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Boeing refines aerodynamic simulations using world’s most powerful open-science supercomputer
Exascale computing at Oak Ridge National Laboratory helps Boeing predict icing and turbulence effects on high-lift wings
How to win friends and throw people: Judo’s big impact on politics
Teddy Riner wants to be president of France. If he wins, could he be the one to get through to Vladimir Putin?
Alice Huang tied to Nvidia’s biggest Chinese competitor
The woman with whom Jensen Huang reportedly had a relationship also owned stakes in companies that provided computing power to Huawei, a key Nvidia competitor.
Alphabet's Cloud Business Doubled, but the Stock Plunged. Here's What a $5,000 Investment Could Be Worth by 2030.
Alphabet is spending big to build out its cloud computing platform.
Alphabet's Cloud Business Doubled, but the Stock Plunged. Here's What a $5,000 Investment Could Be Worth by 2030.
Alphabet is spending big to build out its cloud computing platform.
Witkoff and Kushner meet Putin in Moscow for Ukraine talks
(CNN) — Talks between President Donald Trump’s son-in-law Jared Kushner, US envoy Steve Witkoff and Russian President Vladimir Putin are underway in Moscow, after negotiations in Abu Dhabi aimed at ending the Ukraine war concluded without a breakthrough.The US envoys were greeted at Vnukovo Airport on Thursday by Russian presidential special envoy Kirill Dmitriev, the CEO of Russia’s sovereign wealth fund.The trip comes after two days of talks in the UAE capital between Russian, Ukrainian and American negotiators – the first of their kind since February – which Kyiv said made progress on some issues, but did not result in a clear resolution.Speaking after Wednesday’s talks, which lasted less than two hours, Kushner said the purpose of the Moscow meeting was to finalize plans for more humanitarian relief and the release of prisoners, as well as to push to “get Europeans added to the format so we can make this a multilateral format.”While Ukraine has long argued that Europe should be at the main negotiating table, Moscow has so far resisted such efforts.Russian Foreign Minister Sergey Lavrov criticized the presence of European officials in the UAE city earlier this week, saying their involvement was not a “promising approach.”Meanwhile, when asked if there had been any contact between the Russians and any EU representatives in Abu Dhabi, Kremlin spokesperson Dmitry Peskov said Wednesday, “No such contacts were planned, and none took place.”Kushner – who acts as one of the country’s top diplomats despite holding no official role within the Trump administration – and Witkoff are also expected to travel to Dublin to meet with Ukrainian President Volodymyr Zelensky and discuss ideas from the Abu Dhabi talks.Kyiv faces drone attackDespite the flurry of diplomacy, the latest push for peace has not afforded Ukraine respite from the daily barrage of Russian attacks.Moscow launched a ballistic missile and drone attack on Ukraine overnight into Thursday, according to the Ukrainian Air Force. In Kyiv, at least one person was killed, and several others injured amid a Russian drone attack Thursday morning, local authorities said.Zelensky also said Thursday that Starshield – a military-grade version of Elon Musk’s Starlink service that is critical to Ukraine’s military operations – has faced “significant disruptions” in recent days, hampering Kyiv’s ability to shoot down Russian drones.“These are not random errors but systematic speed restrictions. For us, this primarily means a significant complication in intercepting Russian jet-powered drones,” Zelensky posted on X. “We are trying to establish all the circumstances with the American side.”In October too, Ukraine said speed limits were imposed on Starshield satellite terminals, as the relationship between Kyiv and Washington deteriorated. Trump denied approving that decision, while Musk said the Trump administration was behind the restrictions.This story has been updated with additional developments.The-CNN-Wire™ & © 2026 Cable News Network, Inc., a Warner Bros. Discovery Company. All rights reserved.
Witkoff and Kushner arrive in Moscow for Ukraine talks with Putin
President Donald Trump’s son-in-law Jared Kushner and US envoy Steve Witkoff have landed in Moscow on Thursday ahead of talks with Russian President Vladimir Putin aimed at ending the Ukraine war.
Trump envoys to meet Putin in Moscow after latest Ukraine talks end without a breakthrough
US President Donald Trump’s son-in-law Jared Kushner said Wednesday he and Middle East envoy Steve Witkoff would travel to Moscow tomorrow after trilateral talks in Abu Dhabi ended without a clear breakthrough.
MIT Schwarzman College of Computing welcomes seven new faculty for 2026
The MIT Schwarzman College of Computing welcomes David Bruns-Smith, Zana Buçinca, Alexandra Cabral, Alexandru Damian, Lindsey Raymond, Jas Brooks, and Hengyun (Harry) Zhou to its faculty for 2026.
PSI is trying to make a business out of AI-driven scientific discovery
PSI used an unreleased Anthropic model to answer a fundamental question about quantum computing that scientists have pursued for nearly two decades.
Women in Computing Science help students explore coding with a new clinic
Coding and computer science attract students from all backgrounds. But some students finding their place in st...
Why US Business Deals Are Unlikely to Get Putin to Give Any Ground
Discover how Trump’s administration shifted its stance on Putin and Russia with potential dealmaking amid Ukraine tensions.
QCi Reports $9.5 Million in Quarterly Revenue as Acquisitions Expand Commercial Business
Quantum Computing Inc. reported third-quarter revenue of $9.5 million, exceeding its first-half total, and ended September.
Cerabyte to Address Future of Data Storage for HPC and AI at SC26
Cerabyte Joins Los Alamos National Laboratory to Discuss Storage Innovation SANTA CLARA, Calif. and CHICAGO, Nov. 9, 2026 — Cerabyte today announced that it will join experts from Los Alamos National Laboratory for a panel discussion at Supercomputing 2026 (SC26). The experts will examine how industry consolidation, limited investment in long-cycle research/development and growing AI […]
Why Dalhousie launched six new computer science majors
As AI, cybersecurity and other fields reshape computing, Dalhousie has launched six new majors designed to give students deeper expertise and clearer pathways into emerging careers.
A quantum breakthrough makes light behave like both a solid and a liquid
Scientists have observed a bizarre quantum state of matter that combines the structure of a crystal with the frictionless flow of a liquid, all at room temperature. The breakthrough overturns a major practical limitation of exotic quantum experiments and could help bring powerful new light-based computing and sensing technologies closer to reality.
Putin Seizes Three More Japan Tobacco Businesses, Bringing Takeover to $3.7 Billion
Vladimir Putin has seized control of three more Japan Tobacco International businesses, bringing the total to seven subsidiaries valued at up to $3.7 billion.
African-linked tankers caught in US blockade as Trump-Putin’s 1.8 million-tonne Russian diesel deal faces rejection by US buyers
At least four tankers carrying Russian diesel have been caught up in the uncertainty surrounding US President Donald Trump’s agreement with Russian President Vladimir Putin to release more than 1
Meet the founder who helped build Nvidia’s early cloud computing infrastructure before leaving the $5 trillion tech giant to bring AI computing power to Africa
After helping build NVIDIA’s early cloud computing infrastructure, Alexander Tsado left a successful career at the $5 trillion technology giant to found Udu Technologies
Unreleased Anthropic model helps solve major quantum-computing problem
An unreleased Anthropic model helped solve a longstanding problem in quantum computing — another major math problem solved with AI.
Cerabyte Completes S3 Integration Testing in Collaboration with CERN openlab
EIC-supported project demonstrates ceramic data storage within established object-storage workflows MUNICH, Nov. 6, 2026 — Cerabyte, a pioneer of ceramic-based data storage solutions, has announced successful S3 integration testing of its ceramic data storage technology in collaboration with CERN openlab, CERN’s public-private partnership for accelerating computing innovation for science. Supported by the European Innovation Council’s EIC […]
Vladimir Putin calls out nations opposing Russia’s return to international sports
Several countries have said their national teams will not play against Russia following a decision by sport’s highest court to readmit Russian teams to international ice hockey.
WATCH: First lady Melania Trump hosted Russian and Ukrainian officials. Trump met with Putin's envoy
President Donald Trump said he met with Vladimir Putin’s envoy at the White House on Thursday but did not meet with Ukrainian officials who were also visiting.
Midterm AI data center backlash could help Amazon, Microsoft, Google
The AI data center backlash could strengthen Amazon, Microsoft, and Google by limiting competition and driving up cloud computing prices.
A tiny squeeze dramatically changes the magnetism of exotic materials
Scientists have discovered that a simple mechanical squeeze can dramatically alter the magnetic properties and electrical behavior of exotic materials called altermagnets. The surprising breakthrough could open the door to faster, more energy-efficient electronics and new technologies for quantum computing.
Jülich Hosts EVE Language Model for Earth Observation and Earth Science
Nov. 3, 2026 — The Jülich Supercomputing Centre (JSC) has started a new collaboration with Pi School, an international centre of excellence for AI headquartered in Rome that brings together innovators, thinkers and entrepreneurs. As a result, JSC’s Helmholtz Blablador infrastructure now hosts the EVE-Instruct model, a large language model (LLM) for Earth observation (EO) and […]
Beyond Programming: Students Explore Research and Build Connections at Faculty Roundtable
What should computer science (CS) students learn in the age of AI? How does philosophy intersect with computing? And what does CS research look like beyond programming? Those were among the topics students explored at Stony Brook University’s Computer Science Faculty Roundtable on September 30. Organized by the Department of Computer Science, the event brought
OpenAI: ChatGPT Search Is About 20% of Google’s, 250 Million People Speak With ChatGPT, and Chat Is Merging With Work
OpenAI says ChatGPT growth is accelerating. The company added 200 million users over just the past two months, jumping from 1 billion to 1.2 billion weekly active users in 60 days. Its search volume, it says, is approximately 20% of Google’s. Its users create 1.5 billion images with it per week. And its voice mode, between dictation and speaking with ChatGPT, now has 250 million combined weekly active users. ChatGPT consumer product lead Adam Fry told me this during a brief fireside chat at an OpenAI-hosted dinner in New York City on Monday night. “The growth is just going big,” he said. During our conversation, Fry also revealed plans to merge ChatGPT’s Chat and Work modes (so that confusing toggle up top will soon go away), agreed with feedback that ChatGPT-created restaurant signage looks too uniform, and addressed the cost to serve its personal assistant Dots. Here’s a look at our full conversation, lightly edited for clarity and length. We also cover image generation, interactive interfaces, advertising, education, and public unease about AI: Alex Kantrowitz: OpenAI has analyzed ChatGPT’s search volume and compared it with Google’s. What did you find? Adam Fry: ChatGPT has been growing quite quickly. ChatGPT’s volume of search-like queries is now roughly one-fifth the volume of queries of Google. It’s a pretty amazing statistic when you think about it, mainly because we started just about a year and a half ago. It’s hard to remember this, but ChatGPT at a time didn’t have any up-to-date information. You’d ask a question, it would say something like, “It’s past my training date,” and nobody had any idea what that meant. You’re saying ChatGPT’s search volume is one-fifth of Google’s. Bing’s share of the search market is around 5%. Does that make you multiple times larger than Bing? To say that more directly, ChatGPT has gone from one billion to 1.2 in just the last two months. That’s roughly 100 million users in 30 days, and another 100 million in another 30 days. The growth is just going big, and we’re staying very focused on how fast ChatGPT is growing. What counts as a search? I might have 20 prompts back and forth in a ChatGPT conversation. Does each one count as a query? Story continues below… A MESSAGE FROM OUR SPONSOR Move upmarket without a rebuild Moving upmarket means saying yes to what enterprise buyers ask for first: SSO, directory sync, and audit logs. WorkOS gives you all three, so your team keeps building the product instead of the checklist. Get enterprise-ready. Continued from above…. Adam Fry: What people came to ChatGPT for to start, if you go back about two years, is really writing help and coding help. Those wouldn’t be things that you really search for. What we’ve seen over the last year is people have grown into all these other topics. Things like you’re looking up a person or people or a place or a thing, and it requires up-to-date information. That’s a key element. Another big part is finance questions or health questions, where you really want to make sure it’s up-to-date information that’s quite factual, and we’ll do a web search for those questions. It’s basically anything that a user would have traditionally gone to a traditional search engine for, but is now deciding to go to ChatGPT for. Does each conversation with a search embedded count as one search, or does every question that requires a web search count? Every time you ask a question and go search the web, that’s a search. You used to work at Google. How would you say the nature of search is changing? One of the things that people really love about ChatGPT is this personalization and this understanding of the broader journey. If you look in the past, people would go for a Q&A box, and you’d ask a one-off question, like how tall is the Eiffel Tower. But there’s so much context in our lives around, “Maybe I’m taking a trip to Paris.” It’s actually helpful for ChatGPT to respond, “Here’s how tall the Eiffel Tower is, but I know you’re going on a trip to Paris.” It’s that ability for people to actually maintain context over time that has led them to start using ChatGPT in this way. You’ve also announced 250 million weekly active users of voice input, including dictation and conversations with voice mode. We are at 250 million weekly active users. What’s amazing about this is, if you look at those 250 million, what we’re seeing is it’s actually also incremental to the product. It’s no longer just a novelty. People aren’t just trying out voice. We’re seeing them use voice input day to day in their daily life, and it’s grown so quickly. Those 250 million users are using voice input, and that could just be a quick question or a long conversation. We’re seeing both behaviors. We’re seeing people just not want to type. You just hold it and start talking to it, or it turns into a longer conversation where people just sit with it. The big reason for this is the frontier capabilities of our models that we’ve shipped. We shipped what we call GPT Live 1, and then we shipped GPT Live 1.1. The big change with this is that it’s much more natural to speak with. It’s something we call backchanneling and interruptibility. Mhhhm… What you just did there is what the model does, because it’s really good at having a bidirectional conversation. That push to the frontier—we were the first to really release this at scale—has made it so much more natural for people to chat. That’s still only about 20% of the user base. From the outside, that looks small. Why is it exciting from the inside? It’s very exciting for a few reasons. We see from the data that when people start using voice, they stay around using voice. One of the things, transparently, we can do a better job of is bringing more people into discovering it. We see that the number of people who discover it could be a lot higher. But once they do, it’s a really new way for people to interact with AI. We’ve made a lot of improvements to it too, where we’re just going to see the number go up. You have background mode, so now by default, if you close the app, it’ll show, “Hey, the conversation’s still going.” If you want to walk around the house or you’re putting away the groceries, you can still have a chat. That kind of continuity has also changed how much people use it. Dots launched on September 29 and is about a month old. How’s it doing? It’s doing great. We talk to people about their Dot, and the first thing that everyone says is, “I love my Dot.” One of the reasons we’re seeing this is the proactivity, its ability to reach out to you and figure out things. The biggest use case I see all the time is, “Did I forget something today that I was supposed to do?” You can just say to your Dot, “Remind me of things at the end of the day that I may have forgotten. Somebody emailed me or messaged me,” and it’ll do that. This Dot changes the paradigm of the relationship with AI, where it’s actually helping you and giving you time back. You’re more conservative with permissions than some competitors. I tried to give my Dot my credit card, and it wouldn’t take it. With the creator of OpenClaw in-house, why are Dots’ permissions more limited? This is a space where we want to be very thoughtful and conservative because it’s a new space where you’re entrusting your Dot to take care of things on behalf of you that matter and are meaningful. We’ve just talked about how you go to this 1.2 billion user growth and asking questions. Now we’re asking people, “Can you ask ChatGPT or your Dot to help get things done?” But when you ask it to help get things done, it’s a whole new level of trust. We’ve built up a tremendous amount of trust in giving the right answers to your questions, and we want to make sure that when we build this new product, it has a high degree of trust. It’s a long game. We think this is going to be a new market for a very long time, and we want to make sure that we’re being this trusted player in the market to start. I initially thought Dots would be available on my $20 plan, then realized I had to spend $100 a month to get it. I upgraded, but I’d like to downgrade at some point…. We hope to keep earning your business and make the value good enough. It’s a really good point. ChatGPT’s goal more broadly, which I think is unique for us in the market, is we are focused on bringing intelligence to everyone safely across the entire world. For Dots in particular, there’s no difference. We don’t have anything to share tonight about the date of when Dots will go to a broader set of users. But you can rest assured we’re working very hard, and there are a couple things we have to get right. Over time we’re going to work on more efficient, agentic models that are able and capable of doing things for you. The first one is, right now we wanted to make sure that your Dot is actually really helpful. We started with some of our best models, but over time we’re going to work on more efficient, agentic models that are able and capable of doing things for you. The second thing is around proactivity. We’re still dialing in: How much do I message you to help you get things done? We can always play around with that to understand how much usage should a user use to have a successful Dot. We’re going to work on both, and our goal and ambition is to bring it out to all users. How much background computing does a Dot require? I’ve read estimates that serving a $100 subscriber might cost OpenAI $3,000 in compute, and mine seems to read every email that arrives. How often is it checking connected data sources? Right now, Dots are on the compute-heavier side, and this is one of the things we want to make sure that we can dial back over time. But the goal is to give you the best product. This is our premium, always-on personal assistant. Once we nail that, we’ll figure out how to dial back the compute needs. The thing we’re seeing also from our models, as we train frontier models, is just how—I can’t even keep up with how quickly we have a new model, and each new model is more efficient. If you looked at 6.1 Sol when it came out relative to Astra, much more efficient. I’m very excited about the next set of models we have coming too that’ll be even more efficient that we can use. Elon Musk said the cost of serving Grok Bot was unsustainably high relative to revenue. Is efficiency the key to winning against competitors, or is there more to it? There are a couple of factors that go into it. One is the cost side, but the other is the revenue side. On ChatGPT and on our consumer business, we’ve been extremely proud of our ability to diversify different revenue streams in a way that’s growing really fast. To give you another stat, we announced several months back that we had passed 50 million subscriptions, and the number has grown considerably since then. On ads, within 200 days, we reached more than a billion in ARR, and that business is growing extremely quickly. We also have credits. Rather than paying for subscriptions, users can buy more credits. Between credits, subscriptions, and ads, I’m confident that the value of a Dot is going to be able to pay for itself. Elon has subscriptions too. This is clearly an expensive product to serve. One thing we continue to bet on at OpenAI and ChatGPT more broadly is that the models will continue to get much better and much cheaper, way faster than any of us think. If you look back at the historicals on it, it continues to be true. From what I’ve seen, I see no slowing down of that. What is Meta doing right with Muse? It has spent a lot of time atop the App Store charts. There are a couple of things they’ve done well. The first is people have been craving simplicity in their products. One of the things we have stated publicly as well is, at times AI products can be too complex. We’ve announced we’re merging ChatGPT’s Chat and Work. We’re making it much more simple in the products. You don’t have as many toggles or sliders between efforts and different model pickers. One of the things they’ve done well is this simplicity. You just go in and you can ask a question. You don’t have to think too much about what you’re doing. That’s one thing that seems to have resonated with users, which we also believe in. Second, there’s something really nice about being able to access your agent across many different channels. We just announced and launched Dots on iMessage. Whether you talk to your Dot on email or iMessage or within the ChatGPT app, the ubiquity in getting your assistant out there is going to be key. Those are two things that, if I had to be nice to our competitors, I would say were two big ones. So Alexandr Wang’s meme-tweeting strategy isn’t something you’re going to emulate? I have a different tweeting style. ChatGPT still asks me to choose between Chat and Work, even though some people call this technology AGI. Will that distinction go away? Couldn’t agree more. We have to solve this. Asking users to choose — we’ve heard repeatedly from all of our users it’s too much to understand. When should you go to Chat? When should you go to Work? It’s solvable. There’s real research to solve to make sure the models can understand what your intent is. Are you asking for a fast, quick reply? Are you asking me to go do a long agentic task? We’re going to solve it, and the plan is to bring Chat and Work together. So one interface will work out whether I’m asking for a conversation or a task. When? Coming soon. You told me the majority of ChatGPT’s 1.2 billion users are using image functionality. What are they doing? Today, 1.5 billion images are created per week. It’s hard to even wrap my head around that number. They’re doing a wide range of things. One of the most interesting things they’ve started to do is really use it for productivity use cases. The first thing it started with—I don’t know if you saw the ’80s trend, but it was quite fun. You could take a photo of yourself and say, “Put me and my family in ’80s outfits.” I did this for my family, and it was a really fun poster. But what we’re also seeing is businesses for their menus, or posters for birthdays. People are using it almost in a productivity-like fashion to create visual diagrams of what they would like to see. We’ve actually brought the latency down on images as well, so it’s gotten a lot faster. It went from a very high number of seconds to getting much faster. As it comes down even further, the number of images people are creating and the number of use cases is going to grow a lot. My feature request: When a restaurant makes a menu or sign, prompt it for more specificity so it doesn’t look like every other AI-generated restaurant sign. Or offer a shuffle button for a different style. I agree with the feedback, and we’ll take it to the team. I’m seeing ChatGPT generate little interactive interfaces within its answers. What’s the direction for Intelligent UI, and how are people using it? I am extremely excited about Intelligent UI. If you go back a couple years, these chat apps were all text, and now that’s changing. They’re going to be visuals. Intelligent UI and our first attempt at this with GPT-6 Intelligent UI is really just the beginning. As you go to the extreme, what you can imagine is, let’s say you’re using voice mode, and you just say, “Show me how offside works.” Immediately it should show you a visual diagram, and you say, “No, I don’t quite understand it.” It should just repopulate. That’s where the future of this technology goes. It’s almost like you’re talking to a canvas that will update and change. People are finding it helpful for a lot of things. My favorite one is—it’s hard to admit that I had no idea how hearts work. The last biology course I took was in high school. I just asked it, “How does the heart work?” and it instantly gave me a beautiful interactive diagram of where blood flows and how it goes through. I’m really, really bullish on this for learning in particular. You’ve described useful applications in finance, health, and learning, and ChatGPT has 1.2 billion users. If people are getting so much value, why is public polling on AI so negative? The reality with AI is it is going to change the nature of how work gets done. For a lot of people, that is something that can feel scary at times, and we’ve all felt that. But I remain confident that when people actually use the product and they use AI, it very quickly changes how they perceive it. We’re at 1.2 billion, but there are still a lot of people who haven’t used it. The more people use it, get familiar with it, how it can actually help you in your life, it’s going to feel a lot less scary. Big Technology is a reader-supported publication. To receive new posts and support our work, please consider becoming a paid subscriber. Thanks!! Subscribe
Elon unites engineering, computer science in new school
Elon University officially celebrated the launch of its new School of Engineering and Computing on Oct. 30, a milestone in the university’s continued growth and commitment to preparing students for
The Missing Engine Behind Quantum Computing Is Literally All Around Us
Scientists say they can corral up to 40 quantum-ready photons from everyday light, even at room temperature.
'Jiuzhang 4.0' wins leading science and technology award at 2026 WIC
A total of 20 projects, including the "Jiuzhang 4.0" programmable photonic quantum computing prototype, won the 2026 World Internet Conference (WIC) Leading Science and Technology Awards at a ceremony held on Monday in Wuzhen, east China's Zhejiang Province.
Pharma giants pay millions to Russian distributor entangled in corruption allegations to win cancer drug business
Confidential whistleblower reports allege that Western companies offer huge bonuses to Russian middlemen, inflating prices for lifesaving treatments at the expense of patients.
Oxford Science Enterprises targets UK’s first trillion-pound deep-tech company
Recent landmark exits in quantum computing and biotech show that the OSE’s model of building companies around university research, recruiting entrepreneurial leaders and providing patient capital produces results. The next step is taking the model global.
AWS will be a $1T business, Jassy says. But first, it will spend big.
Amazon wants to be the king of AI cloud computing and become the first $1 trillion revenue business. But to do that it will have to continue on its capex spending spree, its CEO said Thursday.
AWS will be a $1T business, Jassy says. But first, it will spend big.
Amazon wants to be the king of AI cloud computing and become the first $1 trillion revenue business. But to do that it will have to continue on its capex spending spree, its CEO said Thursday.
Amazon earnings fall short as retail revenue lags
Amazon missed earnings estimates on Thursday, as less-than-impressive sales growth in its retail division undermined strong cloud-computing revenue. Though the bulk of Amazon’s $201.9 billion revenue from July through September
Adobe vs. International Business Machines: Which Technology Stock Is a Better Buy in 2026?
IBM is growing steadily with software leading the way and a major quantum computing investment on the horizon. Adobe faces serious questions about whether AI will erode Creative Cloud’s dominance despite continued strong cash flow.
Tandy Warnow honored at American Academy of Arts and Sciences celebration
Tandy Warnow, CS professor and associate director at the Siebel School of Computing and Data Science, was celebrated for her recent induction into the prestigious American Academy of Arts and Sciences on October 14 at a ceremony on the University of Illinois Urbana-Champaign campus. Warnow was elected for her excellence and continued accomplishments in the fields of discrete mathematics, probability theory, computer science and data.
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
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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.
A quantum computing feat brings the ‘megaquops’ era a step closer
Error correction creates qubits that glitch only 5 out of a million times, the first step toward a new phase for quantum computing.
Trump’s Push for Deals With Russia Powers His Frustration With Ukraine
The series of recent steps by the president that seem to favor Vladimir Putin reflect his intensifying desire to normalize the relationship with Moscow even if it means further undercutting Ukraine.
Infleqtion Convenes Business Leaders in London to Chart Quantum Commercial Value
OXFORD, England, October 19, 2026--Infleqtion (NYSE: INFQ), a global leader in quantum computing and quantum sensing powered by neutral atom technology, will host the first Quantum Application Forum, an exclusive one-day event convening senior business and technology leaders on 3 November 2026 at IET Savoy Place in London. Executives responsible for technology, innovation and investment decisions will explore how quantum technology can address critical challenges, unlock commercial value and dri
Putin proposes summit with Trump and Xi to discuss Ukraine deal, WSJ says
Oct 18 (Reuters) - Russian President Vladimir Putin has proposed a summit next month with US President Donald Trump and Chinese leader Xi Jinping to negotiate an end to the four-year-old Ukraine
WVU Parkersburg launches Cloud Computing program connecting technical skills to business needs
PARKERSBURG, W.Va. — WVU Parkersburg is launching a new Cloud Computing program designed to prepare students to build and manage cloud resources while understanding the costs and business decisions behind
Trump’s Overtures to Putin Put Republican Candidates in Hot Seat
GOP politicians in a number of tight races are breaking with Trump on Russia policy ahead of midterms.
U.S. taking Putin’s side would be a ‘historic mistake,’ says Zelenskyy
The Ukrainian leader told NBC News’ “Meet the Press” that Ukrainian officials believe the decision to limit Starlink, imposed by Elon Musk’s SpaceX, came from the U.S. government.
Is International Business Machines (IBM) A Bargain Or Fully Priced?
International Business Machines has delivered a strong multi year share price run. With the stock recently at US$232.14 and cash flows in focus, the live question is whether that price still lines up with what the business can earn and return over time. Over the past 5 years the share price has gained 130.1%, which puts a lot of weight on the idea that IBM’s future cash flows can justify a much higher valuation than in the past. Fresh commitments in areas such as quantum computing and...
Scientists who discovered a third form of magnetism win major physics prize
Physicists have uncovered a hidden third form of magnetism that challenges more than 100 years of established physics. Called altermagnetism, the discovery could transform future computing and electronics while opening an entirely new frontier in quantum research.
Intel "Razor Lake-AX" to Pack 700 TOPS of AI Compute
Intel's senior vice president, Cho Min-sung, announced at the Semiconductors Powering Humanoids summit in South Korea that the next-generation "Razor Lake-AX" platform will be a computing powerhouse, featuring 700 TOPS of AI compute on die. This means that at INT8/FP8 precision, "Razor Lake-AX"...
Trump's decision to waive Russian sanctions outrages Congress, but no quick counter seen
Lawmakers from both parties blasted the executive order late Friday, the latest in a series of steps that the Republican president has taken drawing the United States closer to Russia's Vladimir Putin.
European Open Science Cloud opens first 13 research hubs
European Open Science Cloud hubs begin operations, expanding access to research data, digital services and computing resources across Europe.
Here's How Microsoft's $175 Billion in Capital Expenditures Is Hurting Its Business
Microsoft cloud computing business may not be as profitable as its other segments.
NVIDIA Denies RTX 5090 Discontinuation Rumors, Admits Supply Is Tight
NVIDIA has finally responded to reports that the GeForce RTX 5090 is being discontinued. Jeff Fisher, senior vice president of NVIDIA's personal computing business, told Taiwanese media after the RTX Spark launch party in Taipei on October 16 that the rumors aren't true, as reported by 4Gamers...
Trump's Diesel Deal With Russia Won't Lower Gas Prices for Americans
Donald Trump's diesel deal with Russia is a big favor to Vladimir Putin, but it won't lower prices for Americans ahead of the midterm elections.
Rep. Ilhan Omar challenges legality of Trump administration search of her home
Democratic Rep. Ilhan Omar is fighting back against the Trump administration’s search of her home earlier this week – disputing its legality and demanding the return of her property seized by federal agents.
NVIDIA Joins European Commission Initiative to Expand AI Resources for Science
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QuEra Scientists Named Co-Principal Investigators on Three of Nine Teams in Department of Energy's Quantum Chemistry Program, Now Underway
/PRNewswire/ -- QuEra Computing, the leader in quantum computing, today announced that its scientists will work with three of the nine teams selected for the...
Companies pledge free software and computing for AI in science
The European Commission has announced that a group of companies and foundations will offer free services, such as software or access to supercomputers, to European scientists using AI in their research.Unveiled on October 15 at a conference in Dublin, the initiative is the latest step by the Commission to try to help researchers use the technology. Scientists have used forms of AI, such as machine learning, for decades. But advances in deep learning in the past decade, and the wider interest in large language models since the release of ChatGPT in 2022, have spurred governments to invest in new AI tools that could help the scientific process.In July, the US announced it is spending $5 billion on the Genesis Mission, which it describes as a “national effort to harness AI for science,” funding 278 projects across areas such as autonomous labs or new AI tools in astronomy. The Commission has also identified AI in science as a priority, but the €107 million it pledged from Horizon Europe in 2025 does not come close to US spending levels. That said, the vast majority of public research spending in the EU comes from national funds. Today’s announcement at the European AI in Science Summit doesn’t commit any new public money. Instead, it launches the AI in Science Enablers Initiative, a set of voluntary contributions from companies and foundations. The idea is that the companies also benefit by getting access to scientific ecosystems, and feedback on how their tools work. Google, for example, has put up €3 million of cloud computing credits to European researchers. The Novo Nordisk Foundation intends to make its supercomputer, Gefion, available to European researchers, according to the Commission statement. The chipmaker Nvidia said it would provide access to tools, compute, training and collaborative projects.The publisher Elsevier also offered complementary licenses to some of its AI tools, plus AI training for PhD, postdoctoral and master’s students. The French AI firm Mistral will provide the European research community with access to its open-weight models, Vibe seats and application programming interface credits, according to the Commission’s statement. Vibe is one of the firm’s chatbot models. The idea is that more firms and foundations will come on board in the future as the initiative grows.
Virginia’s Fairfax County Public Schools builds bridges to quantum computing
The district is installing a quantum computer at one high school that will be usable in early 2027 and has built local partnerships on curriculum.
UNCP Computer Science Program earns ABET accreditation
Photo by UNCPUNC Pembroke’s Bachelor of Science in Computer Science program has earned initial accreditation from ABET’s Computing Accreditation Commission, providing independent recognition of the quality and rigor of the program as it prepares students for careers and advanced study in computing.The accreditation, which extends through Sept. 30, 2032, comes as UNCP continues to expand its computing programs amid rapid growth in artificial intelligence, cybersecurity, data science and other emerging technologies.ABET accreditation is a voluntary, peer-reviewed process that evaluates academic programs against established standards for quality. The review examines curriculum, student outcomes, faculty qualifications, assessment practices, student support and continuous improvement.“Earning initial ABET accreditation is a significant milestone for our Computer Science program because it provides external recognition of the quality of our program and validates our commitment to continuous improvement,” said Dr. Haitao Zhao, assistant professor in the Department of Mathematics and Computer Science, in a press release. “For our department, this achievement reflects the hard work and dedication of our faculty and staff and gives our students greater confidence in the quality of their education and preparation for careers and graduate study.”For prospective students, the accreditation provides independent assurance that UNCP’s program meets standards established for computing education and is preparing graduates to enter technical fields in the global workforce.Earning the accreditation required faculty and staff to conduct a comprehensive review of the program, including its curriculum, educational objectives, student learning outcomes and assessment processes. The review included a detailed self-study, documentation and an on-site evaluation.The accreditation arrives as UNCP looks toward the next phase of growth in computing. The department plans to continue strengthening its computer science curriculum while expanding hands-on learning and research opportunities for students. It also plans to pursue ABET accreditation for its information technology and cybersecurity programs.Future plans include developing new opportunities in artificial intelligence and graduate education, including an AI track and a Master of Science in Computer Science with a focus on AI and cybersecurity.
NVIDIA and Danaher are working to link AI with lab experiments. The initial work focuses on select instruments.
Danaher is working to combine lab instruments, automation, software and data with NVIDIA computing and AI; work could extend beyond select instruments.
Senators Urge Trump to Reverse Diesel Deal With Putin
A bipartisan group of 12 senators called on the president to drop his decision to ease sanctions on Russia’s exports of diesel fuel, saying the deal would help Moscow continue the war in Ukraine.
Georgia Tech secures $5m Google.org commitment for AI science | ETIH EdTech News
Georgia Tech has received $2.25 million toward a $5 million Google.org commitment to expand AI research access across US institutions. ETIH’s edtech news coverage details plans for 15 research projects, shared computing tools and training for more than 300 researchers and faculty.
Trump's diesel deal with Putin draws bipartisan criticism from a frustrated Congress
The deal is being criticized by both Republicans and Democrats as fueling Russia's war against Ukraine, violating existing U.S. laws and drawing the U.S. closer to Russia.
Bipartisan group of senators call on Trump to rescind Russian diesel deal
Six Democratic and six Republican senators wrote a letter calling on President Trump to withdraw from his recent deal with Russian President Vladimir Putin.
FSU physicists receive funding through Department of Energy's $7.3M Quantum Technology Outposts research initiative
Three Florida State University theoretical physicists are part of a major national initiative exploring how quantum computing could help answer
National Compute Grid Launches to Pool Idle AI Capacity Across Providers
For all the talk about a shortage of AI computing power, and the need for more powerful chips and bigger data centers, there’s another side to the story. A significant amount of existing capacity isn’t being used. While tech companies race to build massive new data centers, expensive GPUs in other facilities can sit idle […]
Scientists Build a World-First DNA Computer That Runs Without Continuous Power
Reusable for up to 25 different calculations, a DNA computer points toward possible uses in energy-efficient computing and long-term data storage.
Trump\'s diesel deal with Putin draws criticism from a frustrated Congress
The deal that President Donald Trump reached with Russian President Vladimir Putin to import tons of Russian diesel is drawing concern from Congress. Trump struck the deal as a way
AI behaves more like a brain than a database – cognitive science’s role in its origin story helps explain why
When people think about what ChatGPT or other large language models are doing behind the scenes, it’s common to reach for the language of computing. You might assume there is
Trump’s Russia diesel deal aids Putin’s war, Europe warns
European officials warned that President Donald Trump’s deal to increase Russia’s diesel sales will boost Moscow’s war effort in Ukraine, pressing for more U.S. sanctions instead. “We would call on
Novel material’s magnetism could improve electronics’ speed, efficiency
A material thinner than a human hair may foster faster computing devices that demand less energy and store more data, according to a multi-institutional team led by researchers in the Penn State College of Earth and Mineral Sciences.
FSU computer scientists appointed National Science Foundation program director
A Florida State University researcher is helping guide national research priorities in artificial intelligence, advanced computing and data systems in his
Kelly Clarkson ‘Pretty Woman’ Drama Explained As She Slams Store’s Response
The singer accused a Brooklyn store of treating her like Julia Roberts in 'Pretty Woman.' Now both sides are disputing what happened.
Trump’s cost-of-living politics just rescued Putin’s war chest
A fuel shipment gave Trump the pre-election boost he craved – and made Kyiv’s best tactic newly dangerous.
Trump’s US-Russia diesel deal draws bipartisan criticism
President Donald Trump’s diesel deal with Russian leader Vladimir Putin is facing bipartisan blowback in the US as lawmakers warn that the Trump administration is defying a federal law meant to crackdown on Russia after the invasion of Ukraine.
UK does not support Trump's Russia diesel deal, says Streeting
The defence secretary says the US and its allies need to increase pressure on Vladimir Putin to end the war with Ukraine.
Is International Business Machines (IBM) Stock Worth More Than Its Cash Flow Suggests?
International Business Machines has almost doubled shareholders' money over the past 5 years, yet after a sharp setback this year and fresh headlines around quantum computing and AI, the key issue for you is whether the current share price lines up with the cash the business is expected to generate. With IBM stock now around US$227 a share, the gap between the recent price path and its cash flow story matters more than ever. IBM has delivered a 99.1% total return over 5 years, which puts...
Trump’s $6 Billion Science Plan Targets AI, Moon and Mars
Trump unveils over $6 billion in science initiatives supporting AI, quantum computing, nuclear-powered Moon and Mars projects, and biomedical research.