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NJBIA launches litigation center to defend businesses
NJBIA and NJ Civil Justice Institute launch the New Jersey Litigation Center to protect businesses from regulatory and legal challenges.
Building an LLM-Powered Content Moderation Bot in the Classroom -
Building an LLM-Powered Content Moderation Bot in the Classroom By Shelby Grossman, Arizona State University, Anthony Mensah, Stanford University, Alex Stamos, Stanford University and Jeffrey Hancock, Stanford University As online platforms seek to improve content-moderation [...]
Building an LLM-Powered Content Moderation Bot in the Classroom -
Building an LLM-Powered Content Moderation Bot in the Classroom By Shelby Grossman, Arizona State University, Anthony Mensah, Stanford University, Alex Stamos, Stanford University and Jeffrey Hancock, Stanford University As online platforms seek to improve content-moderation [...]
FSU chemists develop light-powered method to resurrect 'dead-end' compounds for drug discovery
Throughout the drug development process, which can take a decade or more, tens of thousands of chemical compounds are produced that never go on to become
What Does a Democratic Congress Mean for Business?
A host of business- and consumer-friendly policies could be on tap as newly empowered Democrats take control of Congress, writes Michael Hardaway.
WhatsApp’s AI-powered photo expansion tool starts rolling out on iOS
Select WhatsApp users on TestFlight and in the main app are now seeing the option to expand an image in the app’s photo editor.
After taking control of Congress, Democrats vow major probes into Trump's 'tentacles of corruption'
Corruption topped the list of concerns for voters who ousted Republicans and handed Democrats control of Congress in the midterm elections. Now, the newly empowered Democratic Party is putting the issue front and center as its leaders chart their course for investigations and policymaking.
Startup bringing AI-powered robotics to the cath lab raises $160M from GE HealthCare, other investors
Laza Medical, a California-based medtech company, is developing advanced technologies for the cath lab. GE HealthCare also participated in a previous financing round in 2023.
Ray Offers Dodgers a Proven Plan B Behind Skubal
Should ace left-hander Tarik Skubal land elsewhere, Robbie Ray provides a veteran left-handed stabilizer to fortify a high-powered Los Angeles Dodgers rotation.
GE HealthCare Technologies' Taha Kass-Hout to Resign as Chief Science and Technology Officer
GE HealthCare Technologies' Chief Science and Technology Officer Taha Kass-Hout will resign effective December 31, 2026.Original SEC Filing: GE HealthCare Technologies Inc. [ GEHC ] - 8-K - Nov. 05, 2026This is an AI-powered summary. It may contain inaccuracies. Consider verifying important informa…
UK backs startup Potential Sciences' AI-powered drug hunt
UK startup Potential Sciences emerged from stealth today with a sizeable seed funding round and an ambition to transform drug discovery with AI.
How AI Is Changing the Way Business Leaders Make Decisions
BoF sits down with Bianca Anghelina, founder and CEO of decision intelligence platform Aily Labs, to explore her vision for AI-powered leadership, why better decisions are the key to business growth and what it takes to turn AI ambition into measurable impact.
Coin Theaters Completes Production Rollout of Creator-Owned Entertainment Platform $CRE8-Powered Monetization
Coin Theaters Completes Production Rollout of Creator-Owned Entertainment Platform $CRE8-Powered Monetization
Monarch butterflies get tiny trackers to help scientists follow their mysterious migration
Solar-powered radio tags attached to nearly 1,750 monarchs this year are helping provide answers to monarchs' migration routes.
This self-powered toothbrush makes electricity to kill cavity-causing bacteria
Researchers have created a self-charging toothbrush that helps fight the bacteria linked to cavities and gum disease while preventing plaque buildup.
I tried Apple's Siri Recaps. They're surprisingly useful — and a little unsettling
Apple's AI-powered Siri Recaps won me over in just one day, despite my privacy reservations. But there's still room for improvement.
AI in Life Sciences Market to Worth USD 69.34 Billion by 2031, at a CAGR of 26.3%
Delray Beach, FL , Nov. 04, 2026 (GLOBE NEWSWIRE) -- MarketsandMarkets™ projects the global AI in life sciences market to grow from USD 21.58 billion in 2026 to USD 69.34 billion by 2031, at a CAGR of 26.3% during the forecast period. Market growth is fueled by accelerating adoption of AI in drug discovery, clinical research, diagnostics, and precision medicine, along with increasing investments in AI-powered life sciences solutions and the growing availability of real-world and genomic data. Ge
Health Plan Unlocks More Than $10M in Savings in Six Months Using Cohere Health's AI-Powered Pre-Payment Review
Cohere Health®, the leading clinical intelligence and operations platform for health plans, today announced that a Medicare Advantage client identified more than $10 million in savings in six months by finding complex clinical and coding errors in high-cost drug and durable medical equipment claims before payment.
HBSE names entertainment booking chief for NJ venues
HBSE appoints Joe Giordano Jr. as senior vice president of entertainment booking to oversee year-round programming at New Jersey venues including Prudential Center.
Galaxy S27 series chipset details leak with a surprising entry for the Pro
New import-export records name the chipsets in the international variants of all four Galaxy S27 models, with the Pro being powered by an unexpected chipset.
Apple Readies Launch of AI-Powered Health App
Apple’s soon-to-be-released redesigned Health app will be powered by artificial intelligence and will provide users with tailored recommendations on
Chinese Startup Showcases Hybrid-Powered Business Jet Concepts
Chinese business aviation startup Beijing Airlink Aircraft (Bairac) has unveiled two hybrid-powered business jet concepts as it secures its first investment round.
watchOS 27.3 Brings Live Rewind and Siri Recap to Apple Watch
With the first beta of watchOS 27.3, Apple is introducing new Audio Intelligence features it previewed for the Apple Watch Series 12 and Ultra 4 earlier this year. Powered by the S11 chip, Audio Intelligence includes Live Rewind, Siri Recap, Sound Recognition, and Shazam Music Recognition. Sound Recognition and Shazam are already on the watch, but Live Rewind and Siri Recap are now available in the beta. Live Rewind can show you a transcript of the last 15 seconds of a conversation.
Remodel Health and Lively let employees choose an individual health plan and open an HSA in one connected experience
Remodel Health, a leading individualized benefits platform, and Lively, a top-rated health and lifestyle benefits provider1, known for building the modern health savings account (HSA), today announced the launch of the Remodel Health HSA, powered by Lively. Employers that administer a CHOICE Arrangement (formerly known as ICHRA) with Remodel Health can now enhance their benefits package with an HSA that connects directly to the employee plan-shopping experience.
Swift, Simmons star as Bears maul Seahawks
D'Andre Swift rushed for three touchdowns as the Chicago Bears overpowered the Seattle Seahawks 34-17 in a statement road victory over the reigning Super Bowl champions on Monday.Seattle responded with a Cooper Kupp touchdown early in the fourth quarter but Chicago's defense took over and Swift bagged two touchdowns to seal victory. rcw/pst
Citizen's Rugged Eco-Drive Field Watch Just Got a Lot More Affordable
The brand's solar-powered timepiece is at its lowest price ever thanks to Amazon's current sale.
Vera Health Secures $28.5 Million to Expand AI-Powered App for Clinicians
Vera Health raised $28.5 million in a Series A funding round to expand its artificial intelligence-powered clinical app, the company said in a Tuesday
Japan Just Powered Up a Quantum Computer Built From Single Atoms—and It May Not Need a Giant Freezer to Run
Just how big could the quantum leap really be?
Fox Doesn’t Want To Talk About Roku, But It’s Got Lots To Say About Tubi And Politics
Tubi, sports and political ads powered Fox’s growth this last quarter, even as Fox News lost viewers and the Roku deal awaits DOJ clearance.
Trust Stamp announces presentation on Company’s progress and 2027 Business Plan
ATLANTA, GA, Nov. 02, 2026 (GLOBE NEWSWIRE) -- Trust Stamp (NASDAQ:IDAI), a global provider of AI-powered software
NJIT project aims to tackle mental health issues in construction industry
NJIT partners with IUOE Local 825 to launch a mental health program to connect construction workers with support and resources.
Retired Olympian Ariarne Titmus is changing her all-or-nothing outlook
Four-time Olympic gold medalist Ariarne Titmus says retirement led her to rethink the mindset that powered her swimming career.
Oregon: Big Coaching Domino Just Fell for Offensive Coordinator Job
Florida State’s dismissal of Mike Norvell creates a massive opening for Dan Lanning to reunite with his former mentor and revitalize the Ducks' high-powered offensive attack.
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,
Business Briefcase: Workshop to explore using AI virtual clinic
SCHUYLKILL HAVEN — People are being alerted to “join Schuylkill LaunchBox,” powered by Penn State, for “an informative virtual workshop exploring how small business owners can use artificial intelligence, social media and influencer marketi…
This $39 lifetime AI workspace could replace several productivity apps
Hive AI offers a workspace combining documents, visual boards, and AI-powered features to help you organize ideas and move projects forward.
The Top 20 Most Played Steam Deck Games of October 2026
The top 20 most played Steam Deck games of October 2026 is here, and it's crazy seeing Final Fantasy Resonance and Castlevania: Belmont's Curse so high up.
Scientists Have Built a Robot Dog Powered by Human Brain Cells
Neurons could beat AI for robot control.
Vikes Views: Who Ya Got? Minnesota Vikings at Detroit Lions
The undefeated Minnesota Vikings will have their biggest challenge of the year as they head to the Motor City to take on the Detroit Lions. It will be strength against strength when the high-powered Lions offense is on the field against the best defense in the league. Series Record: 82-45-2 Road Record: 37-27-1 Largest Win: […]
Is AI cannibalizing Google's search business?
In the Loop, Host Ejaaz Ahamadeen breaks down the market reaction to Alphabet's (GOOGL, GOOG) latest earnings and the growing concerns surrounding its core search business. Ahamadeen examines signs of slowing search revenue growth and how AI-powered search tools could be disrupting Google's traditional advertising model.
Mobile-health Network Solutions Launches MaNaDr Health Operating Ecosystem Connecting AI-Powered Virtual and In-Person Care
After two and a half years in development, the integrated platform brings doctors, patients, clinical records, pharmacy, and logistics into one connected healthcare ecosystem.Singapore, Singapore--(Newsfile Corp. - October 30, 2026) - Mobile-health Network Solutions (NASDAQ: MNDR) ("MNDR" or the "Company"), a leading AI-powered digital health ecosystem, today announced that the MaNaDr Health Operating Ecosystem is scheduled to launch first in Singapore on November 2, 2026, followed by a phased..
This Week’s Leadership Moves: W. R. Berkley, Truist, and PENN Entertainment
CDO Magazine’s weekly leadership brief: Key data, AI, governance, and security executive appointments at W. R. Berkley, Truist, PENN Entertainment, and more.
Frights, Fangs, and Healthcare Nightmares in Winning Halloween Haikus
This year’s winning Halloween haikus take on measles, the high cost of dental care, and AI-powered insurance denials.
Samsung’s advanced sleep apnea detection is coming to these Galaxy wearables
Samsung announced a new, advanced version of its AI-powered sleep apnea detection, and which devices it will appear on in...
Digital Science Acquires Jenni AI, Expanding Research Tools to 8 Million Users
Digital Science has acquired Jenni AI, an artificial intelligence-powered research and writing platform used by more than 8 million students... Read More
Attainable Luxury at Scale: ZLINE Kitchen and Bath Keeps Operations Moving With Spectrum Business
Spectrum Business’s Dedicated Fiber Internet and Cisco-powered Managed Network Services give ZLINE Kitchen and Bath the reliable, enterprise-grade connectivity it needs for same-day fulfillment and cloud operations, without adding IT headcount.
Surf Air Mobility Provides SurfOS Business Update, Highlighting OperatorOS Commercial Launch and Enterprise Growth Priorities
LOS ANGELES, October 29, 2026--Surf Air Mobility Inc. (NYSE: SRFM) ("Surf Air Mobility" or "the Company") today provided an update on recent achievements and priorities for SurfOS, the Company’s AI-enabled software operating system powered by Palantir Technologies (NASDAQ: PLTR) ("Palantir").
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
Preorder Xreal Aura, new smart glasses powered by Android XR
The futuristic new glasses combine elements of augmented and virtual reality.
Jonathan Taylor Trade to Seahawks Suggested By NFL Rumor Mill
The defending champions could ignite the trade deadline by pairing a prolific rushing veteran with their high-powered offense, potentially finalizing a massive roster overhaul in Indianapolis.
Xreal Aura is a new wired headset powered by Android XR
Xreal Aura will start at $1,279 and will have multiple XR specific launch apps at launch
Akai opens its MPC Sample sampler to the community for custom firmwares
Akai MPC Sample is a battery-powered portable hardware sampler: soon it will be open for the community.
Vir Biotechnology appoints Suzanne Topalian to board and Science and Technology Committee
Vir Biotechnology appointed Suzanne Topalian, M.D., to its board and Science and Technology Committee, effective October 27, 2026.Original SEC Filing: Vir Biotechnology, Inc. [ VIR ] - 8-K - Oct. 28, 2026This is an AI-powered summary. It may contain inaccuracies. Consider verifying important inform…
How small businesses can use AI, access capital and reach new customers
Explore how small businesses are using AI to save time, find new paths to financing and prepare to reach customers through AI-powered shopping.
PENN Entertainment Appoints Gary Hunter Chief Information Security Officer
PENN Entertainment appoints Gary Hunter as Chief Information Security Officer to lead enterprise cybersecurity across its gaming, technology, and digital experiences.
Baptist Health launches AI-driven platform for improving outcomes for mothers and babies
Baptist Health announced a partnership with PeriGen to launch a centralized OB Hub utilizing AI-powered technology to enhance monitoring for mothers and babies across its seven delivery hospitals.
Amazon Business Adds AI Procurement Tools, Building on $60B Marketplace Momentum
At its annual Reshape conference, Amazon Business introduced new AI-powered procurement and analytics capabilities designed to simplify purchasing and demonstrate savings. Here's the details and our analysis.
Rize Credit Union Launches New Small Business Deposit Experience with Enable Technologies
On-schedule implementation expands Rize's ability to serve small businesses, deepen member relationships, and create new opportunities for growthSAN FRANCISCO, CA / ACCESS Newswire / October 27, 2026 / Rize Credit Union, with roughly $1.3B in assets based in Irwindale, CA, today announced the successful launch of its new small business deposit account opening experience, powered by ("Enable").
🔴LIVE WNEM Morning Extra: Better Business Bureau warns of AI-powered scams
Here are the latest stories from the TV5 newsroom!
📈 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.
New Twin Cities Lab Changing The Landscape Of Health
Human Powered Health is strengthening its mission to help motivated people improve their health and longevity with the opening of an expansive human performance lab. Featuring an array of physiological assessments, longevity and athletic performance services, the state-of-the-art technology and health optimization services once reserved for elite athletes are now accessible and available for everyone.
The PAW Patrol® unleash their superpowers in Nickelodeon and VStar Entertainment Group’s newest live stage show PAW Patrol™ Live! “A Mighty Adventure” at the Fox Theatre March 26 and 27
(DETROIT – October 26, 2026) – Nickelodeon and VStar Entertainment Group proudly present PAW Patrol™ Live! “A Mighty Adventure.” The latest live stage show takes fans on a super-powered adventure inspired by PAW Patrol:
Fanatics teams up with OpenAI for new AI-powered shopping suggestion feature
Fanatics is using AI to help shoppers navigate more than 1.5 million items of sports merchandise, with a Shop the Look feature powered by OpenAI Codex.
We May Be Living Through a Sixth Mass Extinction—but Scientists Think Some of the First Five Never Happened
New AI-powered research questions whether a majority of Earth’s mass extinctions were as abrupt as previously believed.
Purdue and Accenture: A model for applied partnership
Artificial Intelligence is reshaping every industry. Find out how Purdue and Accenture are working together to prepare Indiana’s workforce and students in Purdue’s Mitch Daniels School of Business for an AI-powered future.
Fabarta Unveils Major Upgrade to Its AI for Science Platform, Named Among Inaugural "Hong Kong Top Talent Ecosystem Partners" Ahead of Hong Kong FinTech Week
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Toy Story 5, Backrooms and The Breadwinner Vie to Become the Second Biggest Film This Year...
(Welcome to my weekly streaming ratings report, the single best guide to what’s popular in streaming TV and what isn’t. I’m the Entertainment Strategy Guy, a former streaming executive who now analyzes business strategy in the entertainment industry. If you were forwarded this email, please subscribe to get these insights each week.) Often, people blame “data” for ruining Hollywood, when I’d argue that hype (which can often be debunked by data) is often worse, as you can see in these two headlines. To use a common meme, here’s how it started: and how it’s going: Earlier this year, I read countless (and I mean countless) articles (in almost every mainstream media outlet) about how Heated Rivalry was a huge hit and that it “heated” interest in hockey. But Heated Rivalry wasn’t a huge viewership hit compared to the hype: [ ](https://entertainment.substack.com/p/you-cant-eat-buzzdid-heated-rivalrys) [ You Can’t Eat Buzz…Did Heated Rivalry’s Buzz Translate into Actual Viewership? ](https://entertainment.substack.com/p/you-cant-eat-buzzdid-heated-rivalrys) Entertainment Strategy Guy · Jan 22 [ Read full story ](https://entertainment.substack.com/p/you-cant-eat-buzzdid-heated-rivalrys) In this case, the hype (Heated Rivalry is really popular) belied the data (It didn’t make the top ten charts, meaning it wasn’t a huge hit.1) Even worse, every time I write about this, I get a lot of negative feedback, hence why I only sparingly mention the show these days or add lots of caveats when I write about it, but this just further cements the hype. But companies, and the executives leading them, have to determine strategy based on reality, not media coverage. The NHL committed real marketing dollars to a campaign that didn’t work. Sure enough, the latest NHL season opened to under a million viewers. At some point, you have to focus on what the data says, because real dollars are at stake. And that’s why we have the Streaming Ratings Report! To provide and analyze cold, hard data. Luckily, this week, I can be positive and take a look at a huge opening on streaming, along with a very buzzy horror film that customers kind of like, but don’t really love. And a comedy that tried to rebound on streaming. Today, I’m covering two weeks—21-Sep and 28-Sep—so we have a lot to discuss (nearly 5,000 words worth), including a series of film misses for Netflix, another argument for weekly-released TV shows on streaming, another sitcom tries to avoid the comedy curse, Netflix has two new game shows (one powered by AI), Reacher falling off the charts after a huge run, Prime Video tries its hands at the romcom game**,** a look at the WNBA’s big season, a big night for the VMAs on CBS and MTV, all the flops, bombs and misses, and a whole lot more. (Reminder: The streaming ratings report focuses on the U.S. market and compiles data from Nielsen’s weekly top ten viewership ranks, Luminate’s Top Ten Data, JustWatch and Reelgood interest data, Samba TV household viewership, company datecdotes, Netflix hours viewed data, Google Trends, and IMDb to determine the most popular content. While most data points are current, Nielsen’s data covers the weeks of September 14th to October 4th, 2026. You can find a link to my terminology here.) Subscribe Film - Two Hits and a Miss Come to Streaming… Three very interesting films came to streaming in one week, and I like comparing them since they show the range of theatrical films. One was a huge blockbuster, one was popular, and one was neither. One is a horror film, a buzzy genre; one is a comedy, a struggling genre; and one is an animated family film, the king of the box office. One went to a tiny streamer (HBO Max), one went to a second-tier streamer (Disney+), and one went to the biggest (Netflix). So… If you just went by box office, you’d expect Backrooms—the $197 million grosser, and partner of Obsession for “YouTube Horror Summer”—to dominate on streaming. If you read this report, though, you know that horror films don’t do great on streaming, and that’s the case again. Backrooms opened at 3.8 million hours according to Nielsen, and had 3.9 million hours in its second week. Here’s how that stacks up to other HBO Max films: We’re just getting started with this issue, but the rest is for paid subscribers of the Entertainment Strategy Guy, so if you’d like to find out… ...why I don’t buy Backrooms budget… …two new games shows arrive on streaming… …another sitcom struggles… ...another argument for putting out shows weekly on Netflix... ...and a whole lot more, nearly 5,000 words more... ...please subscribe! We can only keep doing this great work with your support. If you want an idea of just how much content you’d get in a full issue, check out this older issue. Coming Soon! Next week, I’ll be taking a look at the eighth season of Hulu’s The Kardashians, answering the question: will this show ever make the charts? If Mormon wives can make it, surely one of social media’s most popular families can as well. Also, Prime Video has a new horror show based on popular IP, Carrie, Netflix has a horror show starring Josh Hartnett, Below, and Apple TV has a UK import, Small Prophets. Plus a bunch of movies like Disclosure Day heading to Peacock, The Housemaid coming to HBO Max, Ben Affleck’s latest on Apple TV, Animals, and Matchbox the Movie on Apple TV, a John Cena action comedy inspired by Hasbro’s toy cars. The week after is even busier! I’ll look at another IP-based horror show, Crystal Lake, a new MCU show on Disney+, VisionQuest, season two of The Lowdown on FX/Hulu, the fourth seasons of The Diplomat on Netflix and Tulsa King on Paramount+, another season of Love is Blind, and four straight-to-streaming films: Netflix’s Sacrifice (starring Chris Evens, Anya-Taylor-Joy, Vincent Cassel, John Malkovich, Salma Hayek. Charlie XCX), Apple TV’s Tenzing (starring Tom Hiddleston and Willem Defoe), Prime Video’s Love of Your Life (Margaret Qualley) and Masterplan (Stanley Tucci) and a whole lot more, somehow. Long term, here’s some huge IP news: a Game of Thrones movie is coming to theaters, Game of Thrones: Aegon’s Conquest in 2029, while Jon Watts (who helmed three of the most recent Spider-Man movies) is directing the next Star Wars trilogy, to be written by Simon Kinberg. I’m cautiously optimistic for both. It feels obvious to say that making good movies matters, but for bets this big (Star Wars is on a swoon, Game of Thrones hasn’t been to theaters yet), the quality has to be there.
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Sundar Pichai: Google’s Frontier Imperative, Compute Scarcity Dilemmas, Future AI Products and Businesses
Alphabet CEO Sundar Pichai has little patience for doubts about Google’s willingness or capacity to compete at the frontier of AI. His company’s latest Gemini 4 Argon model — long-promised and recently delivered — beats OpenAI and Anthropic’s latest public models on coding, math, science, and knowledge-work benchmarks and has placed Google’s AI back among the world’s best. Amid chatter that Google was fine with good-enough lightweight ‘Flash’ models, Pichai pushed back hard against any suggestion that company is limiting its research ambitions. “We won’t do it any other way,” he told me in a Big Technology Podcast interview recorded this week at Google’s Mountain View headquarters. Pursuing cutting-edge research is costly for Google, requiring compute and chips that otherwise could be sold at a high markup by its cloud business. But Pichai said allocating resources to frontier AI development is “ground zero” for Google. “Our cloud business is at any given moment in time forgoing short-term revenues because we are making compute trade-offs to support frontier AI development,” Pichai told me. In our conversation, we discussed Argon (which Pichai says processed three times as many tokens as Gemini 3 in the 72 hours after launch), AI’s role in developing better models, the AI interface of the future, competition with OpenAI, Google’s partnerships with Anthropic and Apple, data centers in space, and the nature of his optimism. You can read the full conversation, lightly edited for length and clarity, below, or listen to it on Apple Podcasts, Spotify, YouTube, or your podcast app of choice. Alex Kantrowitz: Google is back at the frontier with Gemini 4 Argon, which beats OpenAI’s and Anthropic’s public models on benchmarks spanning coding, math, science, and knowledge work. The conventional wisdom was that Google had given up on the frontier to focus on fast, good-enough models for its products. That was wrong? Sundar Pichai: Very wrong. Look, we are so committed to being at the frontier. It’s in our DNA. We won’t do it any other way. We’ve been thinking about the AI frontier for well over a decade now. The frontier is very dynamic. It’s very competitive. It’s very exciting. I couldn’t be more pleased with how Argon has come together. We took our time with it. We made sure we tested it safely and deployed it carefully. We took a bit longer than our other models, given its capabilities. Across the board, be it for agentic work, knowledge work, coding, creative tasks, Argon is one of the really good frontier models. The first 72 hours since we launched, it’s almost up to 25 trillion tokens used, which is about three times what Gemini 3.0 had in the first 72 hours. And we are capacity constrained as we are serving it. Excited, but there are always areas….. You know, the frontier is very dynamic, and you have to keep evolving the models. There are areas which we can see we need to improve as well. We’ll be at work on all of that as well. Which areas? On any given model, Argon would be across the board better at everything if you just look even two months back. But the space is so evolving. Even on coding, for example, there are pockets where we know we need to do better, and there’s clear feedback. We’ll make it better. The argument for standard models is that they’re already good enough to build great products, with the frontier valuable only for certain tasks. Meta built Muse with good models rather than the best, and Google could put standard models into Maps, Gmail, and Search. Why isn’t that sufficient? It’s definitely not how I have thought about it at all. We are deeply committed to driving the state of the art and actually pushing where this technology can go, all the way from transformers. That’s a journey we’ve been on. You will see us continue to do that. When you’re at the frontier, you get an earlier look into the emerging capabilities, earlier than others, which helps you better prepare for the future. We are fortunate to have a wide set of opportunities across consumer and enterprise. Some of our cloud customers, for sure, want the model at the frontier. The frontier models will be better at developing the next set of models in an era of recursive self-improvement. I would argue it matters there. Internally, we have definitely seen a steep point of inflection by deploying Argon to our developers. It definitely tackles problems which our previous models could not do. Being at the frontier puts you at the innovation frontier and helps you drive that as well. There is room for models of all sizes and capabilities. That’s why we did Flash, Flash-Lite, etc. I agree with you that you can develop very good product experiences with not necessarily the most frontier model, because they tend to be slower and more expensive too. But we care about the Pareto frontier, and we’re committed to doing all of that. You said developing frontier models gives you a look at what’s coming. What do you see on the horizon? What makes this moment so exciting is you build something which you think is a culmination of months of work, and you put it out. You already see clear headroom ahead. We’ve all been working on these agentic, long-horizon capabilities, but clearly the frontier is going to have multiple of these agents working together to do things. That’s an example of an exciting paradigm underway. It’s also clear to me that users, both in consumer and enterprise, will have access to some multi-agent framework for them, working all the time doing things. It feels like a moment of infinite possibilities. Sergey Brin said one of Argon’s core innovations was relying more on the model to help develop the model. Yet incidents involving OpenAI’s bots breaking containment and Google’s bots accessing a site they shouldn’t have suggest these systems still aren’t perfectly aligned. Isn’t there reasonable cause for concern about accelerating toward recursive self-improvement? There are three parts to your question. Let’s first talk about RSI itself. You’re right, Sergey mentioned RSI being a critical contributor as we are developing Argon. But to be very clear, today, in a continuum, we are pretty far from the end state of RSI or something like that. The right way to describe it is you have RSI loops along with AI researchers in various aspects of model development. You almost have agents helping AI researchers in some automated way work together to build these models. It’s good to at least understand where we are today, and it’s super exciting. I expect that continuum to keep making progress as we go into next year. The second question is around safety. Security comes from defense in depth. You’re talking about alignment, obviously. We all are doing a lot of work to make sure the models are as aligned as possible. But it’s an active area of research, and there are clear gaps. That’s not the only thing we rely on. You have the work you do on alignment, but beyond that, we put a lot of effort into real-time monitoring as these models are working. Making sure you have good telemetry into what’s happening so that you can intervene in real time if you see unexpected behaviors. How you do that, the architecture with which you do that, how you make sure it’s all sandboxed and isolated in a way that it can continue doing its job during real-time monitoring — these are all areas of investment. Obviously, when we deploy these models, giving customers, enterprises, control, both with real-time monitoring, but their ability to decide where they deploy, what type of data which models ever have access to, etc. All that work is very, very important, and we are doing a lot of that. As an industry, we need to constantly up our game and take the time to do all this stuff, including dedicating the compute required to make sure you can safely deploy these models. The third part is: Is it all enough? This is why there are a lot of conversations in the industry, and I think it’s reasonable to be worried. The right way to approach something you worry about is early on thinking about what more we can do. As an industry, you’re definitely seeing increased levels of work on alignment, testing for safety, etc., as we deploy these models. The next step would be developing better standards so there are cross-industry standards of how we do these things, including the ability to evaluate using independent evaluators. If you look at areas like financial controls, we have accounting standards, we have independent auditors. Those are all the next natural steps for us as an industry to work toward, and regulation will end up playing a role as we go through all of that as well. Google pursues both language models and world models, while some other AI companies like OpenAI have focused more heavily on LLMs as the path to AGI. Why is world-model research important to you? As humans, you obviously perceive the world around you, and you’re taking all those inputs. Making sure these models are able to understand the laws of physics, simulate the world in high fidelity. All that, I think, are key enabling blocks for intelligence. We are working across all possible applications of this. Take an area like robotics. It’s not an accident: If you look at embodied reasoning benchmarks, Argon is state of the art. That all comes from our experience building world models. Inherently, I would expect there will be architectures which will converge all of this in the future. But for us, we view this as, today, this jagged frontier that is intelligence. We want to be at the frontier of all the possible approaches, and that will help us both drive better research, better insights, and develop a much better AGI model, is how we think about it. Some people argue Google should focus on selling its computing resources rather than using them for AI research. Cloud wants to sell compute at a high markup, while researchers need a very big computer to build the best models. How do you balance those demands? Ground zero for us is making sure we are investing in the compute that’s needed to be at the AI frontier. In 2015, I set the company to be AI-first. AI is that key horizontal ingredient across everything we do, all the way from Search to Cloud to Waymo. That is ground zero for us, making sure both for research and developing the models that we have enough compute needed. Beyond that, we have these independent businesses which each have their own long-range plans. They’re using those long-range plans to project forward, and we plan for compute for all of them. The cloud team, they have a business and they have a projection and they have demand signals. They have committed contracts. With that help, we can plan on a long-term compute trajectory for them. We’re fortunate to have this wide set of opportunities. There’s no company in the world which isn’t making trade-offs at any given moment in time. But the first principles we operate on is making sure we have enough compute to invest in the frontier, and that’s non-negotiable, and everything else works on top. Remember, we are building our own compute, our own data centers. There are long lead times. The compute we have today is a function of decisions I made in 2024. Even if you think back to a year from today, last year the agentic coding takeoff hadn’t happened. Today, obviously, when you sit and look at anything, everyone is short—not just us, other companies—and everyone wishes you had two times what you had planned for. That comes with the territory. At any given moment in time, we are planning for what we would need two to three years out in the future. You’re going to find some decisions you make may look suboptimal two years out, depending on where the state of the art is. I don’t spend too much time thinking about it. The way I think about compute allocation is making sure Google DeepMind, both for research and frontier model development, is cutting edge in terms of compute access. After that, we plan for all our businesses. It’s great we can provide our compute to other people too, like we have done. It allows us to scale that up, get real tests for it in the widest application set possible, test it in the real world, and make sure we are at the cutting edge of technology at every layer of our AI stack as well. Ben Horowitz relayed on a podcast that Andy Jassy had said compute was sold out until 2028. Are you seeing the same thing? If today a customer is coming out and asking us for compute in ’27, that is incredibly difficult to do. Even last year we went out and leased some short-term compute to accommodate some customers and help them out. But yes, we are all dealing with similar timelines. Every major technology shift seems to involve overbuilding, from railroads to fiber. Do you think that’s happening here? It depends on the time frames you’re looking at. This level of build, if I take a long enough time frame, it makes complete sense to me. We are at early stages of the most consequential technology ever, and this is not like fiber. It’s not dark. We are using it. We are capacity constrained on a daily basis. The applications, the underlying technology is improving rapidly. But running a company, the way I work through the moment is you look at your demand signals. You look at your committed revenue opportunities that you’re getting in the form of contracts. You balance that with the underlying technical capability, or internally you balance how we are using it internally, the model trajectories. Based on all that, you take a disciplined approach—a confident, aggressive, but disciplined approach—to plan out compute. But when you talk about the broader economy as a whole, you’re having people operate with various levels of risk. I focus on what I can control as a company and plan through it. If your question is, can there be an overbuild? Yes, because you can have some constrained components. Remember, what matters is a set of everything you need to build out that compute. Memory, electricity, electricians…. Land. Like 2,000 components or more. You could get constrained at some point. There could be things which cause bottlenecks, and then you could have overbuilds. There could be external shocks to the economy. There are macro risks. All that is possible at any given time. But am I confident that the compute we are planning for, we will have uses for it given enough time? Absolutely, yes. I was in the room when you said AI would be as profound as fire and electricity. If you believe that, shouldn’t you give researchers the biggest possible computer, even at the expense of today’s cloud profits? We’re all doing some form of that. Our cloud business is at any given moment in time forgoing short-term revenues because we are making compute trade-offs to support frontier AI development. What you’re seeing is true already. But you want to be thoughtful. Even our AI researchers have some constraints, which are also good. That’s what inspires creativity. That’s what helps you come up with a more efficient algorithmic breakthrough, maybe to use less memory. What we are committed to is to invest what’s needed to stay at the frontier. OpenAI President Greg Brockman told me a few months ago that the ideal AI product might have no interface at all—just as natural as conversation. What do you think the interface looks like in the age of AI? Computing historically has been humans adapting to computing. With AI, it’ll be the other way around. The technology will adapt seamlessly to how humans live and go about their day-to-day life. You want to be able to speak to that AI when you want to. It has context. It can be persistently running somewhere, give you continuity. You’re not dealing with a lot of complexity. It’s natural. All that makes sense to me. That doesn’t mean it won’t have any interface, however. Nowadays, even on my phone, Gemini voice models are so good. I can just press the microphone button. I can just ramble. That’s why we call it Rambler. I can ramble. It intelligently corrects with my personal context. Even my friends’ names, weird names, it gets it right, even if it first guesses it wrong. It’s amazing. I’m not going in, backspacing, editing once I finish saying it. It’s remarkable how good it’s gotten. But there are going to be moments you will need an interface. You may want to entertain yourself. You may want to watch a video. You may ask your AI to create something, and you want to watch it, consume it. The way I think about it is, you want it to be as seamless and as natural as possible, and that you are not doing the hard work adapting to whatever the interface is. The technology is adapting to how you’re working at any given moment in time. When I asked Sam Altman about competition from Google, he argued that OpenAI was building AI-native products and implied Google was bolting AI onto existing ones. Do you accept that framing, and can a “bolt-on” compete with an AI-native product? First of all, it’s a very dynamic moment. We are all pushing each other. OpenAI has pushed us as a company too. This is not just we are doing something to them. Far from it. Yes, Google went into its own version of a code red too. In the AI moment, all of us have to respond with varying levels of urgency at any given moment in time. We are constantly building new products as well. Gemini was built from the ground up as a product—the Gemini app I’m talking about. Or tomorrow, as all of us are adapting to agents, are you bolting agents onto ChatGPT, or are they building it natively? How would you think about that? You can always put these questions back. I don’t think of it that way. I think of it as you want to build world-class product experiences that are AI-native and they are winning. Some of it is adapting products, some of it is rethinking new experiences. They will all be part of how you approach this moment. How do you expect to make money from these AI interfaces? If an agent recommends a product and I buy it, should it take a cut of the sale, even if that’s disclosed? I’m going to put enterprise aside. Consumers are going to increasingly value this technology. Take software developers today. Developers are willing to pay for these coding models because it really helps make them more productive. Nobody would want to code the way they were coding three years ago, or even the same time last year. When you’re delivering value, there’s value to be had. I’ve never seen examples where consumer products really scale to billions of users, where people are using it intensely, and haven’t had monetization to go along with it. If you’re providing value to people, there is inherent value to be had. The nature of it may change. You’re asking a scenario in which agents are doing stuff for you. At the end of the day, you will have commercial needs. The agents need to fulfill those commercial needs. The providers on the other side are going to see value in that. There’s going to be some mechanism by which it gets fleshed out. That’s what an efficient system does. But what matters is that you’re building those world-class consumer experiences. I promised myself one product feature request: Can you make Gemini-powered search in Gmail as good as searching Gmail through ChatGPT? What’s going on here? One of the exciting things we announced recently is the unified Gemini agent for enterprise, and we’re bringing that to consumers. These are persistent agents which run 24/7 in the cloud. We’re spinning up a micro VM effectively for every query, and from the runtime, the harness, the model—it’s all a headless service. It’ll work everywhere, and it has your context with you. You can address this agent whether you’re in Workspace, in Search, in Gemini. Agents are going to drive this unification from a customer experience, which people are looking for. When you have a broad product portfolio and you’re moving forward fast, there are always areas where you’re moving forward faster than some other areas. That’s valid feedback. But we are putting a lot of time and effort into making sure, as we build out our agentic experience, that unification and that capability works well across all these services. People have asked where Google’s equivalent of Muse or Dots is. Do you anticipate creating a new product to compete with them? Our goal with Gemini is to be a personal, proactive, and powerful assistant. That’s what we are working toward. You saw us describe the early versions of it at Google I/O this year with Spark. Effectively, already, if you open the Gemini app as a Pro or an Ultra user, you see Chat and Task, and they start giving you that agentic experience. With Argon now, that capability is dramatically accelerated. What we announced recently in our enterprise event, we are bringing that to consumers as well. Soon in Gemini, you will be able to ask something, and it’s a continuum. You don’t need to think about whether you’re asking a chat or an agentic task. It’ll just automatically do the right thing, and it can work persistently in the background securely and get things done for you. Already, empirically, when I look at what’s coming into Gemini in our app, last year you would have tagged them and most of them would have been chats. But a steadily growing percentage is already agentic tasks in Gemini. Yes, we are going to bring personal agents to our consumers. It’ll work in the Gemini app. It’ll work in Google Search. It’ll work in Gmail. You can address it, call it, etc. We want to make sure we’re getting it right. It’ll be powered by Argon, and we are very excited to bring it to our users. Google has invested and committed tens of billions of dollars to Anthropic. What do you see in Dario Amodei that makes you want such a deep investment and partnership? Very early on, when we looked at the founding team of Anthropic, it was very clear to us this was a world-class team which was involved in some of the earliest GPT models. We were quickly able to assess that they would be at the frontier. We’ve always tried to identify and partner with companies which would be at the cutting edge of technology. We’ve done that with SpaceX. We’ve done that with Anthropic. That’s what we saw, and we had a chance to partner. Is your partnership with Apple to license Gemini for Siri expanding, contracting, or standing still? For both us and Apple, that partnership was about translating the capabilities of AI into better experiences for users. I thought Apple has done a phenomenal job of incorporating—they customize these models and build it into Siri—and it’s been great to see the reception. We are committed to doing more. You’re going to do more with them? There’s nothing specific to comment, but it’s a partnership. It’s working well. What have you learned so far about the feasibility of data centers in space? It’s a very exciting project. Projects like this remind me—I still recall that moment in the desert for the DARPA Grand Challenge, watching the Stanley car compete in it, which ended up being the basis for Waymo. With all these technologies, you’re at a moment in time where it looks a bit like sci-fi, but 10 to 20 years down the line, it’s actually working in a meaningful way. I feel the same in space. We’ve always believed in betting big on these technologies. Quantum has been a great example of it. We’re probably 10 years into the journey. Waymo, we are almost 20 years into the journey. Space, we are two to three years into the journey. It’s clear to me that it’s an approach which will make a lot of sense in the future. Being in sub-synchronous orbits with 24/7 access to the most abundant form of energy we have access to will make a lot of sense for this exponential increase in compute that we are going to need. Having said that, this is a challenging project. The team has disciplined milestones. It was a super exciting milestone to send the TPUs up to space. Sometime, as we are speaking, we are expecting a “hello world” moment from them. I look forward to seeing that happen, and we’ll continue investing here too. You previously said 25% of the code written within Google was written by AI. What’s the number today? That number in our world today is meaningless in the sense that every part of what we now touch is influenced by AI deeply. What we are tracking with Argon, for example, it’s been a point of inflection. People are using Argon to really go tackle complex projects, including projects which affect millions of lines of code. In the four weeks since we have launched Argon, we have seen a 30% increase in CLs, and more importantly, almost a similar increase—a one-third decrease in rollbacks. People are writing code more productively and better code with it. We are tracking it with newer metrics now. How are you dealing with opposition to data centers? If you can’t build in the U.S. the way you want to, where else do you go? We’re very committed to building our data centers very responsibly. When I look at our largest data centers in some of the communities where we have been the longest, I visit them, I talk to people. There are people who come and tell us their kids have gone to college because of our investments in these communities, and we’ve grown in those communities. To me, this is about doing it right. Engaging with the communities early, doing it transparently. We are committed to making sure we are bringing incremental power, and we cover 100% of the cost of our power needs for these projects. It’s understanding the impact on neighborhoods and making sure you’re working thoroughly to address them. Done rightly, you can continue making progress. But we are constantly taking feedback into account. I do worry not everyone operates the same way. People have been surprised by data centers showing up in their backyard. Those are all important. As an industry, we should continue to do better. But part of the reason, in a 20-year time frame, we are thinking about things like data centers in space is making sure we are able to put these data centers where maybe the trade-offs will be different. What unproven area of AI is Google working hardest to crack? Is it continual learning—getting models to keep updating after release—or something else? There are multiple parallel paths. You saw with Argon we have a million output tokens. With our Gemini models early on, we had a one-million context window on the input side. These are all examples of breakthroughs we are constantly working on. How do you think about synthetic data? How do you think about continual learning? How do you think about more efficient architectures? We do dedicate a meaningful percentage of our compute to these research bets too. It always goes hand in hand, including our world models. Those are all bets we are placing. Sometimes, as part of these bets, there are ideas that emerge which just make their way into the mainline models. Maybe we don’t fully talk about it, but we have multiple steps in these areas. Working in technology requires optimism that building something will improve the world, but those bets can leave real consequences when they fail. I recently returned to a town in El Salvador where enthusiasm for Bitcoin had given way to unfinished construction and something resembling a ghost town. How do you maintain the optimism you spoke about in your Stanford commencement address while accounting for those risks? I genuinely have a deep conviction that improving technology and improving access to technology makes people’s lives better. It’s been my personal experience in my life, out of which comes that deep conviction, optimism. But with that, I’ve always felt humanity’s journey is always about working hard to harness that technology to make it work well for society. The optimism is not an optimism that you just build whatever you want, whenever. You work hard to make sure you’re building technology in the service of humanity, to make the human condition better. That’s got to be the defining goal as we are approaching it. With that framework, I’m very, very optimistic. We are in very early stages of the kind of progress that we are going to see on many areas. We’re all talking about areas like healthcare. A lot of the progress is ahead. The ability to make things in a fundamentally cheaper way on many things where things have been getting expensive for a long, long time, discovering new materials, etc., which really bend the curve—it’s tough to step today and understand the impact of all that, but they’re going to be very profound. I’m very optimistic, but we have to be rightfully concerned about all the ways this technology can go wrong. Sundar, I know you don’t do many of these. Thank you. Thanks, Alex. Appreciate it.
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NJBIZ In the Lead 2026: Women-Owned Businesses
NJBIZ celebrates women entrepreneurs across industries who have built successful businesses, overcome funding challenges and contributed to their communities.
A – C | NJBIZ In the Lead 2026: Women-Owned Businesses
In the Lead: Enterprises that innovate, disrupt, break molds and showcase the energy, talent, intellect and perseverance of women as business leaders.
D – L | NJBIZ In the Lead 2026: Women-Owned Businesses
In the Lead: Enterprises that innovate, disrupt, break molds and showcase the energy, talent, intellect and perseverance of women as business leaders.
M – S | NJBIZ In the Lead 2026: Women-Owned Businesses
In the Lead: Enterprises that innovate, disrupt, break molds and showcase the energy, talent, intellect and perseverance of women as business leaders.
T – Z | NJBIZ In the Lead 2026: Women-Owned Businesses
In the Lead: Enterprises that innovate, disrupt, break molds and showcase the energy, talent, intellect and perseverance of women as business leaders.
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