For the last couple of years, I’ve been following the progress of a group of individuals who believe death is humanity’s “core problem.” Put simply, they say death is wrong—for everyone. They’ve even said it’s morally wrong. They established what they consider a new philosophy, and they called it Vitalism. Vitalism is more than a philosophy, though—it’s a movement for hardcore longevity enthusiasts who want to make real progress in finding treatments that slow or reverse aging. Not just through scientific advances, but by persuading influential people to support their movement, and by changing laws and policies to open up access to experimental drugs. And they’re starting to make progress.
Vitalism was founded by Adam Gries and Nathan Cheng—two men who united over their shared desire to find ways to extend human lifespan. I first saw Cheng speak back in 2023, at Zuzalu, a pop-up city in Montenegro for people who were interested in life extension and some other technologies. (It was an interesting experience—you can read more about it here.) Zuzalu was where Gries and Cheng officially launched Vitalism. But I’ve been closely following the longevity scene since 2022. That journey took me to Switzerland, Honduras, and a compound in Berkeley, California, where like-minded longevity enthusiasts shared their dreams of life extension.
It also took me to Washington, DC, where, last year, supporters of lifespan extension presented politicians including Mehmet Oz, who currently leads the Centers for Medicare & Medicaid Services, with their case for changes to laws and policies. The journey has been fascinating, and at times weird and even surreal. I’ve heard biohacking stories that ended with smoking legs. I’ve been told about a multi-partner relationship that might be made possible through the cryopreservation—and subsequent reanimation—of a man and the multiple wives he’s had throughout his life. I’ve had people tell me to my face that they consider themselves eugenicists, and that they believe that parents should select IVF embryos for their propensity for a long life. I’ve seen people draw blood during dinner in an upscale hotel restaurant to test their biological age. I’ve heard wild plans to preserve human consciousness and resurrect it in machines. Others have told me their plans to inject men’s penises with multiple doses of an experimental gene therapy in order to treat erectile dysfunction and ultimately achieve “radical longevity.” I’ve been shouted at and threatened with legal action. I’ve received barefoot hugs. One interviewee told me I needed Botox. It’s been a ride. My reporting has also made me realize that the current interest in longevity reaches beyond social media influencers and wellness centers. Longevity clinics are growing in number, and there’s been a glut of documentaries about living longer or even forever. At the same time, powerful people who influence state laws, giant federal funding budgets, and even national health policy are prioritizing the search for treatments that slow or reverse aging. The longevity community was thrilled when longtime supporter Jim O’Neill was made deputy secretary of health and human services last year. Other members of Trump’s administration, including Oz, have spoken about longevity too. “It seems that now there is the most pro-longevity administration in American history,” Gries told me. I recently spoke to Alicia Jackson, the new director of ARPA-H. The agency, established in 2022 under Joe Biden’s presidency, funds “breakthrough” biomedical research. And it appears to have a new focus on longevity. Jackson previously founded and led Evernow, a company focused on “health and longevity for every woman.” “There’s a lot of interesting technologies, but they all kind of come back to the same thing: Could we extend life years?” she told me over a Zoom call a few weeks ago. She added that her agency had “incredible support” from “the very top of HHS.” I asked if she was referring to Jim O’Neill. “Yeah,” she said. She wouldn’t go into the specifics.

Gries is right: There is a lot of support for advances in longevity treatments, and some of it is coming from influential people in positions of power. Perhaps the field really is poised for a breakthrough. And that’s what makes this field so fascinating to cover. Despite the occasional weirdness. 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.

We’re expanding our risk domains and refining our risk assessment process.AI breakthroughs are transforming our everyday lives, from advancing mathematics, biology and astronomy to realizing the potential of personalized education. As we build increasingly powerful AI models, we’re committed to responsibly developing our technologies and taking an evidence-based approach to staying ahead of emerging risks.Today, we’re publishing the third iteration of our Frontier Safety Framework (FSF) — our most comprehensive approach yet to identifying and mitigating severe risks from advanced AI models.This update builds upon our ongoing collaborations with experts across industry, academia and government. We’ve also incorporated lessons learned from implementing previous versions and evolving best practices in frontier AI safety.Key updates to the FrameworkAddressing the risks of harmful manipulationWith this update, we’re introducing a Critical Capability Level (CCL)* focused on harmful manipulation — specifically, AI models with powerful manipulative capabilities that could be misused to systematically and substantially change beliefs and behaviors in identified high stakes contexts over the course of interactions with the model, reasonably resulting in additional expected harm at severe scale.This addition builds on and operationalizes research we’ve done to identify and evaluate mechanisms that drive manipulation from generative AI. Going forward, we’ll continue to invest in this domain to better understand and measure the risks associated with harmful manipulation.Adapting our approach to misalignment risksWe’ve also expanded our Framework to address potential future scenarios where misaligned AI models might interfere with operators’ ability to direct, modify or shut down their operations.While our previous version of the Framework included an exploratory approach centered on instrumental reasoning CCLs (i.e., warning levels specific to when an AI model starts to think deceptively), with this update we now provide further protocols for our machine learning research and development CCLs focused on models that could accelerate AI research and development to potentially destabilizing levels.In addition to the misuse risks arising from these capabilities, there are also misalignment risks stemming from a model’s potential for undirected action at these capability levels, and the likely integration of such models into AI development and deployment processes.To address risks posed by CCLs, we conduct safety case reviews prior to external launches when relevant CCLs are reached. This involves performing detailed analyses demonstrating how risks have been reduced to manageable levels. For advanced machine learning research and development CCLs, large-scale internal deployments can also pose risk, so we are now expanding this approach to include such deployments.Sharpening our risk assessment processOur Framework is designed to address risks in proportion to their severity. We’ve sharpened our CCL definitions specifically to identify the critical threats that warrant the most rigorous governance and mitigation strategies. We continue to apply safety and security mitigations before specific CCL thresholds are reached and as part of our standard model development approach.Lastly, in this update, we go into more detail about our risk assessment process. Building on our core early-warning evaluations, we describe how we conduct holistic assessments that include systematic risk identification, comprehensive analyses of model capabilities and explicit determinations of risk acceptability.Advancing our commitment to frontier safetyThis latest update to our Frontier Safety Framework represents our continued commitment to taking a scientific and evidence-based approach to tracking and staying ahead of AI risks as capabilities advance toward AGI. By expanding our risk domains and strengthening our risk assessment processes, we aim to ensure that transformative AI benefits humanity, while minimizing potential harms.Our Framework will continue evolving based on new research, stakeholder input and lessons from implementation. We remain committed to working collaboratively across industry, academia and government.The path to beneficial AGI requires not just technical breakthroughs, but also robust frameworks to mitigate risks along the way. We hope that our updated Frontier Safety Framework contributes meaningfully to this collective effort.

We thank the International Collegiate Programming Contest (ICPC) for their support.This project was a large-scale collaboration, and its success is due to the combined efforts of many individuals and teams. Hanzhao (Maggie) Lin led the overall technical direction for Gemini competitive programming and ICPC 2025 efforts, and co-led with Heng-Tze Cheng on the overall research and execution.The leads and key contributors of the ICPC 2025 team are the following: Chenkai Kuang, Yuan Liu, Zhaoqi Leng, Jieming Mao, Lalit Jain, Chenjie Gu, Goran Žužić, Adams Yu, YaGuang Li, Xiaomeng Yang, Yang Xiao, Adam Zhang, Alex Vitvitskyi, Ashkan Norouzi Fard, Blanca Huergo, Evan Liu, Golnaz Ghiasi, Huan Gui, John Aslanides, Jonathan Lee, Kuba Lacki, Larisa Markeeva, Luheng He, Nigamaa Nayakanti, Nikos Parotsidis, Paul Covington, Petar Veličković, Qijun Tan, Ragha Kotikalapudi, Renshen Wang, Sasan Tavakkol, Shuang Liu, Sidharth Mudgal, Steve Li, Vincent Cohen-Addad, Xianghong Luo, Xinying Song, Yiming Li and Zicheng Xu.The advanced Gemini Deep Think for ICPC was built on foundational research jointly from the Gemini post-training, Thinking and Coding areas including: Aja Huang, Andreas Kirsch, Ankesh Anand, Archit Sharma, Betty Chan, Chenxi Liu, Cosmo Du, Dawsen Hwang, Dustin Tran, Edward Lockhart, Feryal Behbahani, Fred Zhang, Garrett Bingham, Hao Zhou, Hoang Nguyen, Irene Cai, Jian Li, Jarrod Kahn, Junehyuk Jung, Junsu Kim, Kate Baumli, Kefan Xiao, Le Hou, Lei Yu, Maciej Kula, Mahan Malihi, Marcelo Menegali, Miklós Z. Horváth, Mirek Olšák, Nate Kushman, Pei Sun, Pol Moreno, Rosemary Ke, Sahitya Potluri, Shane Gu, Shubha Raghvendra, Siamak Shakeri, Sid Lall, Steven Zheng, Thang Luong, Theophane Weber, Tong He, Tianhe (Kevin) Yu, Trieu Trinh, Vikas Yadav, Vinay Ramasesh, Vinh Tran, Weiyue Wang, Wilfried Bounsi, Xiyang Luo, Yangsibo Huang, Yi Tay, Yong Cheng, Yuan Zhang, Yuri Chervonyi and Yujing Zhang.This effort was advised by Quoc Le and Vahab Mirrokni, with program and operation management from Kristen Chiafullo, Eric Ni, Srinivas Tadepalli, Jessica Lo and Sajjad Zafar.We’d also like to thank our competitive programming experts for providing insights: Alexander Grushetsky, Chun-Sung Ferng, Ilya Kornakov, Liang Bai, Petr Mitrichev and Sergey Rogulenko.We want to extend our deepest gratitude to the Gemini serving team: Abhijit Karmarkar, Cip Baetu, Emanuel Taropa, Evan Senter, Federico Lebron, Girish Ramchandra Rao, Greg Anielak, Hamish Tomlinson, Hayden Jeune, Jia Zhao, Joe Stanton, Ashish Shenoy, Jonathan Kairupan, Juliette Love, Justin Mao-Jones, Kashyap Krishnakumar, Ken Franko, Mahesh Palekar, Minh Giang, Nikhil Sethi, Rohan Jain, Rohit Varkey Thankachan, Soheil Hassas Yeganeh, Thomas Jimma and Vitor Rodrigues.Further thanks to the following people for support, collaboration, and advice: Benoit Schillings, Ed Chi, Koray Kavukcuoglu, Jeff Dean, Oriol Vinyals, Noam Shazeer, James Manyika, Yossi Matias, Philipp Schindler, Pushmeet Kohli, Demis Hassabis, Sergey Brin, Melvin Johnson, Omer Levy, Timothy Lillicrap, Anca Dragan, Slav Petrov, Ya Xu, Madhavi Sewak, Erika Gemzer, Eugénie Rives, Erica Moreira, Tulsee Doshi, Alex Goldin, Jane Labanowski, Andy Forbes, Sean Nakamoto, Yifeng Lu, Denny Zhou, Alexander Novikov, Cristy Hayner, Hanada Tatsuki, Harsh Dhand, Ritu Ghai, Hiroki Kayama, Jenny Rizk Nicholls, Jo Chick, Song Zuo, Pratyusha Mukherjee, Shibo Wang, Carlos Guia, Xiaofan Zhang, …Finally, we thank Dr. Bill Poucher from the ICPC global for the support and endorsement.The ICPC global has confirmed that our submitted solutions are complete and accepted. It is important to note that their review does not extend to validating our system, processes, or underlying model.

Our new method could help mathematicians leverage AI techniques to tackle long-standing challenges in mathematics, physics and engineering.For centuries, mathematicians have developed complex equations to describe the fundamental physics involved in fluid dynamics. These laws govern everything from the swirling vortex of a hurricane to airflow lifting an airplane’s wing.Experts can carefully craft scenarios that make theory go against practice, leading to situations which could never physically happen. These situations, such as when quantities like velocity or pressure become infinite, are called ‘singularities’ or ‘blow ups’. They help mathematicians identify fundamental limitations in the equations of fluid dynamics, and help improve our understanding of how the physical world functions.In a new paper, we introduce an entirely new family of mathematical blow ups to some of the most complex equations that describe fluid motion. We’re publishing this work in collaboration with mathematicians and geophysicists from institutions including Brown University, New York University and Stanford UniversityOur approach presents a new way to leverage AI techniques to tackle longstanding challenges in mathematics, physics and engineering that demand unprecedented accuracy and interpretability.The importance of unstable singularitiesStability is a crucial aspect of singularity formation. A singularity is considered stable if it is robust to small changes. Conversely, an unstable singularity requires extremely precise conditions.It’s expected that unstable singularities play a major role in foundational questions in fluid dynamics because mathematicians believe no stable singularities exist for the complex boundary-free 3D Euler and Navier-Stokes equations. Finding any singularity in the Navier-Stokes equations is one of the six famous Millennium Prize Problems that are still unsolved.With our novel AI methods, we presented the first systematic discovery of new families of unstable singularities across three different fluid equations. We also observed a pattern emerging as the solutions become increasingly unstable. The number characterizing the speed of the blow up, lambda (λ), can be plotted against the order of instability, which is the number of unique ways the solution can deviate from the blow up. The pattern was visible in two of the equations studied, the Incompressible Porous Media (IPM) and Boussinesq equations. This suggests the existence of more unstable solutions, whose hypothesized lambda values lie along the same line.

Our new Perch model helps conservationists analyze audio faster to protect endangered species, from Hawaiian honeycreepers to coral reefs.One of the ways scientists protect the health of our planet’s wild ecosystems is by using microphones (or underwater hydrophones) to collect vast amounts of audio dense with vocalizations from birds, frogs, insects, whales, fish and more. These recordings can tell us a lot about the animals present in a given area, along with other clues about the health of that ecosystem. Making sense of so much data, however, remains a massive undertaking.Today, we are releasing an update to Perch, our AI model designed to help conservationists analyze bioacoustic data. This new model has better state-of-the-art off-the-shelf bird species predictions than the previous model. It can better adapt to new environments, particularly underwater ones like coral reefs. It’s trained on a wider range of animals, including mammals, amphibians and anthropogenic noise — nearly twice as much data in all, from public sources like Xeno-Canto and iNaturalist. It can disentangle complex acoustic scenes over thousands or even millions of hours of audio data. And it’s versatile, able to help answer many different kinds of questions, from “how many babies are being born” to “how many individual animals are present in a given area.”In order to help scientists protect our planet’s ecosystems, we’re releasing this new version of Perch as an open model and making it available on Kaggle.

Our novel Deep Loop Shaping method improves control of gravitational wave observatories, helping astronomers better understand the dynamics and formation of the universe.To help astronomers study the universe’s most powerful processes, our teams have been using AI to stabilize one of the most sensitive observation instruments ever built.In a paper published today in Science, we introduce Deep Loop Shaping, a novel AI method that will unlock next-generation gravitational-wave science. Deep Loop Shaping reduces noise and improves control in an observatory’s feedback system, helping stabilize components used for measuring gravitational waves — the tiny ripples in the fabric of space and time.These waves are generated by events like neutron star collisions and black hole mergers. Our method will help astronomers gather data critical to understanding the dynamics and formation of the universe, and better test fundamental theories of physics and cosmology.We developed Deep Loop Shaping in collaboration with LIGO (Laser Interferometer Gravitational-Wave Observatory) operated by Caltech, and GSSI (Gran Sasso Science Institute), and proved our method at the observatory in Livingston, Louisiana.LIGO measures the properties and origins of gravitational waves with incredible accuracy. But the slightest vibration can disrupt its measurements, even from waves crashing 100 miles away on the Gulf coast. To function, LIGO relies on thousands of control systems keeping every part in near-perfect alignment, and adapts to environmental disturbances with continuous feedback.Deep Loop Shaping reduces the noise level in the most unstable and difficult feedback loop at LIGO by 30 to 100 times, improving the stability of its highly-sensitive interferometer mirrors. Applying our method to all of LIGO’s mirror control loops could help astronomers detect and gather data about hundreds of more events per year, in far greater detail.In the future, Deep Loop Shaping could also be applied to many other engineering problems involving vibration suppression, noise cancellation and highly dynamic or unstable systems important in aerospace, robotics, and structural engineering.