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Forever Chemicals Are Uninsurable. Here's What That Means for Business.
This blog was authored by: Roya Alkafaji, Manager, Healthy Communities, and Abhinand Krishnashankar, Manager, Economics and Policy. Photo credit: iStock zoomstudio Every year, new lawsuits reveal the shocking scale of PFAS contamination. These common and long-lasting synthetic “forever” chem
Scientists find a natural sunscreen hidden in hot springs bacteria
Researchers studying cyanobacteria from hot springs in Thailand have discovered a new natural UV-blocking compound with impressive antioxidant power. Unlike conventional sunscreens, it’s biocompatible and potentially safer for both people and the environment. The molecule is produced only under UV and salt stress and uses a unique biosynthetic pathway never seen before. This could help drive a new generation of eco-friendly sunscreens and skincare products.
Forever Chemicals May Not Be So “Forever” After All, Scientists Say - Futura-Sciences
Known as PFAS – per- and polyfluoroalkyl substances – these synthetic molecules have been used since the 1940s for their water-resistant and greaseproof properties. They can be found in clothing, upholstered furniture, nonstick pans, food packaging, and even firefighting foams. The problem? Their carbon-fluorine bond is so stable that it...
Utah health officials issue ‘urgent’ warning about kratom, ‘cat’s claw’ products after overdoses
The Utah health officials are warning against cat's claw herbal supplements that may contain dangerous semi-synthetic kratom chemicals.
Scientists rethink DNA after discovering it can be built from scratch
Scientists show DNA polymerases can build long, patterned DNA without a template, opening new paths for synthetic biology and gene design.
White House, hemp lobbyists bet on shutdown politics to delay ‘diet weed’ ban
Lawmakers say the Trump administration is promising to use the one-month punt to work on new regulations.
FDA's New Playbook: Lots of Media Announcements but Not Much Rulemaking
WASHINGTON (AP) — In one of his first major announcements as health secretary, Robert F. Kennedy Jr. called a news conference to unveil a plan to “phase out” synthetic food dyes. Food and Drug Administration Commissioner Marty Makary opened the event by saying his agency was “removing all ...
MSU-DOE Plant Research Laboratory celebrates 60 years
The Plant Research Laboratory's mission is to study and understand how photosynthetic organisms work on the molecular level, to ultimately develop technologies that improve human lives.
Lucas Museum among L.A.’s boldest, most contradictory buildings
Synthetic yet organic, populist yet elitist, the $1 billion Lucas Museum of Narrative Art is full of contradictions. Highlights include an extraordinary park, cascading waterfall, structural feats and unique vantages. But the galleries leave much to be desired.
Scientists turn DNA into a memory device that uses 100x less power
Researchers combined synthetic DNA with a semiconductor to create an ultra-low-power memory device capable of storing and processing information in the same place. The bio-hybrid technology could eventually help make AI systems and next-generation computers far more energy efficient.
July 2026: Sustainable Business Wins Roundup
Each quarter, Plastic Pollution Coalition is reporting exciting news from companies that are leading the way for a transition away from fossil fuels and synthetic materials to a healthier plastic-free future.
Scientists rethink DNA after discovering it can be built from scratch in a process they call 'doodling'
Scientists show DNA polymerases can build long, patterned DNA without a template called "doodling," opening new paths for synthetic biology.
Scientists are building viruses from scratch to fight superbugs
Researchers from New England Biolabs (NEB®) and Yale University describe the first fully synthetic bacteriophage engineering system for Pseudomonas aeruginosa, an antibiotic-resistant bacterium of global concern, in a new PNAS study. The system is enabled by NEB’s High-Complexity Golden Gate Assembly (HC-GGA) platform. In this method, researchers engineer bacteriophages synthetically using sequence data rather than bacteriophage isolates.
As Food Companies Ditch Artificial Dyes, What Does Science Say About Their Health Effects?
General Mills has removed artificial colors from all of its U.S. cereals, including Lucky Charms and Trix, replacing synthetic dyes with colors derived from fruits, vegetables and spices such as turmeric and paprika. The move comes as food companies face growing pressure from consumers, retailers and policymakers to eliminate artificial dyes. GW public health experts are available to discuss what this shift means for consumers and what we actually know about the health effects of artificial foo
New potent street drug prompts Hawaii health alert
Cychlorphine, a synthetic opioid up to 10 times stronger than fentanyl, has caused deaths across the U.S., prompting local officials to prepare for its arrival.
For The First Time, Scientists Say They've Built a Synthetic Cell From Scratch
A step closer to artificial life?
7 Media Trends That Will Redefine Entertainment In 2026
Learn how AI-driven video, synthetic talent, immersive experiences, and new IP protection technologies will transform the industry and influence global audiences.
Opinion: New Hampshire deserves science on kratom
Jenn Mercier argues against New Hampshire's potential kratom ban, highlighting existing studies and the need for botanical vs synthetic distinction.
Scientists Have Created The World's First Synthetic Cell, First Ancient Human DNA Recovered From Cave Art, And Much More This Week
All the biggest science news stories of the week.
Anabaena Learns A New Trick - Cyanobacteria Surprise Scientists With Evolutionary Shift
Photosynthetic bacteria helped shape Planet Earth.
California bans sale of Kratom products
CALIFORNIA’S DEPARTMENT OF HEALTH IS PUTTING RETAILERS ON NOTICE ABOUT THE SALE OF PRODUCTS WITH KRATOM OR ITS SYNTHETIC VERSIONWHILE LEGISLATORS SEEK TO BAN T
CSDA Program Announces Eight New Data Agreements
NASA’s Commercial Satellite Data Acquisition (CSDA) Program announced eight new agreements with seven of its commercial partners to give users more access to near‑global multispectral and synthetic aperture radar data.
BBC Audio | BBC Inside Science | Should we be scared of AI viruses?
Researchers have announced synthetic viruses designed by artificial intelligence.
Physicists recreate black hole energy extraction in the lab
Researchers have recreated the physics of extracting energy from a spinning black hole using a stationary device that produces synthetic ultrafast rotation. The achievement transforms a long-standing theoretical idea into a practical experiment and could inspire new advances in optics, wireless communications, and quantum science.
Minnesota company is tackling nitrogen pollution with ag science in volatile fertilizer market
Pivot Bio, a company selling farmers a self-sustaining, non-synthetic, bacterial source of nitrogen, aims to lower the nitrogen levels that are the source of significant water pollution in farm states.
Scientists stunned by signs of ancient life in a place no one expected
Scientists exploring ancient seafloor rocks in Morocco discovered mysterious wrinkle patterns where they were never expected to occur. These structures are normally linked to microbial mats in shallow, sunlit waters, yet the rocks formed hundreds of feet below the surface in darkness. Evidence indicates that chemosynthetic microbes created the wrinkles, revealing that deep-ocean microbial ecosystems may have been more widespread than previously thought.
“AI polls” are fake polls
A few weeks after Donald Trump’s second presidential win, I took the train up from London (where I was living at the time) to Oxford to attend a conference on polls and forecasts of the 2024 election. Most of the attendees were pollsters or academics, but I also watched presentations from Aaru and Electric Twin, two companies that do what is interchangeably called synthetic sampling, silicon sampling, or synthetic audiences. Stripped of startup jargon, that means they use large language models (LLMs) to simulate responses to public opinion polls by having AI agents take on the role of survey respondents. I had already heard of Aaru thanks to some articles with eye-catching headlines like “No people, no problem: AI chatbots predict elections better than humans” in the months leading up to Election Day. The founders were making some big, some might even say far-fetched claims, such as: “within two years, we will simulate the entire globe — from the way crops are grown in Ukraine to how that impacts production of oil in Iraq, trade through the strait of Malacca, and elections for the mayor of Baltimore.” When Semafor asked Aaru’s cofounders — Cameron Fink and Ned Koh — about my boss, they said “we respect all those who came before us.” Nate (as he so often does) shared his thoughts on Twitter: Fink and Koh were relatively good-natured about this back-and-forth when we spoke at Oxford. They even offered to mail me one of the t-shirts featuring Nate’s quote they apparently had made. I never took them up on the offer, which I now somewhat regret. These synthetic sampling companies fell off my radar for a while, but they do still exist. In fact, Aaru recently received a $1 billion valuation. Is what they’re doing anywhere close to the most important frontier in AI development? Not by a longshot, especially when Anthropic just developed a model so adept at exploiting software vulnerabilities that it’s only being released to 40 companies. Still, silicon sampling is increasingly finding its way into public polling. Axios reported in March that “a majority of people trust their own doctors and nurses” based on findings from Aaru — without mentioning that the “people” in that sentence were actually LLMs. Around the same time, the Public Sentiment Institute “boosted” their online sample of 373 real survey respondents with 114 AI agents.1 (Spoiler alert: even the co-founder of Electric Twin doesn’t think that’s a particularly defensible approach.) Polling companies like Qualtrics and Ipsos are also developing synthetic data panels. So, what should we make of these … “polls”? Let’s get one thing out of the way: whatever they are, they’re not polls in the way that term is usually defined. Subscribe You can’t replace polls with AI On one hand, using LLMs to essentially make up fake survey respondents sort of sounds like the dumbest idea ever, one that will at best imperfectly replicate real polls while introducing all sorts of biases. On the other hand, with LLMs improving at a remarkable, perhaps even alarming rate, maybe that means I’m a dinosaur at the ripe old age of 24 because I still want to rely on polls that talk to actual people. I’m not going to argue that synthetic samples are completely useless. In fact, as I’ll return to later, there is evidence that some techniques can replicate topline survey results quickly and cheaply. But the marketing from certain companies can be slightly optimistic. “No traditional poll will exist by the time the next general election occurs,” said Fink in 2024. We’re just 206 days away from the midterms, and based on the fact that I still have to collect a bunch of polls every day, I’d say he should have run that prediction by a sample of AI agents before the interview.2 To see why synthetic samples can’t replace polls, here’s a quick primer on how they work. The simplest version of these models involves taking a LLM (like ChatGPT or Claude), giving it a demographic profile (e.g., a white, college-educated woman who lives in Utah and makes $70k a year), and then asking it to respond to a survey question. You repeat that process a few thousand times using different demographic profiles and end up with a sample of synthetic survey responses. The actual models used by private companies are more sophisticated than this, usually because they incorporate more hypothesized demographic characteristics for each agent and provide them with extra information. Aaru, for example, feeds agents a diet of news and information they’d be likely to consume, while Electric Twin incorporates their customers’ proprietary data about the audience they’re trying to replicate. The way Ben Warner, the co-founder of Electric Twin, explained it to me was “we have a large amount of data on […] for instance, 5,000 people. Can we make an accurate prediction of how they would respond to another question?” Still, it should be obvious why synthetic samples can’t replace polls. Polling is fundamentally a data collection process. We might use surveys to make predictions by feeding them into election forecasts, but the main purpose of a poll isn’t prediction, it’s gathering new data about what people think and how they feel. Silicon sampling, on the other hand, produces no new data. It’s simply a model: you input LLM training data, demographic prompts, and a bunch of other information, and it spits out a prediction for what a poll would say. We love models here, but models aren’t polls. That difference is an important philosophical sticking point for most pollsters I talk to. “I think politics should stay away from [synthetic sampling], because we’re trying to […] represent the voice of the people,” said Natalie Jackson, a vice president at GQR Insights. Democratic pollster John Hagner told me: “I think I’m just incredibly skeptical of this idea. I don’t think it’s research. At that point, you’re asking the machine to tell you what you already believe.” Hagner has seen some presentations of early synthetic sampling experiments, but so far, “if it’s being used in a campaign, people are keeping it incredibly quiet.”3 But Eli, I hear you saying, aren’t polls themselves increasingly governed by modeling decisions? Indeed they are: pollsters’ choices on which sampling method to use, how to define their likely voter models, and how to weight their samples can and do lead to dramatic differences in the results they publish. Aaru even referenced these limitations in the methodology statement included with that maternal mortality “poll” — although I’m using the term “methodology statement” loosely here, because it doesn’t really explain how the model works at all. We can ignore the (frankly preposterous) implication that synthetic sampling isn’t subject to a separate set of biases. The important point is that there’s still a meaningful difference between using weighting and other statistical techniques on actual polling data and using a model to predict what a poll would say. The latter is far closer to election forecasts or techniques like MRP — potentially useful models, but not a replacement for polls.4 To be fair, other synthetic sampling companies are perfectly happy with the distinction between polls and models. Warner compared polling and synthetic sampling to different tools in a toolbox. “The mistake I think we make is we think that these new tools should either work in exactly the same way or somehow replace these old tools,” he said. “Rather than thinking of it as, okay, so we’ve always had the hammer, we’ve always had the screwdriver, now we’ve got a saw. But don’t use a saw to try [to] do the job of a hammer.” A quick comment from Nate Eli didn’t ask me for a comment — rather rude of him, don’t you think? But since I’m editing this story, I figured I’d add a few quick thoughts rather than putting words in his mouth. Beyond the frequently misleading marketing, what bothers me about the AI “poll” hype is that as AI tools make statistical inference cheaper and/or better (note that these are not synonyms) that actually increases the comparative value of collecting original data. You might be able to train a model to make a reasonable estimate of what some hard-to-reach poll respondent would say — say, a young Black man who voted for Trump. (Such a person checks a number of boxes for a voter who is usually hard to reach in surveys.) Indeed, this is closely related to what models like the Silver Bulletin forecast already do. They essentially smooth out the kinks in noisy survey data by making inferences based on past voting patterns or national polls or surveys of other states. But you don’t actually know what these voters think unless you’re reaching them directly. If there’s a shift in opinion among this subgroup, you’re not going to detect it. So if I were running a campaign, I’d invest more in going the extra mile to find a representative sample of those voters. And then I’d hire some smart quants — or Claude? — to figure out the implications for campaign strategy based on proprietary data that my competitors didn’t have access to. -Nate Silver Are these models any good? If synthetic surveys are just a new type of model, the next obvious question is whether the models are at least accurate. The answer very much depends on who you ask. On one end of the spectrum, you have the maximalist argument that synthetic sampling is more accurate than actual polls. “It’s an incredibly challenging problem to go to someone and say ‘hey, we’re going to be more accurate at predicting human behavior than you, even when you talk to your customers directly’,” Koh recently told CNBC. In his view, synthetic sampling isn’t a saw to polling’s hammer, it’s “magic.” There’s certainly evidence that synthetic samples can replicate certain survey toplines. But if Aaru does have any examples of their approach outperforming the polls, they’re keeping those to themselves.5 Aaru’s 2024 election model, for example, had Kamala Harris leading in Michigan, Nevada, Pennsylvania, and Wisconsin on November 4th. And although they’ve since taken down their forecast page, they gave Harris a 50.5 percent chance of winning the race on November 2nd.6 After the election, Fink told Semafor he was happy enough with those results because they were “within margin of error,” a term that is completely meaningless when applied to a “sample” of AI agents. And of course, Aaru says their models have improved since 2024, so supposedly now they’d be more accurate than the polls? Still, their stronger argument is on cost: “We are significantly faster and cheaper than traditional polling, and still more accurate,” said Fink. The first two claims are undeniably true, but the third brings us to the opposite end of the spectrum. Both Jackson and Hagner are skeptical that these models are reliable for anything beyond replicating common survey toplines. “I just […] don’t think the machines are what we want when we’re looking for nuanced views. My example on this is people in Arizona and Nevada in 2024 who voted for Trump and voted for expanding abortion in their states on ballot initiatives,” said Jackson. Hagner identified another issue. Maybe the synthetic respondents, like sycophantic LLMs, are inhuman in one important way: they’re too nice. “The reports that have come through at the meetings that I’ve been at are that the early experiments on this, they cannot get respondents to be as racist or sexist or, frankly, as negative as human respondents,” he said. Academic research mostly agrees on this point. While there are some papers that show promising results when using LLMs to replicate polling data, most show that LLMs suffer from various quirks like producing too few “don’t know” responses and can seriously overpredict the favorability of politicians like Donald Trump and Kamala Harris. They also seem to struggle with too little variation between demographic subgroups, so the difference in predicted opinion between Democrats and Republicans, for example, is too small. When I asked Warner about these studies, his response to these papers was that just because academics can’t get synthetic sampling to work doesn’t mean that the technique doesn’t work in general. “Actually, the argument is, okay, yours does not [work]. That does not mean […] for this complex set of machinery, which uses a lot of investment, a lot of time, a lot of money, you can’t get it to work.” Cards on the table, I’m somewhat sympathetic to this argument because academics aren’t exactly great at making election forecasts. Usually, the people with skin in the game are the most accurate. Warner’s argument is that the approach Electric Twin takes — which includes, for example, making multiple predictions for each synthetic respondent using different models and prompts and subsequently averaging those to get a final prediction in a sort of ensemble forecast — produces better results than the simpler academic models. Warner shared a comparison between his method and the method from a recent academic paper with me, and Electric Twin was indeed able to get more accurate replication. But even still, he acknowledged that synthetic sampling “is not a crystal ball.” “If you asked me, do I think using other data sources will be more accurate than asking somebody who they will vote for, I would probably say no. But if you asked me ‘would your system be useful for our turnout modeling today?’ I would say yes.” For better or worse, it looks like the method is already getting more popular in the market research world. Most of the clients Aaru touts these days are businesses like EY and McDonald’s. And AI will probably begin to pop up in other parts of the political polling process. Pollsters are already using it to code open-ended survey responses, and some firms, like YouGov, are testing using LLMs to ask survey respondents questions. More worryingly, one danger to actual polls is that AI agents can be used to infiltrate online surveys. Most online polls use various checks to prevent that from happening, but there’s conflicting evidence on how effective those filters are and how prevalent AI agents currently are in online panels. If those agents ever become impossible to detect, it might spell the end of online polling, but the solution isn’t to replace all of your respondents with ChatGPT. Silver Bulletin is a reader-supported publication. To receive new posts and support our work, consider becoming a subscriber. Subscribe 1 That particular poll obviously doesn’t meet Silver Bulletin standards for aggregation. But we exclude all Public Sentiment Institute polls from our averages because we classify them as an amateur polling firm. 2 You could argue that Fink meant the next presidential election, but (a) I’m also confident we’ll still have real polls in 2028 and (b) in that case he should have asked an LLM to define “general election.” 3 Quick caveat: that’s reporting from a Democratic pollster. It’s possible that Republicans are more willing to use AI in political campaigns. 4 Indeed, Silver Bulletin does not include “polls” produced by MRP in our forecasts or averages, and we think it’s extremely misleading when their practitioners describe them in a way that suggests original data had been collected among a large number of states or Congressional districts. 5 A recent report from Aaru and EY did show two examples of a synthetic estimate being closer than a survey to a real-world benchmark — but I’d take those findings with a grain of salt because the report reads more like an ad and didn’t involve any sort of prediction being made ahead of time. 6 For comparison, our odds for Harris on the same day were 48.2 percent.
Hands-on science can be cool
After spending many long days conducting experiments to find a new way to combat bacterial infections in crops, Misha Hegde and Mia Soleil Maurer have been rewarded with international recognition and access to top research facilities. But these are not typical scientists. They are 16-year-old German high school students.“Our supervisor told us that in order to start a project, you need to begin with either a question or a problem,” says Misha, adding that she and Mia had read about antibiotic resistance in certain plants being treated for bacterial infections. “We found out that antibiotics overuse is very common in agriculture,” explains Mia. “Our aim was to create an alternative to antibiotics and to try to raise awareness of the problem of overusing antibiotics.”Their subsequent research has focused on harnessing bacteriophages, the natural predators of bacteria, to eliminate pathogens threatening food crops treated with antibiotics. At the Schuldorf Bergstraße Jugend forscht MakerLab in Seeheim-Jugenheim, located in the German state of Hesse, they discovered a novel phage that infects the Rhizobium rhizogenes bacterium, which causes the hairy root disease that can reduce yields of tomatoes, carrots, soya beans and other food staples.The students believe their method could curb antibiotic use, while protecting plants from infections without disturbing the natural soil microbiome. “The application of phages is host-specific, so they would only kill the targeted pathogen or disease-causing bacteria,” explains Mia. It may also offer environmental benefits, since applying antibiotics can contaminate groundwater.Getting to this point has not been easy. The childhood friends admit to feeling frustrated when their trials did not yield the expected results. “We spent six months on our first experiment, just doing the same thing over and over and over again, to the point where we thought, is it even worth it? Why are we doing this?” Misha says. “You just need to be brave enough to take that first step and keep going.”The benefits of inquiry-based learningTheir determination has paid off. In 2025, the pair won first prize in biology at Jugend forscht, Germany’s national competition for young people interested in science, technology, engineering and mathematics. They were also among the winners of the German Research Foundation Europa-Preis, which is awarded to selected national winners at Jugend forscht, and took third place in the 2025 European Union Contest for Young Scientists.The Jugend forscht Foundation opens doors for young people to connect with scientists as well as industry professionals. Jessica Bönsch, executive director of the Foundation, sees Misha and Mia as prime examples of how young people can develop a deeper understanding of science through their own research projects. “The experience of working on your own research question is very important,” she explains, adding that “inquiry-based learning engages with real-life problems in a way that can be more motivating than traditional classroom teaching.”The Jugend forscht Foundation offers financial support through sponsor pools in Germany’s federal states. Some projects are developed in cooperation with student research centres, university labs or informal collaborative learning areas called makerspaces. Other activities start as hands-on “garage projects” and can grow within the Jugend forscht network and infrastructure.The Jugend forscht Foundation is also a partner of Science Comes to Town, an EU initiative to strengthen public awareness about science and create bridges between researchers and communities. The EU-financed project involves universities, research institutions and other stakeholders in three host cities — Brest (France), Kiel (Germany) and Split (Croatia).“It's so important for people not to live in a bubble, because I think science can be very scary if you misunderstand it,” Mia says of SCTT. The initiative’s aim of raising awareness about science is “absolutely amazing,” Misha adds. “I think it's the best way to kind of make science digestible for the public. In a world where misinformation in science is getting more radical, more dangerous, becoming more of a threat to the trust in scientific infrastructure and foundations, we need such programmes.”Misha and Mia also believe that SCTT can help break down gender stereotypes in science by encouraging young women to study and pursue careers in scientific fields. Both students have benefited from female mentors and encouragement they had when they were much younger. Mia’s love of science began with a class trip to a lab, while Misha — with a smile on her face — says she wanted to study history until “a teacher kind of coerced me into joining a school science club.”Recognition beyond EuropeIn May, Misha and Mia were among the finalists at the Regeneron International Science and Engineering Fair held in Phoenix, Arizona, and involving nearly 1,700 pre-university students from over 60 countries. “It was very crazy because there were so many people and so many teenagers interested in science,” Mia recalls. The event was also challenging. “It was eight hours of continuously presenting your project,” adds Misha.Attending the fair was a chance for the young women to see first-hand the value of international collaboration as well as how research cultures differ. “America is a bit more willing to take risks, and I think that's something we can learn about here in Europe,” Misha explains. However, she continues, “I think Europe has the talent, has the innovation and has the resources” to be competitive.Misha and Mia plan to continue their phage experiments in the new school year, inspired by the project’s hands-on nature and the support they receive from their supervisors. They have also attracted the interest of professional scientists and received invitations to leading research facilities, including the Centre for Synthetic Microbiology, jointly run by the University of Marburg and the Max Planck Institute for Terrestrial Microbiology in Germany. “It is so cool that we can do something to try to move the world in our school and at this age,” Mia says.After completing secondary school, Mia plans to study microbiology and pursue a career in medical research, focusing on Ebola and other infectious diseases. “This has been my dream for so long,” she says. Her friend Misha is interested in biological research with a focus on medicine. “I've gotten addicted to the process of going to the lab, planning out experiments, then failing, trying again,” Misha says. “I think there's just so much to be discovered, so much to be done.”The students' names and photos were published with the permission of their parents.Science|Business, which connects policymakers, academics and industry through its news reporting, analysis and events, is an SCTT partner.
How synthetic turf and a $5M new HQ are reshaping baseball league owner’s business
The USPBL is looking beyond baseball as it looks to bring in additional revenue.
Scientists covered 200 square metres of a Swiss glacier with sheep’s wool; early tests found it protected the ice about as well as plastic sheets, but only 0.02% of Swiss glacier ice is covered
A natural wool covering placed over 200 square metres of high-altitude Alpine ice has proved as effective at slowing summer melting as conventional synthetic tarpaulins, offering a possible biodegradable alternative to plastic sheets. Initial results from the "Keep It Wool" trial on the Tsanfleuron Glacier in western Switzerland show that prototype wool blankets protected snow and ice from heat about as well as plastic geotextiles.
Health department issues warning after testing finds unexpected opioid in tablet marketed as kratom
The St. Louis County Health Department is warning you to check the labels of products you buy, after lab testing found a synthetic opioid in a tablet marketed as a kratom product.
Does the science of peptides live up to the hype?
Synthetic peptides are getting some serious hype, from fitness influencers on your FYP to people seeking new options for chronic conditions. And while the human body already makes peptides, like insulin and the hormone oxytocin, most of the viral synthetic peptides are not approved by the Food and Drug Administration. Scientists say that raises some big concerns about safety and efficacy. So ahead of this week’s FDA meeting to discuss seven commonly used peptides, we get into the science with health reporter Sarah Boden.Interested in more science behind health trends? Email us your question at shortwave@npr.org.Support public media with NPR+ and enjoy perks for over 25 podcasts like this one. It includes perks like bonus episodes, early access, archive access, curated playlists and sponsor-free listening. Learn more at plus.npr.org.
Methane-infused cow burps meet climate science
A synthetic compound added to cattle feed is shown to reduce methane emissions.
Albany County issuing public health order banning sale of all kratom products
The ban includes both natural leaf kratom and synthetic alkaloid products, according to county leaders.
AI identifies 800 plant proteins for sustainable food emulsifiers
Scientists at the University of Leeds have developed an AI-driven process capable of rapidly identifying plant proteins with emulsifying properties, potentially replacing synthetic and animal-derived alternatives in food and cosmetic applications.
Converting Waste to Black Gold
CALS student Joy Camenisch, a double major in animal science and agroecology, spent the summer in Spain, where she researched how composted manure can replace synthetic fertilizer.
PFAS Pose a Serious Threat to Human Health
Per- and polyfluoroalkyl substances (PFAS) are a large group of synthetic chemicals that were created in the mid20th century and quickly became popular for their water-, grease-, and stain-resistant properties. A growing body of research has linked certain PFAS to various health issues.
This Cell Feeds, Grows and Reproduces. And It’s Manmade.
From chemical building blocks, scientists have created synthetic cells that have most of the hallmarks of life.
What's behind the wellness claims for the synthetic dye methylene blue?
Taking drops of a medical dye is trending in wellness and biohacking circles. Some influencers claim it boosts longevity and brain health. Scientists say the hype has gotten ahead of the research.
Glyphosate: A story of science, risk, and nuance
In the summer of 2014, a California groundskeeper named Dewayne Johnson started noticing patches of raw, weeping skin spreading across his body. He had spent years applying Roundup, Monsanto’s glyphosate-based herbicide, for a school district, sometimes drenched in it when equipment malfunctioned. By 2014, he had a diagnosis: non-Hodgkin’s lymphoma. In 2018, a jury awarded him $289 million in damages, later reduced on appeal. Tens of thousands of lawsuits followed. The story hasn’t quieted down. It’s all over the internet, swinging between “this will kill you, buy organic” and “relax, you’re fear-mongering.” Some of that recent chatter stems from an Executive Order protecting glyphosate and its production, which set off intense infighting within the MAHA movement. Some of it reflects a deeper truth: Americans genuinely care about harmful chemicals in their environment (see poll below). And the conversation isn’t going anywhere, with a Supreme Court case involving Monsanto set for this April. Glyphosate (the active ingredient in Roundup) is one of the most widely used herbicides in the world. But does glyphosate cause cancer? And how much exposure is actually risky? With so much mistrust in institutions and corporations (and some genuine conflicts of interest), making sense of the actual scientific evidence is genuinely hard. Here is a translation of what the scientific evidence actually says. Does glyphosate cause harm? Glyphosate is an herbicide that kills most plants it contacts rather than targeting specific species. It works by blocking an enzyme pathway (EPSPS) that plants and many microorganisms need to produce essential amino acids. Without those amino acids, the plant can’t grow and dies. Glyphosate was first developed by Monsanto in the 1970s and became ubiquitous after the company introduced “Roundup Ready” crops in the 1990s. In 2024, Bayer removed glyphosate from residential Roundup-branded products due to litigation pressure. But today it is still the most widely used herbicide in the world. Farmers also discovered they could spray it directly on crops like oats, lentils, beans, and peas just before harvest to dry them out faster and make harvesting easier. Because of its widespread use, it’s obviously really important to understand if it’s harmful to humans. The key principle: The dose makes the poison The most important concept in toxicology is that “the dose makes the poison.” Sixteenth-century physician Paracelsus articulated what remains the foundational principle of toxicology: sola dosis facit venenum. Water will kill you if you drink enough of it. Aspirin saves lives at 81 milligrams and causes internal bleeding at higher chronic doses. So, at what dose is glyphosate poisonous? The LD50 in rats (the dose that kills half the test animals) is around 5,600 mg/kg body weight. Table salt is roughly 3,000 mg/kg. By that classic measure, glyphosate is less acutely toxic than salt. But some people are exposed to a lot of glyphosate through their work. And among the general public, there are many questions about low-dose, cumulative exposure from multiple sources over long periods. Does glyphosate cause health problems? Cancer Correlation maps are striking, with one analysis finding that 60% of counties that spray the most glyphosate have non-Hodgkin’s lymphoma rates above the national average. But correlation alone doesn’t establish cause, which is why animal and human studies matter. In animals: Rat studies are typically what most fear is rooted in, but most people don’t know the study details on the dosage given to rats. A pooled analysis of the 13 most rigorous rat studies found evidence that very high doses, around 55,000 times greater than typical food residue exposure, can cause certain cancers. In humans: One of the strongest studies followed 55,000 herbicide users over 20 years and found no overall increased cancer risk. There was one signal: among those with the highest use, rates of acute myeloid leukemia (AML) appeared elevated. A broader meta-analysis of 18 human studies also found no overall cancer risk, though a separate meta-analysis focused specifically on non-Hodgkin lymphoma found that people with high cumulative glyphosate exposure faced a 41% higher relative risk compared to those with low or no exposure. These human studies are observational studies (randomized controlled trials would be unethical), and meta-analyses are only as reliable as the studies they pool. But the story seems consistent: at very high exposure levels, there may be some cancer risk. But that’s about as far as the science currently takes us. Emerging research: gut and reproductive health Gut: Because glyphosate targets a pathway that exists in some gut bacteria, scientists have questioned whether it could affect the microbiome. In mice, a 2023 University of Iowa study found that glyphosate altered gut bacterial composition and raised markers of intestinal inflammation. A 2024 systematic review found similar patterns: disrupted gut bacteria and problems with the mucus layer that protects the intestinal wall in mice. Whether this translates to humans is an open question. The human gut is highly variable and resistant, and we don't yet have strong studies confirming the same effects in people. Reproductive health: Animal studies have found that glyphosate-based herbicides can interfere with reproductive hormones, including disrupting egg cell development in mice and reducing the number of eggs available in sheep’s ovaries. However, the doses used in these experiments were high, so it’s not yet clear whether the same effects would occur in humans. On the human side, a 2021 pilot study led by Mount Sinai researchers tested urine samples from pregnant women across the U.S. and found glyphosate in 95% of them. The study also found a possible connection between higher glyphosate exposure during pregnancy and a subtle physical difference measured in female newborns. That said, some connections weren’t statistically reliable and this was a small study. The researchers themselves said more and larger studies are needed before drawing firm conclusions. What do regulators say about glyphosate safety? In 2015, the International Agency for Research on Cancer (IARC), part of the World Health Organization, classified glyphosate as "probably carcinogenic to humans" (Group 2A). The classification is worth taking seriously. But, there’s an important distinction: IARC classifies hazard, meaning the potential to cause harm under some conditions. It does not assess risk, which accounts for actual exposure levels and the real-world probability of harm. The U.S. EPA, the European Food Safety Authority, Health Canada, and regulatory agencies in Australia and Japan have all concluded that glyphosate is “unlikely to be carcinogenic to humans” at realistic exposure levels. They’re asking a different question: given how much of this people actually encounter, what is the probability of harm? Your actual exposure is what determines your actual risk. What does glyphosate mean for your daily life? At the grocery store: Food residues are small. The EPA’s acceptable daily intake is 1.75 mg/kg of body weight per day. To hit that threshold from oatmeal alone, a 150-pound adult would need to eat roughly 50 pounds of oats every day. But this calculation addresses only one pathway (food), and we don’t yet have robust tools to measure people’s actual total exposure. If reducing exposure matters to you, organic certification prohibits synthetic herbicides, and organic oat products generally test at lower or undetectable levels. Either way, a diet rich in whole grains, conventional or organic, is far better for your health than one that omits them out of fear of residues. On the farm: Occupational exposure is orders of magnitude higher than consumer exposure. Farmers and farm workers who mix, load, and apply glyphosate-based herbicides, especially without protective equipment, face meaningfully different risk profiles. Proper PPE isn’t optional. Integrated weed management strategies that rotate chemical modes of action are also worth taking seriously, both for health and for long-term effectiveness. If you live near a farm: Spray drift is real. Concentrations near field edges can spike during and after application, particularly on windy days. This is an area where I think public health could really step up and empower rural communities with buffer zones, rather than placing the onus on individuals. But for now, knowing your local agricultural calendar and keeping windows closed during application are the first steps. Bottom line Dewayne Johnson was exposed to glyphosate at levels most people will never encounter, and a jury decided he deserved compensation. That outcome is not evidence that the oatmeal in your pantry is dangerous. But it is a reminder that the dose, the context, and the person doing the exposing all matter enormously. Glyphosate is a chemical tool with benefits, trade-offs, and genuine open scientific questions. Learning to hold that complexity without collapsing into panic or dismissal is, honestly, one of the most useful and hardest things to do in navigating modern health science. Love, YLE Your Local Epidemiologist (YLE) is founded and operated by Dr. Katelyn Jetelina, MPH PhD—an epidemiologist, wife, and mom of two little girls. Hannah Totte, MPH, is an epidemiologist and YLE Community Manager. YLE reaches more than 425,000 people in over 132 countries with one goal: “Translate” the ever-evolving public health science so that people will be well-equipped to make evidence-based decisions. This newsletter is free to everyone, thanks to the generous support of fellow YLE community members. To support the effort, subscribe or upgrade below: Subscribe
Lab-made DNA can store memory like a flash drive
The new device combines synthetic DNA and a semiconductor to create an efficient memory resistor, or 'memristor.'
NVIDIA’s Synthetic Video Detector Spots Fake News & AI-Generated Content With 92% Accuracy, Analyzing 1080p Footage In Just 22ms
NVIDIA introduces the Synthetic Video Detector NIM microservice, achieving up to 92% accuracy in identifying Fake News.
Virginia Tech scientists turn hard-to-recycle PVC into high-performance synthetic oil
There may also be an economic incentive to pursue the technology.
How Machines and Magnets Taught Science a New Way to Explain the World
A new historical analysis argues that cognitive science and statistical physics independently converged on a shared simulative methodology, the synthetic method, as the only viable path to understanding complex systems.
Scientists unveil ten-year roadmap for building synthetic cells
Scientists from six Asian countries have launched an ambitious 10-year effort to build synthetic cells from non-living molecules, marking the region's first coordinated push to create an artificial single-celled ...
Scientists just created chocolate honey packed with surprising health perks
Scientists in Brazil have transformed cocoa waste into a functional chocolate-infused honey packed with antioxidants and natural stimulants. Using ultrasound waves, they enhanced honey’s ability to pull beneficial compounds from cocoa shells—no synthetic solvents required. The process is considered green and sustainable, and the product could find its way into gourmet foods and cosmetics.
Legislators move to ban AI in political ads, deceptive ads in MA
The bill requires that any audio or video political ad that uses “synthetic media” must contain a message reading “contains content generated by AI.”
American Greens Announces a New Concept in Golf Entertainment
STATESVILLE, N.C. – American Greens, a national leader in golf-related synthetic turf installations, and The 500 Club have announced completion...
Mantis Biotech is making 'digital twins' of humans to help solve medicine's data availability problem
Mantis takes disparate sources of data to make synthetic datasets that can be used to build so-called "digital twins" of the human body, representing anatomy, physiology and behavior.
Synthetic biology promised to rewrite life – with the death of its pioneer, J. Craig Venter, how close are scientists?
Advances in genetic engineering have enabled researchers to seek ways to program new life. But has synthetic biology actually changed medicine and the environment, nearly two decades on?
On the Future of Species by Adrian Woolfson review – are we on the verge of creating synthetic life?
A genomic entrepreneur’s guide to the coming revolution in biology raises troubling questions about ethics and safety
U.S. Department of Health and Human Services (HHS) Recommends Science-Based Thresholds to Target Dangerous Enhanced Kratom Products
Kratom products credit: CDPH DEA takes action based on updated recommendation from HHS October 11, 2026 - WASHINGTON - On Friday, the U.S. Department of Health and Human Services relesed the following: The U.S. Department of Health and Human Services (HHS) provided a new recommendation to the Drug Enforcement Administration (DEA) that establishes science-based thresholds for 7-hydroxymitragynine (7-OH) and mitragynine pseudoindoxyl (MGPI), potent opioid compounds associated with products marketed as kratom. The Department of Justice today announced that DEA issued new Notices of Intent reflecting HHS’ updated scientific analysis and recommendations, including a new Notice of Intent for 7-OH and MGPI incorporating HHS’ recommended concentration and ratio thresholds. Following a review of available scientific data and comments, HHS Assistant Secretary for Health Admiral Brian Christine, M.D., recommended that DEA use concentration or ratio-based thresholds to determine which products containing 7-OH and MGPI are subject to Schedule I control for purposes of temporary scheduling of these substances under the Controlled Substances Act. The recommendation is designed to target concentrated or chemically altered products while distinguishing them from natural botanical kratom products, which may contain trace amounts of 7-OH and MGPI. “We are drawing a clear line between natural kratom and dangerous, enhanced opioid products,” said HHS Secretary Robert F. Kennedy, Jr. “We listened to the public, followed the science, and developed a clear recommended threshold for temporary scheduling that targets concentrated 7-OH and MGPI without sweeping natural botanical kratom into Schedule I. The Trump Administration will continue taking decisive action against dangerous products that fuel addiction while making sure our policies are grounded in rigorous science.” Under the recommendation, 7-OH and MGPI would be controlled in Schedule I for purposes of temporary scheduling when either substance alone, or their combined amount when both are present: Exceeds 1 milligram per gram in solid powders, encapsulated material, or other solid forms, or exceeds 1 milligram per milliliter in liquid preparations; or When the mass of mitragynine present divided by the mass of 7-OH, or MGPI, or the combined amount of 7-OH and MGPI if both present, is less than 100. The ratio-based approach reflects the alkaloid composition of natural kratom products and provides a way to identify products intentionally enhanced or concentrated with 7-OH or MGPI. Available scientific data indicate that 7-OH occurs naturally in botanical kratom at relatively low or trace levels, while some evidence reviewed by FDA suggests MGPI may also occur at trace levels in some natural or kratom-derived products. FDA's scientific review found that 7-OH is substantially more potent than morphine in preclinical studies, with available data indicating approximately two to nine times greater potency depending on the study. FDA also identified evidence supporting a meaningful risk of opioid-like physical dependence and withdrawal from MGPI. “Our recommendation gives DEA a scientifically supportable framework to distinguish natural botanical kratom from products that have been concentrated or manipulated to deliver dangerous levels of potent opioid compounds,” said Assistant Secretary for Health Admiral Brian Christine, M.D. “This approach allows us to focus on the products that pose the greatest threat to public health.” In July 2025, following an FDA scientific and medical evaluation, HHS recommended that 7-OH above a specified threshold be placed in Schedule I of the Controlled Substances Act. In July 2026, DEA announced its intent to temporarily schedule 7-OH above a specified threshold, and HHS simultaneously sought public comment on the proposed threshold. DEA subsequently temporarily placed MGPI and two synthetic 7-OH derivatives, MGM-15 and MGM-16, in Schedule I. Since then, questions have emerged about how scheduling could affect natural botanical kratom products potentially containing trace amounts of MGPI. The new HHS recommendation responds to those concerns by providing DEA with a more precise threshold based on both the concentration of 7-OH and MGPI and their relationship to mitragynine, the predominant alkaloid in kratom. HHS and FDA will continue working with DEA to protect Americans from dangerous concentrated and synthetic opioid products while ensuring federal policy is grounded in the best available science. Source: HHS
City of Independence bans synthetic 7-OH, regulates kratom sold in businesses
The City of Independence voted to ban synthetic 7-OH and regulate its parent product, kratom, at Wednesday night's city council meeting.
Twisted graphene reveals a hidden superconductivity switch
Scientists have uncovered a surprising new way to control superconductivity — the mysterious phenomenon where electricity flows with zero energy loss. By pairing twisted layers of graphene with a synthetic diamond material, researchers were able to effectively switch superconductivity on and off by tweaking how electrons interact with their surroundings. Even more intriguing, the material behaved in ways that defied the rules of conventional superconductors, hinting at an entirely new kind of physics.
‘Beautiful blobs’: synthetic life a step closer as scientists make cells using lab-made DNA
Tiny, quivering spheres designed to feed and multiply raise prospect of artificial organisms to make drugs, food and fuel
All known life uses 4 DNA letters; UC San Diego showed an existing bacterial enzyme can accurately read and transcribe 8, doubling the genetic alphabet with synthetic letters
All life on Earth, from bacteria to blue whales, writes its genetic instructions using the same four-letter alphabet. These four DNA bases, usually referred to as A, T, C and G, pair up in predictable ways to store and pass on the information needed for life.
Scientists devise new method for tracing environmental PFAS contamination better
Perfluoroalkyl and polyfluoroalkyl substances (PFAS) are a class of synthetic chemicals widely used in industrial processes and consumer products because of their resistance to heat, water and oil. However, ...
Multi-omics insights into floral–fruity aroma formation during Pu’er tea fermentation inoculated with a synthetic fungal community
npj Science of Food - Multi-omics insights into floral–fruity aroma formation during Pu’er tea fermentation inoculated with a synthetic fungal community
Did scientists just create synthetic life?
The SpudCell certainly resembles a living cell, but a key structure inside the cell falls short of the real thing
Synthetic chemicals in food system creating health burden of $2.2tn a year, report finds
Scientists issue urgent warning about chemicals, found to cause cancer and infertility as well as harming environment
A grad student’s wild idea sparks a major aging breakthrough
A casual conversation between graduate students helped spark a breakthrough in aging research at Mayo Clinic. Researchers discovered that tiny synthetic DNA molecules called aptamers can selectively attach to senescent “zombie cells,” which are linked to aging, cancer, and neurodegenerative disease. The method could eventually help scientists identify and target these cells in living tissue with far greater precision.
Synthetic everything is warping America
It's poisoning the signals people rely on to understand reality.
Cal Poly offers first synthetic cell course to students, partnering with network of innovative scientists
A new course offered this spring quarter at Cal Poly introduced upper-division undergraduates to the fundamentals of building synthetic cells while collaborating on innovative projects. The cutting-edge course titled “Engineering Synthetic Cells,” taught by Javin Oza, a Cal Poly biochemistry associate professor, involves a partnership with the San Francisco-based company b.next, an open-source synthetic cell platform Nucleus, and an international science network.
More Than a Third of All New Podcasts Are AI-Generated
Synthetic podcasts just keep coming.
Rice graduate students showcase innovative research at synthetic biology science cafe
The Science and Technology Policy Program at Rice’s Baker Institute for Public Policy hosted a “science cafe” Oct. 13 to bring research to reality and answer questions from the public, giving attendees a look at projects underway in university laboratories and an opportunity to ask questions about the science, its potential applications and its implications for society.
Custom blood vessel grafts in minutes? Harvard engineers share vision of the future
Researchers have developed a new way to craft custom synthetic vascular grafts for patients in need of immediate care. The technique has shown early potential to be a game-changer for cardiovascular care.
How the Internet Became the ‘Cookbook’ of the Drug Trade
A baffling overdose death took investigators to the frontier of ultra-potent synthetic drugs. The clues were hauntingly familiar.
Scientists say they have built a cell from scratch for the first time
Scientists say they have built a cell from scratch for the first time that feeds, grows and replicates like a natural cell, a breakthrough in synthetic biology.
'Milestone': Scientists claim to build synthetic cell, raising concerns in step toward artificial life
University of Minnesota researchers say "SpudCell" is the most life-like synthetic cell yet, able to grow, divide and pass traits to offspring.
Scientists React to the Lab-Made, Yet Lifelike, SpudCell
This cell-like structure can grow, feed, divide and compete. Researchers ponder what it means for the future of synthetic biology and our definition of “life.”
Scientists got mouse eyes to perform photosynthesis — and no, they didn't turn green
Special eye drops containing photosynthetic machinery from spinach leaves have helped combat dry eye, a new mouse experiment reveals.
Stem cell scientists engineer 'synthetic organizer' cells to improve kidney organoids
In a study published in Science, USC researchers paired a biological discovery with an engineering feat to create more faithful, reproducible lab-grown kidney structures from stem cells, known as organoids.
The science of synthetics: How a Saint Michael’s grad is changing the way the world views lab-created DNA
When Noah Robinson, Ph.D., graduated from Saint Michael’s College in May of 2021, he left campus with much more than a diploma – he left with the skills
DEA Highlights the Integral Role of Forensic Science in Addressing the Synthetic Drug Crisis
As synthetic drugs continue to evolve and threaten American lives, DEA chemists and forensic science professionals play an essential role in helping the U.S. Drug Enforcement Administration identify emerging threats, connect evidence to investigations, and provide timely information that can save lives. From analyzing suspected fentanyl and other novel synthetic drugs to examining fingerprints and…
Six of the biggest health news stories today
High-end infant formula scrutiny, the spread of synthetic opioids, and more health news from Morning Rounds
Actors\' union approves 4-year contract with studios and streamers
Television and movie actors have voted to ratify a four-year contract with studios and streaming services. This comes after union leaders negotiated protections against synthetic actors created by artificial intelligence.
Scientists turn one of the world's most hated plastics into premium lubricant
Polyvinyl chloride, or PVC, is among the most common plastics on the planet but also one of the hardest to recycle responsibly. In a new study in Nature, scientists describe a method to strip chlorine from PVC and convert the remaining material into synthetic lubricant-based oils that perform competitively with commercial products.
Sundar Pichai: Google’s Frontier Imperative, Compute Scarcity Dilemmas, Future AI Products and Businesses
Alphabet CEO Sundar Pichai has little patience for doubts about Google’s willingness or capacity to compete at the frontier of AI. His company’s latest Gemini 4 Argon model — long-promised and recently delivered — beats OpenAI and Anthropic’s latest public models on coding, math, science, and knowledge-work benchmarks and has placed Google’s AI back among the world’s best. Amid chatter that Google was fine with good-enough lightweight ‘Flash’ models, Pichai pushed back hard against any suggestion that company is limiting its research ambitions. “We won’t do it any other way,” he told me in a Big Technology Podcast interview recorded this week at Google’s Mountain View headquarters. Pursuing cutting-edge research is costly for Google, requiring compute and chips that otherwise could be sold at a high markup by its cloud business. But Pichai said allocating resources to frontier AI development is “ground zero” for Google. “Our cloud business is at any given moment in time forgoing short-term revenues because we are making compute trade-offs to support frontier AI development,” Pichai told me. In our conversation, we discussed Argon (which Pichai says processed three times as many tokens as Gemini 3 in the 72 hours after launch), AI’s role in developing better models, the AI interface of the future, competition with OpenAI, Google’s partnerships with Anthropic and Apple, data centers in space, and the nature of his optimism. You can read the full conversation, lightly edited for length and clarity, below, or listen to it on Apple Podcasts, Spotify, YouTube, or your podcast app of choice. Alex Kantrowitz: Google is back at the frontier with Gemini 4 Argon, which beats OpenAI’s and Anthropic’s public models on benchmarks spanning coding, math, science, and knowledge work. The conventional wisdom was that Google had given up on the frontier to focus on fast, good-enough models for its products. That was wrong? Sundar Pichai: Very wrong. Look, we are so committed to being at the frontier. It’s in our DNA. We won’t do it any other way. We’ve been thinking about the AI frontier for well over a decade now. The frontier is very dynamic. It’s very competitive. It’s very exciting. I couldn’t be more pleased with how Argon has come together. We took our time with it. We made sure we tested it safely and deployed it carefully. We took a bit longer than our other models, given its capabilities. Across the board, be it for agentic work, knowledge work, coding, creative tasks, Argon is one of the really good frontier models. The first 72 hours since we launched, it’s almost up to 25 trillion tokens used, which is about three times what Gemini 3.0 had in the first 72 hours. And we are capacity constrained as we are serving it. Excited, but there are always areas….. You know, the frontier is very dynamic, and you have to keep evolving the models. There are areas which we can see we need to improve as well. We’ll be at work on all of that as well. Which areas? On any given model, Argon would be across the board better at everything if you just look even two months back. But the space is so evolving. Even on coding, for example, there are pockets where we know we need to do better, and there’s clear feedback. We’ll make it better. The argument for standard models is that they’re already good enough to build great products, with the frontier valuable only for certain tasks. Meta built Muse with good models rather than the best, and Google could put standard models into Maps, Gmail, and Search. Why isn’t that sufficient? It’s definitely not how I have thought about it at all. We are deeply committed to driving the state of the art and actually pushing where this technology can go, all the way from transformers. That’s a journey we’ve been on. You will see us continue to do that. When you’re at the frontier, you get an earlier look into the emerging capabilities, earlier than others, which helps you better prepare for the future. We are fortunate to have a wide set of opportunities across consumer and enterprise. Some of our cloud customers, for sure, want the model at the frontier. The frontier models will be better at developing the next set of models in an era of recursive self-improvement. I would argue it matters there. Internally, we have definitely seen a steep point of inflection by deploying Argon to our developers. It definitely tackles problems which our previous models could not do. Being at the frontier puts you at the innovation frontier and helps you drive that as well. There is room for models of all sizes and capabilities. That’s why we did Flash, Flash-Lite, etc. I agree with you that you can develop very good product experiences with not necessarily the most frontier model, because they tend to be slower and more expensive too. But we care about the Pareto frontier, and we’re committed to doing all of that. You said developing frontier models gives you a look at what’s coming. What do you see on the horizon? What makes this moment so exciting is you build something which you think is a culmination of months of work, and you put it out. You already see clear headroom ahead. We’ve all been working on these agentic, long-horizon capabilities, but clearly the frontier is going to have multiple of these agents working together to do things. That’s an example of an exciting paradigm underway. It’s also clear to me that users, both in consumer and enterprise, will have access to some multi-agent framework for them, working all the time doing things. It feels like a moment of infinite possibilities. Sergey Brin said one of Argon’s core innovations was relying more on the model to help develop the model. Yet incidents involving OpenAI’s bots breaking containment and Google’s bots accessing a site they shouldn’t have suggest these systems still aren’t perfectly aligned. Isn’t there reasonable cause for concern about accelerating toward recursive self-improvement? There are three parts to your question. Let’s first talk about RSI itself. You’re right, Sergey mentioned RSI being a critical contributor as we are developing Argon. But to be very clear, today, in a continuum, we are pretty far from the end state of RSI or something like that. The right way to describe it is you have RSI loops along with AI researchers in various aspects of model development. You almost have agents helping AI researchers in some automated way work together to build these models. It’s good to at least understand where we are today, and it’s super exciting. I expect that continuum to keep making progress as we go into next year. The second question is around safety. Security comes from defense in depth. You’re talking about alignment, obviously. We all are doing a lot of work to make sure the models are as aligned as possible. But it’s an active area of research, and there are clear gaps. That’s not the only thing we rely on. You have the work you do on alignment, but beyond that, we put a lot of effort into real-time monitoring as these models are working. Making sure you have good telemetry into what’s happening so that you can intervene in real time if you see unexpected behaviors. How you do that, the architecture with which you do that, how you make sure it’s all sandboxed and isolated in a way that it can continue doing its job during real-time monitoring — these are all areas of investment. Obviously, when we deploy these models, giving customers, enterprises, control, both with real-time monitoring, but their ability to decide where they deploy, what type of data which models ever have access to, etc. All that work is very, very important, and we are doing a lot of that. As an industry, we need to constantly up our game and take the time to do all this stuff, including dedicating the compute required to make sure you can safely deploy these models. The third part is: Is it all enough? This is why there are a lot of conversations in the industry, and I think it’s reasonable to be worried. The right way to approach something you worry about is early on thinking about what more we can do. As an industry, you’re definitely seeing increased levels of work on alignment, testing for safety, etc., as we deploy these models. The next step would be developing better standards so there are cross-industry standards of how we do these things, including the ability to evaluate using independent evaluators. If you look at areas like financial controls, we have accounting standards, we have independent auditors. Those are all the next natural steps for us as an industry to work toward, and regulation will end up playing a role as we go through all of that as well. Google pursues both language models and world models, while some other AI companies like OpenAI have focused more heavily on LLMs as the path to AGI. Why is world-model research important to you? As humans, you obviously perceive the world around you, and you’re taking all those inputs. Making sure these models are able to understand the laws of physics, simulate the world in high fidelity. All that, I think, are key enabling blocks for intelligence. We are working across all possible applications of this. Take an area like robotics. It’s not an accident: If you look at embodied reasoning benchmarks, Argon is state of the art. That all comes from our experience building world models. Inherently, I would expect there will be architectures which will converge all of this in the future. But for us, we view this as, today, this jagged frontier that is intelligence. We want to be at the frontier of all the possible approaches, and that will help us both drive better research, better insights, and develop a much better AGI model, is how we think about it. Some people argue Google should focus on selling its computing resources rather than using them for AI research. Cloud wants to sell compute at a high markup, while researchers need a very big computer to build the best models. How do you balance those demands? Ground zero for us is making sure we are investing in the compute that’s needed to be at the AI frontier. In 2015, I set the company to be AI-first. AI is that key horizontal ingredient across everything we do, all the way from Search to Cloud to Waymo. That is ground zero for us, making sure both for research and developing the models that we have enough compute needed. Beyond that, we have these independent businesses which each have their own long-range plans. They’re using those long-range plans to project forward, and we plan for compute for all of them. The cloud team, they have a business and they have a projection and they have demand signals. They have committed contracts. With that help, we can plan on a long-term compute trajectory for them. We’re fortunate to have this wide set of opportunities. There’s no company in the world which isn’t making trade-offs at any given moment in time. But the first principles we operate on is making sure we have enough compute to invest in the frontier, and that’s non-negotiable, and everything else works on top. Remember, we are building our own compute, our own data centers. There are long lead times. The compute we have today is a function of decisions I made in 2024. Even if you think back to a year from today, last year the agentic coding takeoff hadn’t happened. Today, obviously, when you sit and look at anything, everyone is short—not just us, other companies—and everyone wishes you had two times what you had planned for. That comes with the territory. At any given moment in time, we are planning for what we would need two to three years out in the future. You’re going to find some decisions you make may look suboptimal two years out, depending on where the state of the art is. I don’t spend too much time thinking about it. The way I think about compute allocation is making sure Google DeepMind, both for research and frontier model development, is cutting edge in terms of compute access. After that, we plan for all our businesses. It’s great we can provide our compute to other people too, like we have done. It allows us to scale that up, get real tests for it in the widest application set possible, test it in the real world, and make sure we are at the cutting edge of technology at every layer of our AI stack as well. Ben Horowitz relayed on a podcast that Andy Jassy had said compute was sold out until 2028. Are you seeing the same thing? If today a customer is coming out and asking us for compute in ’27, that is incredibly difficult to do. Even last year we went out and leased some short-term compute to accommodate some customers and help them out. But yes, we are all dealing with similar timelines. Every major technology shift seems to involve overbuilding, from railroads to fiber. Do you think that’s happening here? It depends on the time frames you’re looking at. This level of build, if I take a long enough time frame, it makes complete sense to me. We are at early stages of the most consequential technology ever, and this is not like fiber. It’s not dark. We are using it. We are capacity constrained on a daily basis. The applications, the underlying technology is improving rapidly. But running a company, the way I work through the moment is you look at your demand signals. You look at your committed revenue opportunities that you’re getting in the form of contracts. You balance that with the underlying technical capability, or internally you balance how we are using it internally, the model trajectories. Based on all that, you take a disciplined approach—a confident, aggressive, but disciplined approach—to plan out compute. But when you talk about the broader economy as a whole, you’re having people operate with various levels of risk. I focus on what I can control as a company and plan through it. If your question is, can there be an overbuild? Yes, because you can have some constrained components. Remember, what matters is a set of everything you need to build out that compute. Memory, electricity, electricians…. Land. Like 2,000 components or more. You could get constrained at some point. There could be things which cause bottlenecks, and then you could have overbuilds. There could be external shocks to the economy. There are macro risks. All that is possible at any given time. But am I confident that the compute we are planning for, we will have uses for it given enough time? Absolutely, yes. I was in the room when you said AI would be as profound as fire and electricity. If you believe that, shouldn’t you give researchers the biggest possible computer, even at the expense of today’s cloud profits? We’re all doing some form of that. Our cloud business is at any given moment in time forgoing short-term revenues because we are making compute trade-offs to support frontier AI development. What you’re seeing is true already. But you want to be thoughtful. Even our AI researchers have some constraints, which are also good. That’s what inspires creativity. That’s what helps you come up with a more efficient algorithmic breakthrough, maybe to use less memory. What we are committed to is to invest what’s needed to stay at the frontier. OpenAI President Greg Brockman told me a few months ago that the ideal AI product might have no interface at all—just as natural as conversation. What do you think the interface looks like in the age of AI? Computing historically has been humans adapting to computing. With AI, it’ll be the other way around. The technology will adapt seamlessly to how humans live and go about their day-to-day life. You want to be able to speak to that AI when you want to. It has context. It can be persistently running somewhere, give you continuity. You’re not dealing with a lot of complexity. It’s natural. All that makes sense to me. That doesn’t mean it won’t have any interface, however. Nowadays, even on my phone, Gemini voice models are so good. I can just press the microphone button. I can just ramble. That’s why we call it Rambler. I can ramble. It intelligently corrects with my personal context. Even my friends’ names, weird names, it gets it right, even if it first guesses it wrong. It’s amazing. I’m not going in, backspacing, editing once I finish saying it. It’s remarkable how good it’s gotten. But there are going to be moments you will need an interface. You may want to entertain yourself. You may want to watch a video. You may ask your AI to create something, and you want to watch it, consume it. The way I think about it is, you want it to be as seamless and as natural as possible, and that you are not doing the hard work adapting to whatever the interface is. The technology is adapting to how you’re working at any given moment in time. When I asked Sam Altman about competition from Google, he argued that OpenAI was building AI-native products and implied Google was bolting AI onto existing ones. Do you accept that framing, and can a “bolt-on” compete with an AI-native product? First of all, it’s a very dynamic moment. We are all pushing each other. OpenAI has pushed us as a company too. This is not just we are doing something to them. Far from it. Yes, Google went into its own version of a code red too. In the AI moment, all of us have to respond with varying levels of urgency at any given moment in time. We are constantly building new products as well. Gemini was built from the ground up as a product—the Gemini app I’m talking about. Or tomorrow, as all of us are adapting to agents, are you bolting agents onto ChatGPT, or are they building it natively? How would you think about that? You can always put these questions back. I don’t think of it that way. I think of it as you want to build world-class product experiences that are AI-native and they are winning. Some of it is adapting products, some of it is rethinking new experiences. They will all be part of how you approach this moment. How do you expect to make money from these AI interfaces? If an agent recommends a product and I buy it, should it take a cut of the sale, even if that’s disclosed? I’m going to put enterprise aside. Consumers are going to increasingly value this technology. Take software developers today. Developers are willing to pay for these coding models because it really helps make them more productive. Nobody would want to code the way they were coding three years ago, or even the same time last year. When you’re delivering value, there’s value to be had. I’ve never seen examples where consumer products really scale to billions of users, where people are using it intensely, and haven’t had monetization to go along with it. If you’re providing value to people, there is inherent value to be had. The nature of it may change. You’re asking a scenario in which agents are doing stuff for you. At the end of the day, you will have commercial needs. The agents need to fulfill those commercial needs. The providers on the other side are going to see value in that. There’s going to be some mechanism by which it gets fleshed out. That’s what an efficient system does. But what matters is that you’re building those world-class consumer experiences. I promised myself one product feature request: Can you make Gemini-powered search in Gmail as good as searching Gmail through ChatGPT? What’s going on here? One of the exciting things we announced recently is the unified Gemini agent for enterprise, and we’re bringing that to consumers. These are persistent agents which run 24/7 in the cloud. We’re spinning up a micro VM effectively for every query, and from the runtime, the harness, the model—it’s all a headless service. It’ll work everywhere, and it has your context with you. You can address this agent whether you’re in Workspace, in Search, in Gemini. Agents are going to drive this unification from a customer experience, which people are looking for. When you have a broad product portfolio and you’re moving forward fast, there are always areas where you’re moving forward faster than some other areas. That’s valid feedback. But we are putting a lot of time and effort into making sure, as we build out our agentic experience, that unification and that capability works well across all these services. People have asked where Google’s equivalent of Muse or Dots is. Do you anticipate creating a new product to compete with them? Our goal with Gemini is to be a personal, proactive, and powerful assistant. That’s what we are working toward. You saw us describe the early versions of it at Google I/O this year with Spark. Effectively, already, if you open the Gemini app as a Pro or an Ultra user, you see Chat and Task, and they start giving you that agentic experience. With Argon now, that capability is dramatically accelerated. What we announced recently in our enterprise event, we are bringing that to consumers as well. Soon in Gemini, you will be able to ask something, and it’s a continuum. You don’t need to think about whether you’re asking a chat or an agentic task. It’ll just automatically do the right thing, and it can work persistently in the background securely and get things done for you. Already, empirically, when I look at what’s coming into Gemini in our app, last year you would have tagged them and most of them would have been chats. But a steadily growing percentage is already agentic tasks in Gemini. Yes, we are going to bring personal agents to our consumers. It’ll work in the Gemini app. It’ll work in Google Search. It’ll work in Gmail. You can address it, call it, etc. We want to make sure we’re getting it right. It’ll be powered by Argon, and we are very excited to bring it to our users. Google has invested and committed tens of billions of dollars to Anthropic. What do you see in Dario Amodei that makes you want such a deep investment and partnership? Very early on, when we looked at the founding team of Anthropic, it was very clear to us this was a world-class team which was involved in some of the earliest GPT models. We were quickly able to assess that they would be at the frontier. We’ve always tried to identify and partner with companies which would be at the cutting edge of technology. We’ve done that with SpaceX. We’ve done that with Anthropic. That’s what we saw, and we had a chance to partner. Is your partnership with Apple to license Gemini for Siri expanding, contracting, or standing still? For both us and Apple, that partnership was about translating the capabilities of AI into better experiences for users. I thought Apple has done a phenomenal job of incorporating—they customize these models and build it into Siri—and it’s been great to see the reception. We are committed to doing more. You’re going to do more with them? There’s nothing specific to comment, but it’s a partnership. It’s working well. What have you learned so far about the feasibility of data centers in space? It’s a very exciting project. Projects like this remind me—I still recall that moment in the desert for the DARPA Grand Challenge, watching the Stanley car compete in it, which ended up being the basis for Waymo. With all these technologies, you’re at a moment in time where it looks a bit like sci-fi, but 10 to 20 years down the line, it’s actually working in a meaningful way. I feel the same in space. We’ve always believed in betting big on these technologies. Quantum has been a great example of it. We’re probably 10 years into the journey. Waymo, we are almost 20 years into the journey. Space, we are two to three years into the journey. It’s clear to me that it’s an approach which will make a lot of sense in the future. Being in sub-synchronous orbits with 24/7 access to the most abundant form of energy we have access to will make a lot of sense for this exponential increase in compute that we are going to need. Having said that, this is a challenging project. The team has disciplined milestones. It was a super exciting milestone to send the TPUs up to space. Sometime, as we are speaking, we are expecting a “hello world” moment from them. I look forward to seeing that happen, and we’ll continue investing here too. You previously said 25% of the code written within Google was written by AI. What’s the number today? That number in our world today is meaningless in the sense that every part of what we now touch is influenced by AI deeply. What we are tracking with Argon, for example, it’s been a point of inflection. People are using Argon to really go tackle complex projects, including projects which affect millions of lines of code. In the four weeks since we have launched Argon, we have seen a 30% increase in CLs, and more importantly, almost a similar increase—a one-third decrease in rollbacks. People are writing code more productively and better code with it. We are tracking it with newer metrics now. How are you dealing with opposition to data centers? If you can’t build in the U.S. the way you want to, where else do you go? We’re very committed to building our data centers very responsibly. When I look at our largest data centers in some of the communities where we have been the longest, I visit them, I talk to people. There are people who come and tell us their kids have gone to college because of our investments in these communities, and we’ve grown in those communities. To me, this is about doing it right. Engaging with the communities early, doing it transparently. We are committed to making sure we are bringing incremental power, and we cover 100% of the cost of our power needs for these projects. It’s understanding the impact on neighborhoods and making sure you’re working thoroughly to address them. Done rightly, you can continue making progress. But we are constantly taking feedback into account. I do worry not everyone operates the same way. People have been surprised by data centers showing up in their backyard. Those are all important. As an industry, we should continue to do better. But part of the reason, in a 20-year time frame, we are thinking about things like data centers in space is making sure we are able to put these data centers where maybe the trade-offs will be different. What unproven area of AI is Google working hardest to crack? Is it continual learning—getting models to keep updating after release—or something else? There are multiple parallel paths. You saw with Argon we have a million output tokens. With our Gemini models early on, we had a one-million context window on the input side. These are all examples of breakthroughs we are constantly working on. How do you think about synthetic data? How do you think about continual learning? How do you think about more efficient architectures? We do dedicate a meaningful percentage of our compute to these research bets too. It always goes hand in hand, including our world models. Those are all bets we are placing. Sometimes, as part of these bets, there are ideas that emerge which just make their way into the mainline models. Maybe we don’t fully talk about it, but we have multiple steps in these areas. Working in technology requires optimism that building something will improve the world, but those bets can leave real consequences when they fail. I recently returned to a town in El Salvador where enthusiasm for Bitcoin had given way to unfinished construction and something resembling a ghost town. How do you maintain the optimism you spoke about in your Stanford commencement address while accounting for those risks? I genuinely have a deep conviction that improving technology and improving access to technology makes people’s lives better. It’s been my personal experience in my life, out of which comes that deep conviction, optimism. But with that, I’ve always felt humanity’s journey is always about working hard to harness that technology to make it work well for society. The optimism is not an optimism that you just build whatever you want, whenever. You work hard to make sure you’re building technology in the service of humanity, to make the human condition better. That’s got to be the defining goal as we are approaching it. With that framework, I’m very, very optimistic. We are in very early stages of the kind of progress that we are going to see on many areas. We’re all talking about areas like healthcare. A lot of the progress is ahead. The ability to make things in a fundamentally cheaper way on many things where things have been getting expensive for a long, long time, discovering new materials, etc., which really bend the curve—it’s tough to step today and understand the impact of all that, but they’re going to be very profound. I’m very optimistic, but we have to be rightfully concerned about all the ways this technology can go wrong. Sundar, I know you don’t do many of these. Thank you. Thanks, Alex. Appreciate it.
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