SCROLL NEWS / DISCOVERY
Search headlines
Find the stories shaping the conversation.
Results for " Gemini " 694 found 🔀 AI-powered Shuffle
November Pixel Drop brings Gemini Intelligence to Pixel 10 family
Spruce up your home screen with expanded custom icons and AI-generated widgets.
Google Debuts Gemini AI Agent for Healthcare Operations
Google Cloud introduced an agentic artificial intelligence solution designed for healthcare operations, according to a Tuesday (Nov. 10) press release.
Google Cloud rolls out Gemini for healthcare
Google launched Tuesday Gemini for healthcare, an agentic artificial intelligence plat | The platform was developed alongside clinical and health plan leaders and is built to support HIPAA compliance, the tech giant says.
Google's Gemini 4 Carbon Is Now Widely Available Around The World
Google is rolling out Gemini 4 Carbon after a limited release to cybersecurity companies.
Google releases Gemini 4 Carbon with agent ‘swarm’ feature
It ‘pushes the frontier even further’ than its last release, Argon.
Gemini 4 Carbon rolling out to Gemini app for AI Ultra, Argon remains
Following the preview at the end of last month, Google is now bringing its latest Gemini 4 Carbon model to the Gemini app.
Google and Adobe partner to add app integration in Google Drive, Gemini
Google Drive will soon prompt users to switch to Adobe Acrobat while viewing PDFs, and Gemini will get a few...
Google launches Gemini for Healthcare, and hospitals like Northwell hope it brings down your medical bill
Northwell CEO John D’Angelo says scattered records lead to repeat scans and wasted spending. Google says its AI can connect them.
Agentic AI Transforms Banking: Scaling Struggles
Financial services leverage Agentic Artificial Intelligence in Banking. Only 5% of institutions scale AI agent ecosystems beyond pilot phases. Ambition meets execution challenges.
ChatGPT's growth rebounds as Claude and Gemini trail behind
ChatGPT has more than 81% of traffic to six major AI chatbots, Graphite found, as growth resumes and Claude and Gemini remain far behind.
Gemini Omni 1.2 Flash now GA: cost-effective video
Gemini Omni 1.2 Flash is now generally available on Google Cloud: fast, cost-effective, and reliable video generation and editing via API.
Motorola Signature 27 gets Pixel’s Gemini Intelligence features with Rambler, Create My Widget
Motorola is adopting Google’s Gemini Intelligence features in the first major expansion beyond Pixel, with Gboard’s Rambler and more supported....
Watchful mama bear Gemini wins 2026 Helicopter Mom Award
Gemini kept an eye on her cubs around other bears fishing in Alaska's Anan Creek.
Sleep Clinic, Real Estate Broker Win BBB Ethics Award
The Better Business Bureau presents its Torch Award for Ethics to Alaska Sleep Clinic and Alaska Premier Group.
RETROFIT Platform to Present AI-Powered Immunotherapy Innovations at Science for Health 2026
RETROFIT Platform to Present AI-Powered Immunotherapy Innovations at Science for Health 2026 / AI, Biovia Science for health, cancer, immunology, OncoDaily,
RETROFIT Platform to Present AI-Powered Immunotherapy Innovations at Science for Health 2026
RETROFIT Platform to Present AI-Powered Immunotherapy Innovations at Science for Health 2026 / AI, Biovia Science for health, cancer, immunology, OncoDaily,
Google's cheapest Gemini plan is losing a major perk; now I can't recommend it to anyone
Pay more or lose Pro access
The latest Google Home update goes way beyond Gemini and fixes your Nest gear too
This is a big update!
Latest Gemini design comes to all Android devices, app upgrades file generation
A pair of Gemini app updates today brings the latest design to all Android devices, while file generation sees a big upgrade.
Google Home update should make Gemini stop saying ‘Sorry, I didn’t understand’ so much
Google’s latest Gemini for Home update targets a few issues, like one where speakers randomly blurt out “I didn’t understand”...
You can generate and edit Google Docs files directly in Gemini chats now
Editing files you generate with Gemini no longer requires leaving the app.
Best of Santa Cruz County entertainment, arts & food events this weekend, Oct. 29-Nov. 1
With the weekend nearly here, check out things to do around Santa Cruz County, with a recommendation from Lily Belli and a specially curated list from Lookout’s BOLO events calendar.
Google Sends Major Updates to All of Its Google Home, Nest , Streamer Devices
Google is back this week updating all of its Google Home-related products, from the app and Gemini for Home to the hardware it still supports. On the hardware side of things, we’re seeing updates for the Chromecast with Google TV, Nest WiFi and Nest WiFi Pro, and its Nest and Google Home Speakers. These updates...
A golden age of science is coming
It’s almost hard to remember, but just a few years ago, a lot of very smart people thought that science was in terminal decline. Respected researchers were writing papers with titles like “Are Ideas Getting Harder to Find?”, “Papers and patents are becoming less disruptive over time”, and “Slowed canonical progress in large fields of science”. In 1996, science writer John Horgan even published a book called The End Of Science: Facing The Limits Of Knowledge In The Twilight Of The Scientific Age. The fundamental idea was that the Universe only has so many insights to discover, and that we’ve picked the low-hanging fruit. It was the idea that Joseph-Louis Lagrange expressed when he said that “we cannot find more than once a system of the world to establish.” As the big ideas and the simple ideas get mined out, we’re forced to throw ever more brains at the mysteries of nature just to keep scientific progress going at a fraction of its old pace: It looked like science was hitting a wall. Then along came AI. It’s far too early to tell how much AI will eventually speed up the growth of total factor productivity. But already we can see signs of a speedup in science. Anthropic Founder Jack Clark assembled some of the hopeful data points in a recent lecture: [ Dror Poleg@drorpoleg "If you pick different parts of science, you see the same pattern show up: Progress is accelerating, and it has started to accelerate in the last two years." Jack Clark @jackclarkSF I’m speaking at the IMF in an hour about the singularity and why I believe it has begun and the choices we'll need to make. Watch live here: https://t.co/Hj9HzcXx5h 6:08 PM · Oct 18, 2026 · 21.2K Views 2 Replies · 10 Reposts · 123 Likes ](https://x.com/drorpoleg/status/2111882070376980529) Anthropic is accelerating its investment into drug discovery, and is already announcing some impressive preliminary results: [ BioTender@Junioryu136689 BREAKING: Anthropic unveils early results from six Claude-powered drug development collaborations. • 70% shorter CAR-T signaling domain • ~35,000 antibody designs • >10× tighter antibody binding • 89% less batch-record review time Still largely preclinical. Anthropic @AnthropicAI Over the past few months, Anthropic scientists have engaged in deep research partnerships with six organizations developing new medicines. We've also begun drug discovery efforts of our own. In doing so, we’ve learned much more about how Claude can support the many stages of 9:43 PM · Oct 27, 2026 · 11.2K Views 3 Replies · 24 Reposts · 205 Likes ](https://x.com/Junioryu136689/status/2115197821150495150) Claude also appears to be enabling rapid progress toward a treatment for food allergies. OpenAI, meanwhile, announced a possible breakthrough treatment for neurodegenerative disease. Meanwhile, Google Cloud Chief Scientist Pushmeet Kohli has a list of recent scientific advances that have been enabled by its new Gemini model, including breakthroughs in nuclear fusion reactor design, rare disease research, and quantum computing: [ Pushmeet Kohli@pushmeet Gemini 4 Argon is a step change in model performance. In recent weeks, it has had a transformative impact on the Science projects in our team at @GoogleDeepMind and our external collaborators, and helped make a number of remarkable advances across scientific disciplines. 🧵👇 4:47 PM · Oct 13, 2026 · 36.5K Views 8 Replies · 41 Reposts · 497 Likes ](https://x.com/pushmeet/status/2110049807385702623) But as cool as these stories sound, they’re just very minor, preliminary efforts. There are a number of reasons to think that AI is going to propel us into a golden age of scientific discovery. That age might not last forever — AI might still run into diminishing returns eventually. But the next few decades are probably going to be an absolutely amazing time for human science. The number of “effective researchers” is no longer limited The most important input into science is scientists themselves. Until this year, that meant human brains — talented, highly trained human brains. The number of trained human brains could be increased by sending more people to grad school, but only to a point — the smartest people tend to be sent into the hardest research fields first, and if you 10x the number of researchers, the average quality will go down. This constraint was already starting to bite, as Ekerdt and Wu argued in 2024: We argue that a substantial portion of the downward historical trend in U.S. research productivity is a composition effect resulting from self-selection in researcher ability and the dramatic expansion of the research sector…[W]e find that this composition effect has lowered average researcher ability by between 37% and 53%[.] And of course, low fertility was making the problem worse, because fewer children meant fewer people who were able to become future scientists. AI just blows up this constraint completely. If an AI agent can function as an autonomous researcher, there’s no constraint on the number of researchers in the world — you can just build more of them, by building more computer chips and data centers that enable the running of more AI agents. Whether or not massive hordes of agents can collaborate to solve individual problems much faster, increasing the number of agents allows them to tackle many different research questions in parallel. What’s more, these AI “researchers” can be trained incredibly quickly compared to human beings. A human scientist takes decades to train, and more years to reach the frontier of their research field by diligent reading and conversation. This is the “burden of knowledge”, and it’s commonly believed to be one reason ideas have been getting harder to find — by the time human scientists can get up to speed, they’re often so old that their ideas are no longer novel. AI just annihilates this constraint — it can assimilate the entire literature and reach the frontier of research extremely quickly, and it can also tell human scientists where the frontier lies without forcing them to discover that for themselves. It can also pivot to new research directions more easily than humans can. And there’s also a self-reinforcing economic cycle here. Because scientific advances enable higher GDP, they enable more resources to be invested into the creation of yet more electronic “scientists”. Now that doesn’t mean the amount of scientific research goes to infinity — there may still be some essential bottleneck task that human researchers are still needed to perform. There are some indications that AI’s progress on open-ended conceptual research tasks is slower than in other dimensions, and it may be that humans are still needed to provide some of this long-term conceptual thinking. I’d bet against this being true for much longer, but it might be true for a long time. Even in this most “pessimistic” case, though, the amount of effective capital that scientists can use to leverage their work just went up by a lot. AI eliminates the theory bottleneck Science proceeds by an iterative process of theory and experiment. The theory part of that cycle is typically short, but non-trivial — it took a couple of years after the blackbody radiation spectrum was observed for Max Planck to come up with the quantum hypothesis to explain it, and decades longer before quantum theory as we now know it was created. It took about a year for Watson and Crick to figure out the shape of DNA after the requisite data was gathered. AI will basically eliminate the theory bottleneck entirely — or at least shorten the turnaround time between experiment and theory by an order of magnitude. AI is an absolute beast at math. Recently, an OpenAI model solved some of the most important open problems in computer science, math, and theoretical physics, and no one even batted an eyebrow because that sort of triumph is becoming increasingly commonplace. One of those solutions was a proof that the Standard Model of Particle Physics could be mathematically well-defined — a problem that human theorists had so far been unable to tackle. Not all research relies equally on theory, but understanding novel experimental results almost always involves some important component of either mathematical or spatial reasoning. That part of the task has now been made much, much faster. And on top of that, science is now open to a greater number of humans who aren’t naturally good at theorizing. AI can read the whole literature Much of the work of human scientists involves reading and understanding endless amounts of papers and going to endless conferences and seminars. The purpose of that work is to understand the existing scientific literature. Understanding the literature allows scientists to do the following things: Come up with new ideas for hypotheses to test Avoid doing research that someone else has already done Synthesize approaches from different areas or fields AI can reduce this effort to almost zero. Consider this excerpt from OpenAI’s blog post about their new research into neurodegenerative disease: The team divided the analysis among thousands of AI agents powered by GPT‑Rosalind, an approach we call distributed biological reasoning. The agents examined a whole-brain CRISPR atlas…a dataset showing how gene disruptions affect different types of cells across the mouse brain. Each agent investigated a gene disruption, analyzed cellular responses, searched scientific literature, and proposed explanations for the results. Their reports were combined into a reasoning atlas linking genes, cellular effects, relevant publications, and possible biological mechanisms. The research team estimated that comparable human analysis and literature review, conducted one perturbation at a time, would take months to a year. The agents reproduced findings from human analyses while uncovering additional leads. Their assessments of novelty and scientific value also aligned with judgments from blinded human experts. These findings help prioritize further experiments needed to test the proposed biological mechanisms. Some of these insights point to new leads for understanding brain disease. [emphasis mine] This is one case in which parallel work by a large number of agents really does make a huge difference: [ Haider.@haider1 this is actually insane openai deployed thousands of GPT-Rosalind agents to analyze millions of mouse brain cells in 32 minutes, work that could take human researchers months to a year also the agents uncovered new leads for brain diseases like Alzheimer's and Parkinson's OpenAI @OpenAI GPT-Rosalind surfaced a new lead for drug discovery: could restoring cholesterol transport protect neurons from degeneration? DCTN1 is a gene linked to Perry syndrome, a rare neurodegenerative disorder. In mouse-brain data, disrupting it increased expression of genes involved in 2:41 PM · Oct 21, 2026 · 51.5K Views 26 Replies · 95 Reposts · 1.26K Likes ](https://x.com/haider1/status/2112917197903540516) Not only does instantaneous literature review vastly speed up the research process, but it allows AI to make novel conceptual leaps from combining approaches from wildly different fields of research. AI regularly does this in math research, combining results, approaches, and theorems from far-flung places that no human mathematician would have thought to combine. It can do it in science, too. Much of scientific innovation, historically, has been “recombinant” innovation — scientists applying insights from one field to another. With AI, essentially all research is now cross-disciplinary — AI can read not just the literature on a particular subject, but the literature on a million other subjects that might have produced useful techniques or insights for the problem at hand. Superscience So far, all of the factors I’ve listed are ways that AI can speed up science as it’s already done. I’ve basically described how AI can send gigantic hordes of superhumanly fast, superhumanly well-read, superhumanly mathematically gifted researchers to re-accelerate scientific discovery. But it’s also possible that AI will enable types of discovery — and ways of understanding the natural world — that no network of humans, no matter how large or how brilliant it was or how long it had to work, would ever be able to discover. In a post back in July, I wrote about how that might work: What if there are lots of…aspects of the Universe that [are] too complex to understand in terms of simple laws, but not so complex that they just dissolve into unknowable chaos? It’s possible that we can reliably control these complex phenomena with AI, even if we never reduce them to the kind of principles that we can teach a grad student in a textbook… Another way of saying this is that there may be laws of the universe that humans can’t understand but AI can. I call these “cloud laws” — causal regularities that can be exploited by technology, but which are too diffuse and complex for an individual human being to either intuit or communicate… Perhaps social sciences like economics, sociology, and political science obey similarly complex regularities, and AI can help us find them. Perhaps there are physical processes — plasma, or topological materials, or aerial turbulence, etc. — that obey cloud laws instead of chaos?… [H]uman collaboration is bottlenecked — it’s limited by our ability to intuit patterns at an individual level, and to communicate these patterns from one individual to another. AI, being a computer, just doesn’t have this sort of limitation; it can work with vast, diffuse patterns without having to break them into pieces or simplify them in order to compress them into the tiny pipelines of person-to-person explanation… If much of the Universe turns out to be ruled by cloud laws, we could be on the precipice of a scientific renaissance. In order to discover cloud laws, AI will need huge amounts of data about the natural world. AI figured out human speech by assimilating essentially all of the text humans have ever written. If it’s going to figure out hidden patterns in biology, neuroscience, materials science, etc., it’s going to need similarly massive amounts of data from physical instruments. That data will be far, far more than humans — individually or collectively — would be able to parse, label, characterize, or model. But AI was built to do this. If scientists simply build huge arrays of sensors to measure complex phenomena like the brain, the cell, the economy, etc., they may be able to feed this flood of data to AI directly and bypass human cognitive intermediation entirely. AI would then learn about the world directly, and it might learn things that humans never could. This “superscience”, if you want to call it that, would actually go beyond the kind of science humans have done heretofore. Many of the technologies it yielded would be beyond human comprehension — indistinguishable from magic. But to the AIs, they would make perfect scientific sense. In any case, it seems clear that the scientific slowdown that threatened human progress will now be delayed — at least for a while. Unless humans have somehow found most of the actual exploitable regularities in our Universe, we’re about to enter a new golden age of science. Subscribe Share
As AI agents reshape the web, Google opens Gemini and Search to more apps
A new developer platform will let businesses bring their services to Google's AI experiences, potentially changing how consumers discover and use apps.
Google adding more Connected Apps to Gemini & Search with new dev platform
Google announced the Connected Apps Platform as a “unified way for more app developers to bring their capabilities directly into Gemini and Search.”
[Hands-On] Comfort, Convenience, and More: A Day With Galaxy Buds On
Earbuds have become an everyday essential for listening to music, taking calls, exercising
Gemini Live redesign makes it easier to add context to chats
Following the other big interface updates this month, Google is rolling out a small 'plus' redesign to Gemini Live on Android...
📈 Monday data: The bottleneck isn’t intelligence
In the AI Investment Brief today: CapEx updates and lease commitment breakdowns Anthropic co-founder Jack Clark recently said in a speech at the IMF that the singularity has begun: For many years, researchers and science fiction authors have forecast a future in which AI systems drastically increase the pace of overall scientific and technical progress, making things move so quickly that it is hard for humans to keep up. They call this the singularity. I believe that this year we have gained convincing evidence that it has now begun. This follows Anthropic’s claim that in August, 26% of its AI R&D was led by Claude, with a human supervising its work. In October, OpenAI posted 719 math manuscripts1 made by its internal model. Math and coding are two fields where a research loop can close without leaving the desk. But can the loop close in the rest of science? Subscribe The loop is real The idea is not new. Mathematician Irving John Good, who had worked with Alan Turing at Bletchley Park, argued that a machine smarter than us “could design even better machines; there would then unquestionably be an ‘intelligence explosion,’ and the intelligence of man would be left far behind.” The Gödel machine, a self-improving computer that can rewrite its own code better than its prior, was first proposed in 2003. In 2025, researchers at Sakana AI and the University of British Columbia set out to show that this was possible. Their agents iteratively improved their own code through a feedback loop, resulting in gains of 30 pp on SWE-bench and more than 15 pp on Polyglot, testing coding skills beyond Python. The idea has since been tested in other labs. A Gemini-powered agent called AlphaEvolve found a faster matrix multiplication kernel used for training Gemini, speeding up the kernel by 23% and cutting Gemini’s training time by 1%. In China, MiniMax reported that its M2.7 model worked on improving its own agent’s harness, skills, sampling and memory. Over 100 rounds of rewriting led to a 30% gain, per its internal evaluations. In each case, what was being rewritten was not the model itself but everything around it. Is it better than human work? In September this year, in what seemed like a milestone, an AI-built agent proved as good at improving other AI agents as a human-built one. It did that in what appeared to be half the steps. But the result was inconclusive: the differences between runs were greater than the difference between the AI- and human-built agent results. Nonetheless, this is increasing the chatter around recursive self-improvement (RSI) in AI. Google’s Sundar Pichai said that “you have RSI loops along with AI researchers in various aspects of model development.” Sakana AI published a paper on RSI through multi-agent collaboration, finding that after two cycles its system scored 1.2–1.6x better per output token than the base model across four research benchmarks. Ramez Naam challenges that view. He splits AI self-improvement into five overlapping types. The first four describe how autonomous the AI is; so far, only types 1 and 2 have been observed. The fifth type describes accelerating returns, and he notes that greater autonomy doesn’t guarantee a takeoff scenario. Instead, each round of self-improvement would need to deliver at least as much as the last. The results from Sakana, AlphaEvolve and MiniMax above might look like type 3 or type 4 autonomy. But in each case, the AI wasn’t updating its own weights directly, only the ancillary parts — the harness, the memory. And where Claude leads its own R&D, for now a human still supervises.
Google appoints Anne Claire Grammer to Gemini mental health role | ETIH EdTech News
Google has appointed clinical psychologist Anne Claire Grammer as Senior Clinical Specialist, Mental Health, working on the accuracy and safety of Gemini's mental health responses. ETIH edtech news covers her appointment, including previous AI product safety work at BetterHelp and her academic role
Google's next TV gadget may do much more than stream
A reported hardware overhaul could bring Gemini further into the living room.
AI image generator test: Canva Craft vs. ChatGPT, Gemini, Meta Muse
While Canva's new image generation tool fell short at making a birthday card, it's catching up to more powerful models in creating realistic images.
Gemini app expands Google Maps Personal Intelligence, replacing Deep Think
Besides coming to Android Auto this week, Personal Intelligence in the Gemini app is also expanding its Google Maps integration.
Google Maps on CarPlay rolling out Gemini when navigating
Google Maps in CarPlay now lets you access Gemini when using navigation for a conversational driving experience.
Google’s more personalized Gemini AI is heading to your car
Personal Intelligence is available in Android Auto starting today.
Gemini's Personal Intelligence is coming to cars
It's probably time to audit your Gemini settings.
Google is bringing a more personal version of its Gemini AI assistant to your car
Google is bringing Gemini's Personal Intelligence feature to cars, allowing its AI assistant to draw on information from Gmail, Calendar, Maps, YouTube, and other Google services to deliver more personalized responses while driving.
La Mesa baker turns a family passion into a full-time business
La Mesa baker Megan Loney turned a sourdough hobby into Gemini Twins Bakery. Rising at 2 a.m. while raising four children, she’s building a thriving family business one loaf at a time.
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.
Google is renaming Gemini again; here's what each feature does now
Gems, Skills, Spark, Scheduled Actions: a field guide to renamed features
Alejandra Mendez: Honored to Receive the 2026 Innovators in Health Award
Alejandra Mendez: Honored to Receive the 2026 Innovators in Health Award / 2026 Innovators in Health Award, Alejandra Mendez, cancer, Childhood Cancer, Chile,
Famous supernova that lit up skies in 1181 is now full of mysterious knots — Space photo of the week
Pa 30, linked to a supernova seen in 1181, reveals chains of tiny gas knots in a new image from the Gemini North telescope in Hawaii.
Can AI solve our mental health access problem?
Last week I asked you what worries you most about AI in health. It was close, but your top answer was “AI giving bad health advice.” Last week’s poll results: You’re in good company. This concern was top of mind at a conference I joined at Stanford last week, where researchers, clinicians, and people who build AI systems spent a day debating trust and safety in health AI. Stepping back, I’m also hearing your emerging concerns about AI’s impact on our well-being generally. I see this as a matter of public health, and we’ll take it in pieces as we learn together. Today we’ll focus on AI chatbots used for therapy for mental health. Early this month, Governor Newsom vetoed Senate Bill 903, California’s most ambitious attempt to regulate AI use for mental health. It’s at the center of active debate, and raises the questions patients, family members, citizens, and voters will face as AI moves deeper into health care. Let’s work through it. What is “AI-based therapy”? It’s really two things: purpose-built mental health apps designed to deliver structured support, and general chatbots like ChatGPT, Claude, and Gemini that people increasingly use for emotional support—even if they weren’t built for it. Spoiler alert: I don’t know whether the shift toward AI therapy is good or bad. The evidence isn’t there yet. But I do know it’s already happening, and I have some ideas about how we can roll it out with safety in mind. The therapy dilemma, in a nutshell Mental health challenges are common, and strain health and well-being across populations. Therapy (interactive dialogue with a trained, empathetic practitioner) helps many people heal. The demand for licensed therapists far exceeds the supply in California, and AI-based therapy can narrow that gap. AI-based therapy remains novel, unproven, and not well tested. Right off the bat, we can see we’re dealing with trade-offs here. Since the main driver of demand for therapy from a robot is lack of access to therapy from a human, we’ll start there. It’s too hard to find and afford a human therapist California is short roughly 55,000 licensed, non-prescribing therapists and counselors, about 40% of what we need. Psychiatrists, who rely on talk therapy as a complement to prescribed medications, are even more scarce. Eight counties have none, and density ranges from 1.7 per 100,000 residents in San Benito County, to 68.1 in Marin County. My wife is a psychiatrist in Marin, and even here access is tight. If it’s hard in the best-supplied county, imagine rural Sierra County, or the San Joaquin Valley. In 2024, more than half of Californians who tried to make a mental health appointment struggled to find a provider who took their insurance, compared with 14% for physical health care. When Senate staff posed as “secret shopper” patients and called 120 mental health providers in Medicare Advantage networks, they landed an appointment less than once for every five tries. Insurers pay behavioral health clinicians about 22% less than medical and surgical clinicians for comparable office visits. On top of that, billing denials and prior authorizations hit hardest at the solo and small practices common in mental health. Psychiatrists opt out of Medicare at the highest rate of any specialty. In many places, a cash practice for therapy fills quickly with patients paying $250 or more an hour, with no insurance paperwork at all. Families who can pay cash find providers more easily. Those who rely on insurance, including Medi-Cal enrollees, older adults, rural residents, and non-English speakers, compete for what’s left. So we can’t pretend the human system works, and we can’t blame people for turning to AI for support when they’re in distress. AI is filling the gap It’s estimated that roughly a quarter of AI users use it for mental health support. If you have internet, it’s available 24/7, it’s cheap, and it speaks your language. Uninsured adults are about twice as likely as insured adults to do it (30% vs. 14%). It feels anonymous in ways that may enable people to ask things they’re unsure or embarrassed about. The largely hidden, personal use makes it hard to study whether it’s actually helping people with their mental health issues, but researchers are trying. Does AI therapy work? A 2026 analysis of nearly 40 randomized trials found chatbots showed a real but small reduction in depression symptoms. But most of these trials were compared to an alternative of no therapy, not a human therapist. So the benefit of AI therapy is probably real, but modest, and mostly untested against the alternative of human contact. We need more studies. Serious concerns OpenAI’s own estimate is that more than a million ChatGPT users each week show signs of suicidal planning or intent. These are high stakes encounters, and AI doesn’t always get it right. That’s partly because a chatbot can sound like a therapist, but it has no license, no duty of care, and it’s not clear who to hold accountable when it gets things wrong. AI is also designed to help users in whatever task they’re seeking to accomplish. People may feel like they’re getting therapy, but they’re mainly getting a convincing conversation. It’s easy for an AI companion to wander into gray areas, like playing along with “creative writing” that may be about the user’s own suicidal intent. Courts are driving safety conversations A wave of lawsuits have been filed on behalf of families claiming that chatbots pushed people they loved toward self-harm, including teens who died by suicide. The first settlements came this year. OpenAI is headquartered in San Francisco, and dozens of cases alleging that ChatGPT drove users to suicide or self-harm are now before a San Francisco judge. Character.ai has filed for bankruptcy after several states brought lawsuits accusing the chatbot of pushing dangerous content to kids. But the technology seems to be improving. Last month, an independent evaluation found that newer AI models “almost never endorse suicide.” That’s progress, but our societal appetite for even one preventable death is very low. So how do we move forward? Finding a middle ground Earlier this month the governor vetoed Senate Bill 903, a bill designed to place guardrails on the use of AI for therapy. The bill would have prohibited any AI-based platform from being advertised as psychotherapy, and would have required a licensed professional to review and approve each encounter where AI was used to support therapeutic decisions. Newsom called it overly broad, since even routine screening questions would have needed clinician sign-off. Knowing we’re already short on mental health providers, requiring human review of AI encounters may not relieve our access problems. Still, the answer isn’t no oversight. Here’s one feasible short term solution. Rather than reviewing each encounter, a licensed clinician can design the guardrails: what counts as a crisis, what triggers an immediate handoff to a human, and what gets sampled for audit each month. The AI runs inside that structure. Conversations get checked, failures get reported, and a real clinician owns the framework. Medicine already has models to learn from. The FDA process for rolling out newly approved medications is an example. After a product reaches patients, we keep watching for adverse events. Patient-facing AI needs the same “digital pharmacovigilance”: systematic tracking of harms, near misses, and the types of groups that get hurt and why. It’s also critical that users know that they can reach a human if they’re in crisis. Most chatbots now include messages like these: Bottom line The rush to use unproven AI tools for mental health support is mostly a workaround, a sign of a health system people can’t get into. We need better access to proven care from licensed clinicians. That takes reimbursement that makes accepting insurance viable, and a bigger workforce. But, built and tested well, AI could be part of the answer. It could be the 2 a.m. check-in when no one else is awake, or a way for patients to practice skills their therapist taught them. It could be the first step to get someone the right level of care, and take paperwork off clinicians’ plates so they spend more time with patients. None of this is simple. The SB 903 veto doesn’t end the debate, and the legislature will be back. In the meantime, those of us using these tools need to stay informed, ask hard questions, and hold the companies building them accountable. I’ll try to do my part, and we’ll keep learning together. Love,Matt P.S. If you or someone you know is struggling with mental health challenges, emotional distress, substance use problems, or just needs to talk, call or text 988- you’ll reach a real person— or chat at 988lifeline.org 24/7. Dr. Matt Willis is the author of Your Local Epidemiologist in California. A California native, he’s served as a primary care physician, CDC epidemiologist, and public health officer for Marin County, where he guided the pandemic response. He lives in Marin with his family and their dogs, Teddy and Ramona. Thank you Cynthia He, MD, PhD, and Parnika Saxena, MD, for their guidance in preparing this article. Cynthia works in an outpatient mental health clinic, treating patients who have Medi-Cal or, sometimes, no insurance. Parnika works in an inpatient psychiatric unit, treating patients with public or private insurance or self-pay ability. All opinions are their own and do not represent their employers.
New Google Messages Feature: Circle to Search a Message to See if It's a Scam
Google Messages now lets you use Circle to Search to instantly scan text messages for red flags and potential scams.
Gemini AI Plus subscribers will lose access to Pro models next month
Google has begun emailing Gemini AI Plus members to inform them when their Gemini subscription will no longer include access...
Entertainment Journalism Icon Jimmy Carter Dies: Cause of Death and Net Worth
Jimmy Carter’s name became closely associated with entertainment coverage and with interviews with major figures in music and film
Gemini 4 Argon Arrives With Big Claims for AI Agents and Scientific Discovery
Google just released the new Gemini 4 Argon, which it claims can generate up to a million tokens in a single run, a big jump for long code migrations and multi-step agent work. Competitors top out around 128,000 output tokens per request, so based on that claim, Argon’s 1 million is roughly 8x higher. Argon […]
O’Mara Named 2026 ‘Guardian of Small Business’
Big Flats Senator Tom O'Mara earned the 2026 'Guardian of Small Business' award. He's one of five senators with a 100% NFIB voting record.
Free Gemini app users now only have access to ‘Auto’ models
As announced last week, Google is rolling out a big change to the Gemini app experience for users on the free plan.
Google employees praise latest Gemini 4 internal model
Google is rolling out new Gemini 4 models to staff ahead of a public release. The latest is called Carbon, and staff are loving it.
Google brings agentic AI to Gemini, starting with businesses
Google is turning Gemini into an AI agent that can plan, execute tasks, and work across business apps and systems. The agent can delegate work to subagents, use multiple AI models, and even gets its own workplace identity, complete with an email address.
Google Cloud introduces Gemini agent for work as AI race heats up
Alphabet's Google Cloud on Thursday introduced the Gemini agent, a single AI agent for work that can answer questions, handle tasks, create content and write code.
Google is launching a one-stop Gemini agent for your work tasks
The enterprise-focused Gemini agent handles work from a single interface.
Google Cloud unveils persistent Gemini Agents for long-running tasks, and they get their own Gmail, Calendar, and Drive storage
While Google supplies its own Gemini models, the agent can already use Anthropic's Claude models, with support for additional proprietary and open-weight AI models planned.
Google Cloud announces ‘Gemini agent’ as ‘universal agent for work’
At Gemini at Work 2026, Google Cloud today announced the "Gemini agent" for enterprise customers. This is for...
Google rolling out ‘massive’ Pixel Buds Pro, Pro 2, and 2a update
Following last month's addition of Gemini control, Google is rolling out a sizable Pixel Buds Pro, Pro 2, and Pixel Buds 2a update.
Claude launches Google Docs, Sheets, and Slides integration with sidebar and more
If Gemini isn’t quite your cup of tea, Anthropic’s Claude now supports sidebar integration with Google Docs, Sheets, and Slides....
Gemini Call for Me might tell your mom you’re running late
Would you call your parents with Google’s AI?
New AI Detector Spots Machine-Written Science Papers With 91.5% Accuracy
Researchers in Jordan have developed a hybrid deep learning detector that distinguishes human-written scientific text from output generated by ChatGPT, Gemini, and LLaMA-3 with 91.5 percent accuracy.
Gemini app limiting what models free & AI Plus users can access, AI Pro adding Deep Think
Following the compute-based usage change in May, Google is introducing limits on what Gemini models free and AI Plus users can access.
Google AI Plus and free users are losing access to Gemini models, and it's happening very soon
The more advanced tiers aren't seeing any changes.
Gemini app gains new ‘Map’ tool, replacing ‘/’ with ‘@’
Google has introduced a “Map” tool to aid your location-based Gemini app prompts on Android and iOS. Meanwhile...
A familiar Android feature is disappearing and users have plenty to say
Google is replacing Google Assistant with Gemini on Android phones, with some users reporting issues during the transition.
Google’s overdue Gemini 4 Argon reaches the frontier with mixed results for science
Google’s Gemini 4 Argon brings competitive AI performance at introductory prices, but independent tests show uneven results against leading rivals.
Google’s new Guided Vision feature can help you read the fine print
Can you trust AI to see what’s going on around you?
Google's new Gemini 4 model could put it back on the AI frontier
Google said its new Gemini 4 matches OpenAI's frontier Astra model in cyber defense, and outpaces rivals in some areas.
Google details Gems to skills migration, including free access
As we spotted over the weekend, Gemini is replacing Gems with skills, and Google today fully detailed the migration.
Gemini app adding Google Wallet integration for ‘personalized insights’
The latest Gemini Connected App is Google Wallet for “information and personalized insights based on your saved info.”
Gemini fully replaces Google Assistant on Android
At the start of September, the Google Assistant deprecation on mobile got underway. It now looks to be widely rolling out.
Google is killing off Gemini's Gems in favor of 'skills'
As all-in-one AI agents like Meta's Muse and Instinct take off, Google is opting to end a feature which built task-specific agents.
Pixel Buds app update lets Gemini control audio and settings
We’re now starting to see Gemini audio controls with the latest Pixel Buds companion app update. This is part of...
Google is officially killing Gemini Gems
The Gems manager in Gemini is now showing a banner officially announcing that Gems will be migrated to Skills starting November 17.
Malicious browser extensions can hijack AI assistants
BragJack research reveals how a single malicious browser extension could hijack Gemini, Perplexity Comet and Edge AI agents with zero clicks.
Gemini app replacing Gems with skills in November
In 2024, Google introduced Gems as “custom versions of Gemini.” The Gemini app will soon replace Gems with “skills.”
You Can Edit Your Photos With Lightroom and Photoshop Inside Google Gemini
Google Gemini users can now edit their photos with Adobe's creative editing tools inside Gemini, including color and lighting changes.
Google Adds Creepy Avatars To Gemini 3.8 Live's Agents
Google has introduced Gemini 3.8 live avatars designed for customer service and other tasks.
Gemini 3.8 Live with Live Avatar gives Google’s AI a face
The AI avatar can transition between 97 languages.
Google Photos Getting 5 Fun Feature Upgrades
Today, Google Photos announced that it is rolling out 5 upgrades to your experience, giving you power over photos with Gemini Spark, access to your digital closet, new Moods to style things with, new Remix templates, and an upgraded markup tool. Some of this new stuff you’ve heard of before, but this is Google acknowledging...
Gemini can now call businesses for you so you don’t have to wait on hold
Would you let Gemini do the talking for you?
Google’s Gemini Can Now Make Calls for You on Pixel Phones
Call for Me—a feature that’s exclusive to the Pixel 11 series—gives robocalls a new meaning.
Google tests letting Gemini call businesses for you
Google says the AI-calling feature will first be available to Pixel 11 owners in the U.S. who pay for a Gemini subscription.
Pixel 11 starts testing ‘Call for Me’ to have Gemini phone on your behalf
Following Direct My Call and Hold for Me, Google is introducing "Call for Me" on the Pixel 11 series. Reminiscent of Google Duplex...
Google tests letting Gemini call businesses for you
Google says the AI-calling feature will first be available to Pixel 11 owners in the U.S. who pay for a Gemini subscription.
Googlebooks will get full GeForce Now support ‘later this year’
Nvidia says Google’s new Gemini-infused laptops are slated to get GeForce Now support before the year is up, which is...
Gemini can now clone your voice and perform scripts like an actor
Google’s Gemini 3.8 Flash TTS can create custom voices, replicate authorized voices, and control accents, pacing, emotion, and dialogue.
Google expands free AI video generation in Google Vids
Google expands Google Vids: create free AI videos with Gemini Omni 1.1 Flash, 1080p HD, avatars & SynthID watermark.
Gemini just got a big upgrade with new connected apps for work, creativity, and life
Google is bringing several new additions to Gemini's connected app ecosystem, including Peloton and SeatGeek.
YouTube Music adding corner access to ‘Ask Music’ with Gemini
As part of Made on YouTube 2026, a handful of updates to YouTube Music that are coming later this year were previewed.
Google opens pre-orders for $899 Googlebook laptops built around Gemini AI
Alphabet's Google on Monday opened pre-orders for Googlebook, its new premium laptop category for Android users built around the company's Gemini AI tools.
Should you trust an AI doctor? Relying on Gemini for health advice could be fatal
A fake disease, complete with absurd clues and a fictional laboratory aboard the USS Enterprise, was enough to fool AI chatbots—and even find its way into the scientific literature. The hoax exposes a deeper problem: AI can sound authoritative without knowing whether the information it has absorbed is true.
Gemini’s exchange business is shrinking. Its regulatory licenses may be the real prize
The crypto platform’s market value has fallen to about $753 million, putting fresh attention on the licenses, custody infrastructure and customer relationships a buyer could inherit.
Small businesses, big impact | Inside Blount County's hospitality growth
Oldham Hospitality and Gemini Hospitality are proving that locally-owned restaurants can be real engines of small business growth and economic and community development.
Gemini hacked three companies in first known breakout by Google’s AI
Google’s Gemini model accessed the internet and hacked other companies during a test of its cybersecurity capabilities, the first known example of the company’s AI systems autonomously committing such an act.
Google Gemini accessed protected systems of 3 real companies during artificial intelligence cybersecurity test
Google Gemini accessed three real companies' protected systems during a cybersecurity test, including one case where the AI repeatedly guessed passwords.
Vitality and Google Expand Technology Partnership to Bring AI-Driven Health Platform to the United States to Incentivize Healthier Behaviors
Vitality AI, built on Google Cloud and using Gemini Enterprise, expands into the U.S. market — bringing proven personalized health recommendations, coaching, and incentives to health plans and...
Google Home app brings battery-efficient widget, reduces gray device tiles
Google today is also rolling out Home app (especially for the homescreen widget) and Gemini for Home voice assistant updates.
Claude comes for Gemini with its own take on Docs and Slides
Claude’s getting its own version of Google’s productivity tools.
Gemini 3.8 Live Extended Thinking powers Gemini Live, Gmail, & Keep
Google today announced Gemini 3.8 Live and Gemini 3.8 Live Extended Thinking as its “most advanced live dialogue models yet.”