sheriffs (who typically run county jails) complained they could no longer afford proper monitoring of calls, and attorneys general from 14 states sued over the ruling. Some prisons and jails warned they would cut off access to phone calls.  While it was building and piloting its AI tool, Securus held meetings with the FCC and lobbied for a rule change, arguing that the 2024 reform went too far and asking that the agency again allow companies to use fees collected from inmates to pay for security.  In June, Brendan Carr, whom President Donald Trump appointed to lead the FCC, said it would postpone all deadlines for jails and prisons to adopt the 2024 reforms, and even signaled that the agency wants to help telecom companies fund their AI surveillance efforts with the fees paid by inmates. In a press release, Carr wrote that rolling back the 2024 reforms would “lead to broader adoption of beneficial public safety tools that include advanced AI and machine learning.” On October 28, the agency went further: It voted to pass new, higher rate caps and allow companies like Securus to pass security costs relating to recording and monitoring of calls—like storing recordings, transcribing them, or building AI tools to analyze such calls, for example—on to inmates. A spokesperson for Securus told MIT Technology Review that the company aims to balance affordability with the need to fund essential safety and security tools. “These tools, which include our advanced monitoring and AI capabilities, are fundamental to maintaining secure facilities for incarcerated individuals and correctional staff and to protecting the public,” they wrote. FCC commissioner Anna Gomez dissented in last month’s ruling. “Law enforcement,” she wrote in a statement, “should foot the bill for unrelated security and safety costs, not the families of incarcerated people.” The FCC will be seeking comment on these new rules before they take final effect. 

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. What we still don’t know about weight-loss drugs Weight-loss drugs have been back in the news this week. First, we heard that Eli Lilly, the company behind Mounjaro and Zepbound, became the first healthcare company in the world to achieve a trillion-dollar valuation.But we also learned that, disappointingly, GLP-1 drugs don’t seem to help people with Alzheimer’s disease. And that people who stop taking the drugs when they become pregnant can experience potentially dangerous levels of weight gain. On top of that, some researchers worry that people are using the drugs postpartum to lose pregnancy weight without understanding potential risks. All of this news should serve as a reminder that there’s a lot we still don’t know about these drugs. So let’s look at the enduring questions surrounding GLP-1 agonist drugs.
—Jessica Hamzelou 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.
If you’re interested in weight loss drugs and how they affect us, take a look at: + GLP-1 agonists like Wegovy, Ozempic, and Mounjaro might benefit heart and brain health—but research suggests they might also cause pregnancy complications and harm some users. Read the full story.+ We’ve never understood how hunger works. That might be about to change. Read the full story.+ Weight-loss injections have taken over the internet. But what does this mean for people IRL?+ This vibrating weight-loss pill seems to work—in pigs. Read the full story. What we know about how AI is affecting the economy There’s a lot at stake when it comes to understanding how AI is changing the economy right now. Should we be pessimistic? Optimistic? Or is the situation too nuanced for that?Hopefully, we can point you towards some answers. Mat Honan, our editor in chief, will hold a special subscriber-only Roundtables conversation with our editor at large David Rotman, and Richard Waters, Financial Times columnist, exploring what’s happening across different markets. Register here to join us at 1pm ET on Tuesday December 9. The event is part of the Financial Times and MIT Technology Review “The State of AI” partnership, exploring the global impact of artificial intelligence. Over the past month, we’ve been running discussions between our journalists—sign up here to receive future editions every Monday. The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology. 1 Tech billionaires are gearing up to fight AI regulation By amassing multi-million dollar war chests ahead of the 2026 US midterm elections. (WSJ $)+ Donald Trump’s “Manhattan Project” for AI is certainly ambitious. (The Information $)2 The EU wants to hold social media platforms liable for financial scamsNew rules will force tech firms to compensate banks if they fail to remove reported scams. (Politico)3 China is worried about a humanoid robot bubbleBecause more than 150 companies there are building very similar machines. (Bloomberg $)+ It could learn some lessons from the current AI bubble. (CNN)+ Why the humanoid workforce is running late. (MIT Technology Review)4 A Myanmar scam compound was blown upBut its residents will simply find new bases for their operations. (NYT $)+ Experts suspect the destruction may have been for show. (Wired $)+ Inside a romance scam compound—and how people get tricked into being there. (MIT Technology Review) 5 Navies across the world are investing in submarine drones They cost a fraction of what it takes to run a traditional manned sub. (The Guardian)+ How underwater drones could shape a potential Taiwan-China conflict. (MIT Technology Review) 6 What to expect from China’s seemingly unstoppable innovation driveIts extremely permissive regulators play a big role. (Economist $)+ Is China about to win the AI race? (MIT Technology Review) 7 The UK is waging a war on VPNsGood luck trying to persuade people to stop using them. (The Verge) 8 We’re learning more about Jeff Bezos’ mysterious clock projectHe’s backed the Clock of the Long Now for years—and construction is amping up. (FT $)+ How aging clocks can help us understand why we age—and if we can reverse it. (MIT Technology Review) 9 Have we finally seen the first hints of dark matter?These researchers seem to think so. (New Scientist $)10 A helpful robot is helping archaeologists reconstruct PompeiiReassembling ancient frescos is fiddly and time-consuming, but less so if you’re a dextrous machine. (Reuters)
Quote of the day “We do fail… a lot.”
—Defense company Anduril explains its move-fast-and-break-things ethos to the Wall Street Journal in response to reports its systems have been marred by issues in Ukraine. One more thing How to build a better AI benchmark It’s not easy being one of Silicon Valley’s favorite benchmarks. SWE-Bench (pronounced “swee bench”) launched in November 2024 as a way to evaluate an AI model’s coding skill. It has since quickly become one of the most popular tests in AI. A SWE-Bench score has become a mainstay of major model releases from OpenAI, Anthropic, and Google—and outside of foundation models, the fine-tuners at AI firms are in constant competition to see who can rise above the pack.
Despite all the fervor, this isn’t exactly a truthful assessment of which model is “better.” Entrants have begun to game the system—which is pushing many others to wonder whether there’s a better way to actually measure AI achievement. Read the full story. —Russell Brandom 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 or skeet ’em at me.) + Aww, these sharks appear to be playing with pool toys.+ Strange things are happening over on Easter Island (even weirder than you can imagine) 🗿+ Very cool—archaeologists have uncovered a Roman tomb that’s been sealed shut for 1,700 years.+ This Japanese mass media collage is making my eyes swim, in a good way.

EXECUTIVE SUMMARY MIT Technology Review Explains: Let our writers untangle the complex, messy world of technology to help you understand what’s coming next. You can read more from the series here. Weight-loss drugs have been back in the news this week. First, we heard that Eli Lilly, the company behind the drugs Mounjaro and Zepbound, became the first healthcare company in the world to achieve a trillion-dollar valuation. Those two drugs, which are prescribed for diabetes and obesity respectively, are generating billions of dollars in revenue for the company. Other GLP-1 agonist drugs—a class that includes Mounjaro and Zepbound, which have the same active ingredient—have also been approved to reduce the risk of heart attack and stroke in overweight people. Many hope these apparent wonder drugs will also treat neurological disorders and potentially substance use disorders, too. But this week we also learned that, disappointingly, GLP-1 drugs don’t seem to help people with Alzheimer’s disease. And that people who stop taking the drugs when they become pregnant can experience potentially dangerous levels of weight gain during their pregnancies. On top of that, some researchers worry that people are using the drugs postpartum to lose pregnancy weight without understanding potential risks.
All of this news should serve as a reminder that there’s a lot we still don’t know about these drugs. This week, let’s look at the enduring questions surrounding GLP-1 agonist drugs. First a quick recap. Glucagon-like peptide-1 is a hormone made in the gut that helps regulate blood sugar levels. But we’ve learned that it also appears to have effects across the body. Receptors that GLP-1 can bind to have been found in multiple organs and throughout the brain, says Daniel Drucker, an endocrinologist at the University of Toronto who has been studying the hormone for decades.
GLP-1 agonist drugs essentially mimic the hormone’s action. Quite a few have been developed, including semaglutide, tirzepatide, liraglutide, and exenatide, which have brand names like Ozempic, Saxenda and Wegovy. Some of them are recommended for some people with diabetes. Ask AIWhy it matters to you?BETAHere’s why this story might matter to you, according to AI. This is a beta feature and AI hallucinates—it might get weirdTell me why it matters But because these drugs also seem to suppress appetite, they have become hugely popular weight loss aids. And studies have found that many people who take them for diabetes or weight loss experience surprising side effects; that their mental health improves, for example, or that they feel less inclined to smoke or consume alcohol. Research has also found that the drugs seem to increase the growth of brain cells in lab animals. So far, so promising. But there are a few outstanding gray areas. Are they good for our brains? Novo Nordisk, a competitor of Eli Lilly, manufactures GLP-1 drugs Wegovy and Saxenda. The company recently trialed an oral semaglutide in people with Alzheimer’s disease who had mild cognitive impairment or mild dementia. The placebo-controlled trial included 3808 volunteers. Unfortunately, the company found that the drug did not appear to delay the progression of Alzheimer’s disease in the volunteers who took it. The news came as a huge disappointment to the research community. “It was kind of crushing,” says Drucker. That’s despite the fact that, deep down, he wasn’t expecting a “clear win.” Alzheimer’s disease has proven notoriously difficult to treat, and by the time people get a diagnosis, a lot of damage has already taken place. But he is one of many that isn’t giving up hope entirely. After all, research suggests that GLP-1 reduces inflammation in the brain and improves the health of neurons, and that it appears to improve the way brain regions communicate with each other. This all implies that GLP-1 drugs should benefit the brain, says Drucker. There’s still a chance that the drugs might help stave off Alzheimer’s in those who are still cognitively healthy.

Are they safe before, during or after pregnancy? Other research published this week raises questions about the effects of GLP-1s taken around the time of pregnancy. At the moment, people are advised to plan to stop taking the medicines two months before they become pregnant. That’s partly because some animal studies suggest the drugs can harm the development of a fetus, but mainly because scientists haven’t studied the impact on pregnancy in humans. Among the broader population, research suggests that many people who take GLP-1s for weight loss regain much of their lost weight once they stop taking those drugs. So perhaps it’s not surprising that a study published in JAMA earlier this week saw a similar effect in pregnant people. The study found that people who had been taking those drugs gained around 3.3kg more than others who had not. And those who had been taking the drugs also appeared to have a slightly higher risk of gestational diabetes, blood pressure disorders and even preterm birth. It sounds pretty worrying. But a different study published in August had the opposite finding—it noted a reduction in the risk of those outcomes among women who had taken the drugs before becoming pregnant. If you’re wondering how to make sense of all this, you’re not the only one. No one really knows how these drugs should be used before pregnancy—or during it for that matter. Another study out this week found that people (in Denmark) are increasingly taking GLP-1s postpartum to lose weight gained during pregnancy. Drucker tells me that, anecdotally, he gets asked about this potential use a lot. But there’s a lot going on in a postpartum body. It’s a time of huge physical and hormonal change that can include bonding, breastfeeding and even a rewiring of the brain. We have no idea if, or how, GLP-1s might affect any of those.
How—and when—can people safely stop using them? Yet another study out this week—you can tell GLP-1s are one of the hottest topics in medicine right now—looked at what happens when people stop taking tirzepatide (marketed as Zepbound) for their obesity.
The trial participants all took the drug for 36 weeks, at which point half continued with the drug, and half were switched to a placebo for another 52 weeks. During that first 36 weeks, the weight and heart health of the participants improved. But by the end of the study, most of those that had switched to a placebo had regained more than 25% of the weight they had originally lost. One in four had regained more than 75% of that weight, and 9% ended up at a higher weight than when they’d started the study. Their heart health also worsened. Does that mean that people need to take these drugs forever? Scientists don’t have the answer to that one, either. Or if taking the drugs indefinitely is safe. The answer might depend on the individual, their age or health status, or what they are using the drug for. There are other gray areas. GLP-1s look promising for substance use disorders, but we don’t yet know how effective they might be. We don’t know the long-term effects these drugs have on children who take them. And we don’t know the long-term consequences these drugs might have for healthy-weight people who take them for weight loss. Earlier this year, Drucker accepted a Breakthrough Prize in Life Sciences at a glitzy event in California. “All of these Hollywood celebrities were coming up to me and saying ‘thank you so much,’” he says. “A lot of these people don’t need to be on these medicines.” 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.