2026-09-29 01:03:16
This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here.
Last Wednesday, Anthropic announced that earlier this year it had launched a molecular biology lab, where Claude agents read and conjecture about hard biology problems and human scientists run experiments on what they report. And this AI-powered lab, the company said, had made its first discovery.
To understand what Anthropic says its system did, imagine you’re flipping through a library of millions of DNA sequences, amassed as scientists sequence more and more of the living world. One step toward a breakthrough might be finding a peculiar sequence that encodes an interesting enzyme, perhaps. Then you’d need to figure out what that enzyme does and, eventually, how to manipulate it to do something useful.
What Anthropic says its system of 950 agents found after 21 hours was not a brand-new sequence. The agents instead flagged a repeating pattern surrounding a known enzyme, a particular pattern Anthropic said hadn’t been catalogued before. But if you read through Anthropic’s announcement, which calls this pattern “reminiscent” of what led to the gene-editing technology CRISPR that “has already transformed science and medicine,” it sounds as if this army of agents really found something of note.
These claims have angered some biologists. A viral post from one, subsequently endorsed by the chair and CEO of the drugmaker Eli Lilly, said that “finding a weird cluster of genes and repeats is often the easy part. The hard part, and where the real discoveries come from, is figuring out what the system actually does.” The agents helped with some laboratory grunt work, in other words. But a discovery it is not.
It’s a reminder that even if AI does something impressive—like finding a pattern in a mass of biological data that would be difficult to perceive with human eyes alone—the result itself may not constitute a breakthrough for science. What is novel for AI may be routine, unsurprising, or simply not that consequential to a biologist.
Muddying the issue further, Mario Rodríguez Mestre, a biologist at the University of Copenhagen, said over the weekend that his team had already discovered this particular pattern, the New York Times reported. Mestre, who regularly chatted with Claude in his work, wondered whether Anthropic’s team had learned from his conversations. Anthropic denies this, but Mestre says he’s stopping all use of Claude anyway.
Part of the problem here is that AI companies aren’t presenting their systems simply as tools scientists can use, like microscopes or supercomputers. They’re insisting that the AI systems are making discoveries themselves. To some, that approach is incompatible with how science actually works, with new knowledge more typically emerging from collaboration and an ever-growing arsenal of tools.
It’s also making people more skeptical of genuine progress when it happens. Whittling 200,000 candidates down to a few worth exploring is no small feat; it is legitimate scientific work. The fact that a general-purpose chatbot could do that work is notable, even if humans helped steer it and ultimately ran the experiments. But once the standard is whether Claude itself made a discovery, all that becomes evidence for one side or the other in a debate that has only two answers: breakthrough or bust.
Once we’re judging AI by whether it has made a discovery, it’s also tempting to shift the goalposts even after it really does seem to notch a win. Earlier this month, OpenAI said its own team agents had cracked a million-dollar problem in mathematics. But a couple of weeks later, nearly every AI skeptic in my feed was sharing an article asking whether it was the math problem that really mattered.
To be clear, the piece did not argue that OpenAI’s solution was wrong. Instead, it argued that the particular result may not be the one mathematicians care most about. Throw in the accusation by a mathematician that the models may have used some of his work without credit, and people are left thinking either OpenAI cheated or the solution wasn’t important anyway. Or both.
That’s part of what concerns Lucas Harrington, the biologist who wrote the post critiquing Anthropic’s announcement. He closed with a suggestion: AI companies, he said, should “set the bar high now, so that when an AI actually discovers a fundamentally new biological mechanism, everyone appreciates how big a deal it is.” But as OpenAI’s Sam Altman and Anthropic’s Dario Amodei race to one-up each other, raising the bar for scientific breakthroughs by AI might be the last thing on their minds.
2026-09-28 20:10:00
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.
Over the past few months, a cascade of cyberattacks by AI agents has stunned the world. In July, OpenAI disclosed that a swarm of its agents had escaped their sandbox and hacked into the AI platform Hugging Face to cheat on a cybersecurity test.
Many experts say it’s only a matter of time until there’s a more damaging incident where AI agents bypass sandboxes to access systems they shouldn’t.
But the big question is: How do we hold companies liable when they lose control of their AI agents?
—Michelle Kim
Separating AI reality from hyped-up fiction isn’t always easy. That’s why we’ve created the AI Hype Index—a simple, at-a-glance summary of what’s shaping the industry right now.
The latest edition includes Chinese chipmakers, German wiki sites, and American Terminators. See where it all landed on this month’s index.
—Michelle Kim
Last week, we published an MIT Technology Review investigation that found more than a thousand people died within the advertised range of surveillance towers along the US border.
Later today, our editor-in-chief Mat Honan, senior AI reporter James O’Donnell and senior reporter for features and investigations Eileen Guo will join a subscriber-only conversation about the investigation, and its implications.
Register now to attend on Monday, September 28 at 7:00pm BST / 2:00pm EDT / 11:00am PDT.
Want to join the conversation? Subscribe to MIT Technology Review for exclusive access to all our Roundtables.
Read the full MIT Technology Review investigation here.
The must-reads
I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology
1 OpenAI says it has paused training its models
The decision comes after its agents interacted with US government websites. (AP)
+ Trump hosted Anthropic boss Dario Amodei at a White House dinner last night. (FT)
+ How much access will evaluators really have inside AI companies? (Atlantic)
+ Why can’t we just keep rogue AI off the internet? (Verge)
+ Democrats are in a mad scrabble to get AI “right”. (New York Magazine)
+ An AI kill switch isn’t that simple, after all. (Bloomberg)
+ Will AI really kill us all? Your questions, answered. (MIT Technology Review)
2 Why isn’t the data center backlash also a climate reckoning?
It’s still hard to get people to care about the emissions they create. (Wired)
+ What does the climate movement do now? (Atlantic)
+ The math behind data centers and energy. (MIT Technology Review)
3 Wall Street big bears aren’t ready to bet against AI, yet
Bubble talk is growing, but not many are willing to go against the crowd. (Information $)
+ What even is the AI bubble? (MIT Technology Review)
4 Did Anthropic’s AI really make a scientific discovery on its own?
One scientist shared research with Claude, and says the new finding matches. Hmm. (NYT)
+ AI for science needs reasoning, not just data. (MIT Technology Review)
5 Ancient superbugs might help us fight antibiotic resistance
They could be emerging from the permafrost. (New Scientist)
6 More reliable—and less easy to jam—alternatives to GPS are coming
Using Earth’s quantum field could be more secure. (Economist)
7 Why social media bans aren’t enough to keep children safe
Online child safety deserves more nuanced policy. (IEEE Spectrum, Opinion)
8 This is how the US is attacking China’s control of critical minerals
It’s a multibillion-dollar effort to loosen Beijing’s chokehold. And it’s working. (WSJ)
9 No one wants to date tech bros anymore
They used to be nerdy and harmless, now they’ve got a real image problem. (Wired)
10 What are the rules around cellphone etiquette now?
Is there an obligation to text someone right back, for instance? (Vox)
Quote of the day
“If you’re evil, you’re at least competent. And if you’re evil, you’re not bad, and therefore you’re actually maybe kind of good because you’re at least getting something done.”
—Venture capitalist and PayPal cofounder Peter Thiel shares his unusual take on competence versus morality in an interview with Axel Springer CEO Mathias Döpfner.
The gig workers who are training humanoid robots at home
When Zeus, a medical student in Nigeria, returns to his apartment from a long day at the hospital, he straps his iPhone to his forehead and records himself doing chores.
Zeus is a data recorder for Micro1, which sells the data he collects to robotics firms. As these companies race to build humanoids, videos from workers like Zeus have become the hottest new way to train them.
Micro1 has hired thousands of them in more than 50 countries, including India, Nigeria, and Argentina. The jobs pay well locally, but raise thorny questions around privacy and informed consent. The work can be challenging—and weird. Read the full story.
——Michelle Kim
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.)
+ This surfboard comes with a 10-liter beer keg built right into it.
+ Hollywood’s iconic Cinerama dome movie theatre is set to reopen at long last in early 2028.
+ From Dracula to Marilyn Monroe, here are 25 famous quotes in pop culture you’ve probably been getting wrong.
+ Baby lobsters in mini pods and a human-sized floating waterlily highlight this stunning selection of science photos.
2026-09-28 16:06:22
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.
Over the past few months, a cascade of cyberattacks by AI agents has stunned the world. In July, OpenAI disclosed that a swarm of its agents had escaped their sandbox and hacked into the AI platform Hugging Face to cheat on a cybersecurity test. Recently, external researchers discovered that OpenAI agents had hijacked a German wiki site and the coding platform RubyGems in May to share test answers.
Earlier this month, Anthropic disclosed four incidents in which its model Claude hacked into third-party systems during cybersecurity exercises. Just last week, Google confirmed that its model Gemini had been caught hacking other companies too.
The researcher who uncovered the OpenAI website hijack has warned it’s likely that similar undiscovered episodes are out there. And many say it’s only a matter of time until there’s another, possibly more damaging incident where AI agents bypass sandboxes to access systems they shouldn’t.
So the big question is: How do we hold companies liable when they lose control of their AI agents?
OpenAI didn’t disclose the German wiki incident or the RubyGems incident until a group of external researchers uncovered them, and it still has not disclosed some crucial details about the Hugging Face hack. That limits our understanding of what exactly went wrong and how to prevent it from happening again.
But you might be surprised to learn that OpenAI likely wasn’t legally required to disclose these incidents. (OpenAI did not respond to a request for comment.)
State AI transparency laws like California’s SB 53, New York’s RAISE Act, and Illinois’s SB 315 require that AI developers report “critical safety incidents.” These are defined as incidents that cause more than 50 deaths or physical injuries or $1 billion in damage. They also include incidents where the model deceives developers outside an evaluation in a way that materially increases catastrophic risks. Many cybersecurity incidents that don’t meet the threshold for physical damage or catastrophic risks could nonetheless be dangerous precursors to such catastrophes, and the existing laws don’t account for that.
“The recent incidents are a perfect example of why the law isn’t ready,” says Mackenzie Arnold, managing director of US policy at the Institute for Law and AI, a think tank. “Only the worst, most egregious, most immediately harmful stuff is going to qualify.”
With no authority under existing AI laws to demand information about anything short of a catastrophe, governments are left to borrow investigative authority from other laws or sue the companies, an expensive process that can take years.
“Normally, something like the Hugging Face incident should have been taken to court,” says Yonathan Arbel, a law professor at the University of Alabama School of Law. “Then we would have discovery, and we would have all the spillover effects that we get from litigation, where all the information comes out.”
But so far, Hugging Face has chosen not to sue OpenAI. Hugging Face’s CEO, Clément Delangue, says it doesn’t have the resources to do so (instead, he asked OpenAI for $100 million in compute). Still, Delangue stressed in an interview with CNN at the end of July that choosing not to pursue legal action shouldn’t be taken to mean he doesn’t think OpenAI should be held accountable. “Everyone has to remember that this cyberattack is a crime. This is illegal. And we have to find a way to make sure these things don’t happen more regularly,” he said. Hugging Face did not respond to a request to comment.
Litigation has the benefit of pushing courts to use existing laws to address AI safety incidents, rather than just waiting for new legislation. One obvious route is tort law, a body of civil law that lets people and businesses sue those who harm them. This is often used to hold companies liable for the mass harms they cause, like when families sued Boeing in 2019 over two plane crashes that killed hundreds of people, or when states and cities sued Purdue Pharma over the opioid crises, extracting settlements worth billions.
“There’s plausible grounds for a negligence claim that OpenAI should have used a stronger sandbox, done more monitoring,” says Gabriel Weil, a law professor at the University of Houston Law Center. For example, when OpenAI employees discovered the covert message board that the agents had created, they could’ve promptly escalated their findings to security and safety teams. And the company could’ve better designed its sandbox to ensure that agents couldn’t access the internet.
But even if OpenAI doesn’t end up in a lawsuit over the Hugging Face hack, the threat of liability could incentivize AI labs to exercise more caution than explicitly demanded by law.
OpenAI announced in its postmortem that it plans to strengthen the safeguards used to contain and monitor the models, accelerate model alignment, and improve its processes for identifying and addressing incidents.
“The liability questions raised by frontier labs’ spate of cybersecurity attacks boil down to the incentives the expectation of liability creates for their future conduct,” says Weil. “That’s why I think it’s important to get these rules right, even if the stakes are pretty low in this particular case.”
One way to get answers—and determine whether OpenAI should be held liable—is to compel disclosure. But the existing state AI laws—California’s SB 53, New York’s RAISE Act, and Illinois’s 315—don’t give governments the authority to investigate incidents like the ones that happened recently.
However, amid rising public alarm, state attorneys general are stepping in, borrowing investigative powers from other laws. Alabama, Montana and a coalition of 15 other states, and California are each demanding information about the incident from OpenAI to understand whether the company’s practices violated state consumer protection laws, among others. Members of Congress are also launching their own probes. Senator Josh Hawley opened a Senate investigation earlier this month, sending OpenAI a list of questions about the incident and the company’s internal policies together with a document request, while a group of House Democrats asked OpenAI and Anthropic to release their incident logs.
“Someone needs to investigate, but it’s unfortunate that it has fallen to attorneys general, who need to rely on creative interpretations of their existing authorities to do this,” says Arnold, the US AI policy expert. Consumer protection statutes were written to catch companies that scam their customers, not companies that lose control of their software. The state attorneys general would have to show that OpenAI deceived or unfairly harmed customers, but it’s unclear if the hacking involved any such conduct.
And “those [consumer protection] laws are not built for doing a thorough investigation of an AI cybersecurity incident,” says Arnold. They weren’t designed to help investigators determine whether a model was adequately contained or whether a company’s security practices were sound.
“This is not the right tool for the job,” says Arbel. “The right tool would have been something like maybe a criminal investigation”—perhaps under a hacking law like the Computer Fraud and Abuse Act (CFAA).
Under CFAA, hacking into another company’s computer systems without permission is a crime. But to be held liable, a hacker must have intended to break into a computer without authorization. Intent arguably requires a state of mind, and no court has ruled that AI agents have one. Without such a precedent, it’s unlikely a court would rule that AI agents had carried out a hack.
One way to keep an eye on AI companies is to mandate external auditors.
After the Hugging Face hack, OpenAI brought in researchers from the AI safety nonprofits METR and Redwood Research to examine the incident. However, it constrained access to the model that led to the hacks, didn’t disclose the company’s safety and security practices, limited the length of the investigation, and had ultimate say over what the researchers could publish. We still don’t know what set the attack in motion back in May and why OpenAI’s employees who spotted the agents’ activity never escalated to their safety and security leaders.
This kind of arrangement has a built-in tension: An auditor without legal authority depends on the labs’ goodwill for continued access, which means it has to scrutinize the labs without jeopardizing their relationship. Last week, Anthropic announced that the company will be hiring Accenture as an embedded evaluator to assess its models. Anthropic CEO Dario Amodei wrote in an essay that frontier AI labs should give “ongoing employee-like access” to “a team of embedded third-party evaluators (such as METR), whose role is to verify adherence to safety practices and commitments, report incidents, and help assess the alignment of not just completed AI models but training pipelines and processes.”
Most existing state AI laws do not require labs to hire an external auditor. California’s SB 53 and New York’s RAISE Act just require AI companies to publish a safety framework describing how they will test their models for dangerous capabilities and then to follow it. The frameworks are written by the companies, and testing can be done internally. Only Illinois’s SB 315 requires companies to undergo an annual third-party audit starting in 2028.
“There’s a lot of headroom for increasing not only reporting requirements for these companies, but also review by external bodies,” says Peter Salib, a law professor at the University of Houston Law Center. Those reviewers could be private auditors accredited by the government but chosen and paid for by the AI companies. Alternatively, they could be government agencies or insurance companies.
None of this is an accident. The laws on the books that failed to hold AI companies accountable for agentic cyberattacks emerged amid fierce lobbying by the AI industry.
SB 1047, the California AI bill that was vetoed by Governor Gavin Newsom in 2024 after lobbying by OpenAI, Meta, Anthropic, and the venture capital firm Andreessen Horowitz, proposed a much tougher set of rules. It would have required AI companies to report a broader set of safety incidents (including incidents in which a model acts on its own or slips its controls), undergo annual third-party audits, and maintain a kill switch. But after a year of intense negotiations, Newsom signed SB 53, which narrowed the types of incidents deemed reportable and dropped the requirements for audits and kill switches.
New York’s RAISE Act followed the same arc. “The version of the RAISE Act that the NY Legislature passed would have required disclosure of this ‘incident,’” Alex Bores, the New York state assembly member who sponsored the bill, wrote on X. New York’s original bill also included third-party audits.
With political pressure mounting, new bills creating better reporting, auditing, and liability regimes for AI development are on the horizon. In Congress, the AI Incident Reporting Act would require AI companies to report to the Commerce Department when a model evades human oversight or breaches a system, even if it doesn’t cause any harm. The Frontier Act would require incident reporting and independent audits. In New York, the Understanding Artificial Intelligence Act, sponsored by Bores, would make companies liable when a model does something that if carried out by a human would be a tort or crime.
As AI agents increasingly become better at launching cyberattacks, the law remains behind. Closing the gap will require lawmakers to move faster than the next breakout.
2026-09-25 20:10:00
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.
The US government wants to spend $30.3 million over the next five years on an improved lie detector, according to a Department of Defense budget request.
The program, called “Polygraph+” or “Polygraph Next,” will focus on scoring algorithms that use AI and machine learning, as well as a technique called “standoff sensing,” which can take physiological readings from a person without attaching a device to them.
The project aims to improve the accuracy and reliability of polygraph assessments. But it could just be the latest in a long line of failed attempts to use technology to detect lies.
—Amit Katwala
Around this time last year, a hot mic caught Vladimir Putin and Xi Jinping discussing the possibility of living forever. “With the developments of biotechnology, human organs can be continuously transplanted, and people can live younger and younger, and even achieve immortality,” Putin reportedly said.
He seemed to be referring to the “replacement” theory of longevity, supported by experiments that involved physically stitching young mice to old ones. Unfortunately for Putin, new research on transplanted hearts pours a little cold water on this idea. But it also offers useful insights for organ transplantation.
Find out what the research reveals about the limits of rejuvenation.
—Jessica Hamzelou
This story is from The Checkup, our weekly biotech newsletter. Sign up to receive it in your inbox every Thursday.
The US has spent billions building a “virtual wall” of surveillance towers along its southern border, promising to detect and apprehend border crossers and save lives. But an MIT Technology Review investigation found more than a thousand people died within the advertised range of the towers without getting caught.
On Monday September 28, our editor-in-chief Mat Honan, senior AI reporter James O’Donnell and senior reporter for features and investigations Eileen Guo will join a subscriber-only conversation about the investigation. They’ll examine the failures of border surveillance technology and uncover the stories of the people who die in the borderlands.
Register now to attend on Monday, September 28 at 7:00pm BST / 2:00pm EDT / 11:00am PDT.
Want to join the conversation? Subscribe to MIT Technology Review for exclusive access to all our Roundtables.
Read the full MIT Technology Review investigation here.
The must-reads
I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.
1 ChatGPT helped the Tumbler Ridge shooter focus on attack tactics
A new investigation found it also advised on evading safeguards. (Mother Jones)
+ British Columbia is suing OpenAI over its failure to alert police. (BBC)
+ And wants OpenAI to pay for a replacement school. (Ars Technica)
+ Do AI chatbots cause delusions or amplify them? (MIT Technology Review)
2 Ukraine just used drones to drop military robots behind Russian lines
The “world-first” assault sent the devices into enemy territory. (Ars Technica)
+ The robots can attack, scout and clear routes for troops. (Business Insider)
+ Europe has a drone-filled vision for future wars. (MIT Technology Review)
3 Google is about to launch AI chips into space
The satellite will run simple AI queries from orbit. (Reuters $)
+ The project is part of plans to put AI data centers in space. (Gizmodo)
+ Here’s how we could put data centers in space. (MIT Technology Review)
4 A new DNA test can diagnose brain tumors in hours
The technology analyzes a tumor’s DNA to identify its type. (BBC)
5 Tesla’s Semi is finally ready to hit the road
The long-delayed electric truck is reaching customers this week.(Verge)
+ It could be a big deal for electric trucking. (MIT Technology Review)
6 Meta has gained an early lead over OpenAI in the AI device market
The Muse Charm is expected to ship before OpenAI’s hardware. (CNBC)
+ Meta promises to put privacy at the center of the products. (Axios)
7 A mathematician has discovered a rare eight-faced shape
He argues that the strange three-holed object can exist in 3D. (New Scientist $)
8 AI agents are flooding researchers with collaboration requests
Some bots are asking for data, money, and research partnerships. (Nature)
9 Venus may have swallowed its own moon
The planet’s slow rotation could have dragged the moon to its doom. (Wired $)
10 Mark Zuckerberg has finally explained his fashion glow-up
His makeover is intertwined with Meta’s wearables push. (NYT $)
Quote of the day
—Jensen Huang compares AI’s need for fossil fuels to surgery in an interview on The Ezra Klein Show.
One more thing
L. Stephen Coles’s brain sits in a vat at a storage facility in Arizona. It has been held there at a temperature of around −146°C for more than a decade, largely undisturbed. Before he died in 2014, Coles had the brain frozen with an ambitious goal in mind: reanimation.
His friend, cryobiologist Greg Fahy, believes it could be revived one day. But other experts are less optimistic.
Still, Fahy’s research could lead to new ways to study the brain. And using cryopreservation for organ transplantation is becoming a viable reality.
Read the full story to find out what the future holds for the technology.
—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.)
+ A French village recently hosted its annual pig-squealing championship.
+ Things get deliciously unattractive at the Iowa State Fair’s annual ugliest cake contest.
+ Greater Victoria is home to more than 1,000 Little Free Libraries, each with its own personality.
+ A study found that seeing someone dressed as Batman nearly doubled the rate of people giving up their seat to a pregnant woman.
2026-09-25 17:16:25
The US government wants to spend $30.3 million over the next five years on an improved form of lie detector, according to a Department of Defense budget request. The program, called Polygraph+ or Polygraph Next, will focus on scoring algorithms that use artificial intelligence and machine learning and on a technique called “standoff sensing,” which refers to the ability to take physiological readings without attaching a device to a subject’s person.
According to details of the budget document, which were first reported by Inside Defense, the project will “modernize federal polygraph and credibility assessment technologies” to improve their accuracy and reliability. But the project may be just the latest in a long line of failed attempts to use technology to detect lies. “It’s a misguided effort to reduce the complex to something that is tangible,” says Kyri Kotsoglou, a legal scholar at Northumbria University in the UK who studies the use of polygraphs in the justice system.
The move comes at a time of high tension within the department. Under Defense Secretary Pete Hegseth, the Pentagon has been increasingly turning to polygraph tests in an attempt to find the sources of alleged leaks to the press. In September, the New York Times reported that around 50 officers on the Joint Staff had been given polygraph tests after news coverage reported on the depletion of US weapons stockpiles in the war with Iran.
Polygraph+ will be run by the Defense Counterintelligence and Security Agency (DCSA), which conducts background checks for the federal government. According to the budget document, which has not yet been approved by Congress, the new technology will be used for vetting of prospective employees and “insider threat detection.” It is not yet clear which specific technologies will be used, and the DCSA did not respond to a request for more information.
But other Pentagon efforts offer potential clues. In 2023, the department’s Defense Innovation Unit (DIU) ran an open submission process to find companies with products that could be used for deception detection.
It selected two companies to build prototypes: Presage Technologies, which claims to be able to measure heart rate and breathing rate using standard cameras, and Altec Research, a medical sensor company now branching out into non-contact sensing technologies. A screenshot of Altec’s prototype technology released by the DIU shows that it tracks head movement, facial skin temperature, and pore activity. Presage Technologies and Altec Research did not respond to requests for comment. The DIU declined to comment.
Current lie detection technology has barely changed since the polygraph was invented in the 1920s. Examiners rely on blood pressure, pulse, breathing, and sweat measurements to determine if someone is lying. They make judgments about the veracity of respondents’ replies on the basis of differences in their physiological response to baseline questions like “Is the sky blue?” and target questions like “Have you ever committed a crime?”
The federal government conducts tens of thousands of the tests each year while screening employees, but the reliability of this technology has been repeatedly challenged—and its results are rarely admissible in court. In 1983, Congress’s Office of Technology Assessment concluded that there was very limited evidence supporting the polygraph’s use for screening employees, and in 2003, the US National Research Council (NRC) said evidence for its efficacy was “weak at best.”
Research suggests humans can spot a lie just over half the time without any technical assistance. The American Polygraph Association claims the polygraph is between 80% and 94% accurate. But the 2003 NRC report pointed out that a screening test with this level of accuracy could still lead to a lot of mistakes. The DOD employs 2.8 million people; an imperfect system applied at that scale could end up falsely accusing tens of thousands.
There are other issues too. Polygraph interpretations are often subjective: Different examiners get wildly different results, and people from minority groups are more likely to be judged as deceptive. What’s more, with training it’s possible for interviewees to learn a variety of countermeasures that can help beat the test; for example, they may artificially heighten their physiological response to baseline questions by stepping on a pin hidden in their shoe.
“If you know how it works, you can beat it,” says Sophie van der Zee, an associate professor who studies deception at Erasmus University in Rotterdam. She says the machine’s biggest effect is deterrence—often, subjects confess before it even begins. “But that only works if people think a polygraph works,” she points out.
Various new strategies for lie detection have been attempted over the decades, with technologies ranging from thermal cameras to pupil trackers to brain scans. None of them have yielded reliable results outside the lab. The problem is that there’s no single telltale sign of lying that’s true for everyone all the time. “There is still no Pinocchio’s nose,” says van der Zee.
AI could theoretically improve polygraphs if it could find patterns in the data that examiners can’t. AI algorithms are also more likely to be used for “multi-modal” deception detection, which seeks to combine multiple measurements into an overall deception “score” that is harder for people to game. Three things are happening under the surface that lie detection tries to home in on, van der Zee says: physiological stress, cognitive load, and the conscious efforts people make to conceal the fact that they’re lying. Current polygraph technology tackles only one.
“The more you can have combined methods that approach it from these three different angles, the more successful you will be,” van der Zee says. This isn’t a new concept—in the 2000s, researchers at Manchester Metropolitan University in the UK developed a system called Silent Talker that generated a deception score from video footage. This was later folded into iBorderCtrl, an EU-funded pilot program. In the US, a project called AVATAR incorporated eye tracking, voice analysis, and body movement detection into a tool for use at border crossings. All these projects have quietly faded away.
Kotsoglou says combining AI and the polygraph is “the worst of both worlds” because it adds uncertainty on top of invalidity. Even if AI or machine learning can spot previously unseen patterns in physiological data, it won’t be able to reliably link them to lying because there is no real ground truth.
“Even if you have all the records in the world from polygraph tests, you don’t know whether those polygraph tests are right or not,” says Marion Oswald, a professor of law who has written with Kotsoglou on the use of polygraphs in the justice system. She fears that new forms of lie detection will, like the polygraph, be used more as a psychological prop than a scientific tool.
“It seems very much a response to the concern of the current administration to leaks and perceived lack of loyalty,” says Oswald. “[Lie detection is] being used as a threat, to intimidate and force people to confess to things, as opposed to anything that’s actually getting valid information.”
2026-09-25 17:00:00
Around this time last year I was attending an aging conference in Manchester, listening to a talk about fly aging, when my phone started pinging. News outlets were reporting that a hot mic had caught Russia’s and China’s leaders discussing the possibility of living forever.
“With the developments of biotechnology, human organs can be continuously transplanted, and people can live younger and younger, and even achieve immortality,” Russia’s Vladimir Putin reportedly told China’s Xi Jinping.
He seems to have been referring to the “replacement” theory of longevity, which has been supported by multiple experiments that involved physically stitching young mice to old ones. Something about the young blood rejuvenated the old mice. Perhaps young organs could rejuvenate world leaders in their 70s, too.
Unfortunately for Putin, new research pours a little cold water on this idea. Studies on transplanted hearts in both mice and humans suggest that new hearts soon adopt the biological age of the recipient, no matter how young they were to begin with. The finding could be important for transplantation, but it also highlights just how complex aging—and rejuvenation—are.
Jesse Poganik at Brigham and Women’s Hospital in Boston is one of the many scientists trying to understand exactly what it is about the bodies of young mice that rejuvenates old ones. Plenty of research has focused on seeking the secrets of youth in young blood. But what if it’s something about young organs instead?
To find out, he and his colleagues performed a set of heart transplants in mice. In humans, heart transplants typically involve removing a damaged or injured heart and replacing it with another from a donor who is usually much younger than the recipient. (When Poganik assessed hospital records, he found that most recipients were about 20 years older than their donors.)
The mouse transplants were different: Mice received a second heart, implanted in the neck—a procedure that’s slightly simpler and allows scientists to compare the new hearts with the existing ones. In some cases, young adult mice were given a heart from a middle-aged donor. In others, middle-aged mice got young hearts.
The team used a trio of “aging clocks”—molecular tools used to estimate the biological ages of tissues and whole organisms—to assess whether the additional hearts affected the mice in any way. These clocks were good at predicting the chronological age of mice that didn’t get new hearts.
Poganik says he was expecting to see a reciprocal effect, and that young hearts might benefit older animals, for example. In previous work by other members of his team, young mice that got old hearts experienced a buildup of senescent cells in their other organs. These cells are thought to contribute to the aging process, suggesting that receiving an old organ might prematurely age an animal.
But that’s not what he found. When he and his colleagues analyzed the transplanted hearts between four and six months after surgery, they found that the hearts seemed to have adopted the biological age of the recipients. Young hearts got older, and old hearts got younger. “The environment of the transplanted organ really dictates how it seems to behave biologically,” he says. The findings were published online at bioRxiv last week.
The team also looked at each mouse’s blood and other organs—including its original heart—and were surprised to find that they seemed to be unaffected by the presence of the new heart, despite the age of its donor.
The finding was backed up by data from human heart transplants. People who receive a donor heart must typically undergo a series of heart biopsies after surgery. The tiny pieces of heart tissue collected from people who had heart transplants at Brigham and Women’s have been stored for decades. And when Poganik and his colleagues tested their biological ages with the aging clocks, they found a similar pattern: No matter the age of the donor, a new heart quickly adopts the biological age of the person who received it.
It’s not clear why this is, but João Pedro de Magalhães, who studies aging at the University of Birmingham in the UK and was not involved in the study, thinks it might have something to do with the recipient’s immune system. Perhaps immune cells circulating in the blood might affect markers of aging in the new organ, he says.
Perhaps the potential rejuvenating effects of a young heart end up being diluted by all the other aged components of an older body, Poganik suggests. Or maybe a single organ just isn’t enough to see an effect.
Poganik hopes his finding will encourage surgeons to consider using hearts from older donors, many of which are discarded on the assumption they won’t function as well. (Machines used to preserve organs before transplantation are changing that already—and Poganik has worked on another study showing that these devices seem to rejuvenate donor livers to some extent.)
But the study also highlights just how complicated aging is. If organs seem to be getting older by one measure but not by another, how can we get a full picture of the biological age of an organ, or a person?
This complexity means that scientists are not likely to discover a true way to completely reverse aging, says Poganik. “That would mean that every aspect of aging has to go back in time,” he says. Reversing DNA damage, structural damage, and all the other degradations that are part of the aging package, across all our various cells and tissues, presents an enormous challenge.
“There are aspects of biological age that are probably reversible, and there are aspects that are probably not,” he says. Sorry, Putin.
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.
