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Decommissioned NVIDIA supercomputer with 9,600 A100 GPUs hits eBay for $60 million, free shipping included
A used NVIDIA AI cluster containing 9,600 A100 GPUs and 4,200 AMD EPYC CPUs appeared on eBay. Its $59.7 million asking price included free ground delivery.
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
Everton Vs Newcastle United | Carabao Cup Fourth Round
Matthias Jaissle’s Magpies head to Merseyside aiming to replicate their 2025 trophy success. High-intensity tactics meet a hostile atmosphere as both sides battle for a quarter-final spot.
Your Google Messages chats are getting a ChatGPT upgrade
OpenAI now lets you text your ChatGPT Dot via RCS, with proactive updates and seamless conversations. Here's how it works and who can use it.
I’ve tested the iPhone Duo, Galaxy Z Fold 8, Pixel 11 Pro Fold, and Razr Fold — here’s the foldable phone I recommend
Only one is worth spending $2,000 on
105th meeting between Science Hill, D-B to determine Region 1-6A champ
SH vs. D-B preview
Palmetto Sports Daily: Gamecocks look to keep focused, Clemson heads into hostile environment
PSD returns with how the Gamecocks and Tigers are preparing for Saturday, plus a preview of South Carolina State’s big Thursday night matchup.
OpenAI expanding ChatGPT dot access to iMessage and RCS
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Chicago trucking entrepreneur plans high-end Naples nightclub
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Woman, 31, dies after ChatGPT convinced her it was a good idea to change medication dosage
Xbox’s missing Year in Review is coming back bigger as ‘Xbox Playback,’ celebrating 25 years of your gaming history
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As families rethink entertainment spending, Chuck E Cheese leans into play
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Marie Gluesenkamp Perez vs. John Braun: Two styles of lawmaking
U.S. Rep. Marie Gluesenkamp Perez and state Sen. John Braun face off in Washington's 3rd, bringing contrasting styles of lawmaking, coalition-building and problem-solving to a tight race.
Laurent Mekies reveals 'attention from F1 teams' in Red Bull-Ford Powertrains power unit amid Aston Martin rumours
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The key to success in business? Take risks. Just ask the owner of the Kobuk
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Why Familiar Screen Symbols Still Work in Entertainment Design
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Roseburg declares Small Business Season from Nov. 1 to Dec. 31, 2026
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Augusta Entertainment Complex to be named for James Brown
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iCloud Mail and Calendar plugins now work with ChatGPT
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OpenAI announces 3 AI-generated discoveries in mathematics, computer science, physics
ChatGPT developer says AI models produced proofs addressing longstanding scientific problems, seeks independent academic scrutiny | Anadolu
How Sports Betting Is Becoming Part of the Luxury Entertainment Experience
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I cofounded GPTZero. Now I worry people can't write without AI.
Edward Tian built GPTZero in college and sold the AI writing detection tool to Superhuman. Now he worries AI is changing how people write and think.
Lexington Mexican restaurant reopens after health department closure
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Firefox brings back internet icons for new anti-tracking, anti-AI campaign
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iPhone AI Images and Edits Now Have a Hidden Watermark
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Philippine Star
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Adam Brody’s ‘Nobody Wants This’ costar speaks out after actor’s pro-Palestine comments
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Homedics parent company cuts 34 jobs in Oakland County
An Oakland County wellness company is laying off 34 employees, citing financial troubles.
ChatGPT Keeps Telling People 404 Media Can Solve Their Tech Support Problems
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Indiana's New Small Business Program: From Policy to Opportunity
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Partner1 Renews WBENC Women's Business Enterprise and Women-Owned Small Business Certifications
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Community investments: How affordable financing supports housing and small business growth
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Today’s Papers: Juventus claw back, Inter never give up
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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
PR Newswire | Powered by the upgraded RSI engine, the platform unifi
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Xbox console users will receive 50 games from October 26 to 31.
From October 26th to 31st, the Xbox ecosystem and Xbox PC service will receive a major expansion of their catalog, including the release of 50 new games for...
Modders Unlock GeForce RTX 20, 30 And 40 GPUs For Up To 2X Compute Gains
The compute barrierbbetween NVIDIA RTX Pro GPUs and NVIDIA GeForce GPUs has finally been breached.
GeForce Unlocked: Modders Boost RTX 20, RTX 30, and RTX 40 Compute Performance by Up to 2x
NVIDIA's GeForce RTX 30-series and RTX 40-series graphics cards are based on the same dies as their professional-grade GPUs. This means the compute potential of consumer-oriented RTX GPUs is restricted by NVIDIA's firmware stack, but modders have managed to unlock the instruction issuing rate...
My Co-Worker Is Losing It Over My Religion
Plus: Reader responses to a woman returning to the office after two decades of self-employment.
Your AMD GPU Has A One-Click Setting That Can Extend Its Lifespan
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‘Things are really booming’: FAMU homecoming brings business opportunities for local vendors
Florida A&M University (FAMU) homecoming is bringing crowds and business opportunities to vendors selling food, clothing, art and accessories along Perry Street and around campus.
South Texas Health System (@stxhealthsystem)
1 likes, 1 comments - stxhealthsystem on October 24, 2026: "Lower Your Risk, Prioritize Your Breast Health: Breast cancer is one of the most commonly diagnosed cancers among women worldwide. While some risk factors, such as age and family history, can't be changed, there are lifestyle choices that may help reduce your risk and support overall breast health. ✅ Maintain a healthy weight ✅ Stay physically active ✅ Eat a balanced, nutritious diet ✅ Avoid tobacco & limit alcohol consumption ✅ Talk with your doctor about hormone replacement therapy Just as important, don't delay routine checkups and recommended screenings. When breast cancer is detected early, treatment is often more effective and outcomes can be significantly improved. For more on breast cancer, watch the latest South Texas Health Minute on ValleyCentral.com at the link in our bio.".
NVIDIA GeForce RTX 30 GPUs Get Up To 2X Faster AI Compute Performance With New GPU Unlock Mod, Real-World Gains Of Up To 60%
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This US House district was once a political battleground. Now it shows why Congress is dysfunctional
The number of politically competitive U.S. House seats is shrinking rapidly.
This US House district was once a political battleground. Now it shows why Congress is dysfunctional
The number of politically competitive U.S. House seats is shrinking rapidly.
This US House district was once a political battleground. Now it shows why Congress is dysfunctional
The number of politically competitive U.S. House seats is shrinking rapidly.
This US House district was once a political battleground. Now it shows why Congress is dysfunctional
The number of politically competitive U.S. House seats is shrinking rapidly.
Voters angry about high prices may get little relief from the next Congress
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Redes named RBHS Science student of the Semester
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AI image generator test: Canva Craft vs. ChatGPT, Gemini, Meta Muse
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George Russell reveals truth behind Horner's Red Bull approach
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Arts and entertainment throughout the region
A listing of arts and entertainment events throughout the region.
Arts and entertainment throughout the region
A listing of arts and entertainment events throughout the region.
Boston 25 News
Road closures, blocked parking lots and construction equipment have made it difficult for customers to get around downtown and, business owners say, even harder to get people through their doors.
Science Hill finishes state runner-up in program's best season
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Entertainment 360 and Griffin Gaming Partners Launch HITpoint Entertainment
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Drake University expands full-tuition Bulldog Promise program to transfer students
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This Chicago Mansion Is Hiding A World Of Magic Behind Its Historic Doors
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AMD FSR 4.1.2 Now Runs On Intel Arc XMX Cores, Delivering Better Image Quality Than XeSS Upscaling With Similar Performance
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There’s a Reason That Netflix’s Ad Business Has Underwhelmed
Somewhat lost in the company’s slew of firings and engagement warnings over the past few weeks was the fact that Netflix is scaling back its ad forecast for 2026 from $3 billion to $2.7 billion. That’s despite the fact that: CTV is growing by 14-15% this year This year saw a monster upfront Netflix is the biggest streamer out there this side of YouTube, and was the platform that for years brands were supposedly dying to be a part of In other words, that’s an alarming pull-back from Netflix. A Wall Street analyst I spoke with last week was blunt, calling Netflix’s ad business “disappointing.” To me, a red flag on the ad front is that Netflix has never revealed how many ad-supported subscribers it has. Because if I know anything about how big companies work, if they have a positive number that makes them look really good - they tend to share it. (How many subs does go90 have right now? We’re not saying just yet. How are ads going on ChatGPT? Oh, a billion run rate - I see). Yes, to date Netflix has shared some fuzzy ad numbers, such as the idea that the company’s ad tier ‘reaches’ 250 million users globally.’ Here’s why that figure gives me pause: a)they lead with global, and b) they don’t explain how they are calculated. (Are they based on average people in each room? Surveys of some kind?). Regardless, some insiders say that Netflix’s ad tier is scaling nicely - others not so much. Either way, one of Netflix’s biggest challenge on the ad front appears to have been its starting point. Remember, when Netflix launched ads in 2022, it had 73 million subs in the US and Canada. That’s 73 million people who were used to Netflix with zero ads. And when the ad product rolled out, the company clearly was going after new users - a limited pool of potential subscribers by default. That focus seems to have changed over time, as Netflix has tried to convert longtime subscribers to the ad tier. Historically Netflix subscribers are very sticky. But is the company’s low churn a problem? Not necessarily, according to Antenna data. Rather, Netflix’s consumer loyalty is a bit of a double-edged sword. “Netflix is the one streamer which actually has seen substantial “trade downs” but has much lower ‘win backs,’ said Antenna founder Jonathan Carson (win-backs being people who were on the ad-free plan, cancelled, then rejoined on the ad plan). “Netflix has lower churn and so has less opportunity to win back old subs with a lower cost plan option.” This all might explain why Netflix has been so slow to roll out all of these table stakes targeting options until just this week. It likely didn’t have enough users to carve up into various targets. etc. However, what really hit Netflix’s ad business was Amazon’s move a few years ago to turn on advertising for Prime Video by default. Suddenly the market was awash in premium inventory, and Netflix was playing catch-up on multiple fronts. Thus, the streaming giant appears to still be figuring out if if wants lead in ‘programmatic TV,’ or premium ‘upfront’ TV sales. “The CTV market now sorts into three businesses, and Netflix is not the leader of any of them,” wrote analyst Jack Myers. Ouch. So how does Netflix ratchet up its ads business? Get the next NFL package You’re gonna have to wait until 2030 unless one of the bigger media companies like Skydance opts out, but the easiest (not cheapest) way for Netflix to become a huge TV ads contender is to get one of the regular NFL packages, ideally Sunday afternoons. That way it can sell ads to its whole U.S. subs base overnight. Go FAST This one is risky, because to program a compelling FAST channel, you’d likely have to take some older Netflix exclusive shows and make them available for free to non-subscribers. But FASTs, for better or worse, are ad-inventory machines. Go full UGC I’m not sure I see this happening. There is something already off about Netflix carrying podcasts and shows for creators. The experience sticks out in what is such a produced, and generally non-communal environment. But Netflix could open up its platform by allowing creators to upload videos without having to be approved by the service. But that opens a whole other can of worms (policing brand safety, copyright infringement, overall quality). Make a huge RMN Deal I was going to suggest something crazy, such as ‘instead of buying Warner Brothers, Netflix should buy Target. But the company doesn’t want to be in the brick and mortar business. Yet for Netflix’s ad business to compete in an Amazon world, it needs to be able to connect ad exposures to shopping. What if, instead of letting Amazon target Netflix viewers with its DSP, Netflix inked a deal with Target, or even took control of Roundel, and created its version of the Amazon full-funnel for brands? Price us out. I hope they don’t do this, but Netflix’s price increases - while significant - haven’t been enough to push long-term customers out of the ad-free tier. But if they made those prices so prohibitive, it could push large swaths of consumers to trade down. Then again, that could also dent engagement - as binging becomes much less appealing once you bring ads into the equation. Or, they could just launch a bunch of hit shows and maybe everyone will quickly forget all this angst. More from Amazon unBoxed A few weeks ago, I recorded two podcasts live at Amazon’s unBoxed event in San Francisco. I was able to go really deep with several brand partners about how Amazon’s various data and ad products work - particularly the under appreciated Amazon Marketing Cloud. First, I talked with Laura Abadias Velilla, of the Amazon specialty agency Amerge, along with Amazon’s own Charlotte Maines, Vice President, Devices Content & Advertising, about how the two partners helped a very specific printer company Epson launch into a brand new category - high end projectors. It’s a great example of how brands connect audience data across platforms to make budget deployment decisions. Next, I chatted with Monks VP Head of Digital Investment for North America Tom Burchill about how the agency actually brought in data from Linkedin to help target B2B customers across various Amazon properties for the brand Quicken. Check it out: Check these out, or just listen:
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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.
AMD Announces FSR 4.1 for Xbox Series X/S, Z2 Extreme and Z2A Handhelds, RX 6000 GPUs and More
Plus: FSR 4 will soon support Vulkan games.
AMD is bringing FSR 4.1 to Xbox consoles and handhelds
AMD’s latest upscaling tech is also coming to laptops, 6000-series GPUs, and handhelds.
Coming to XBOX Game Pass: Final Fantasy VII Remake Intergrade, The Séance of Blake Manor, and More
Ghosts to question and a planet to save. Sounds like a perfectly reasonable few weeks to me! We have another batch of games coming soon to Game Pass, so clear a little room in that “Play Next List” and let’s get to it!
Xbox Series X will offer identical graphics to the base PS5 in Grand Theft Auto VI
Social media is buzzing about user XGREEN's claim that the Xbox Series X console will supposedly offer better value for money.
Opinion: How one-party politics and a complacent media fail Cook County
Cook County and lately Illinois are paying a high price for the one-party system that the monetization of politics propagates.
Opinion: How one-party politics and a complacent media fail Cook County
Cook County and lately Illinois are paying a high price for the one-party system that the monetization of politics propagates.
DOJ Charges 28 California Residents With Voting-Related Crimes Amid Trump’s Voter Fraud Push
The effort by the Trump administration comes as it wages a broader fight with California officials over voter rolls.
AMD Ryzen AI Halo Review - Local-AI Powerhouse with 128 GB of Memory
The AMD Ryzen AI Halo is great at running local AI workloads, featuring a Ryzen AI Max+ 395 APU with 128 GB of unified memory in a compact form factor. It excels at handling large models, enabling tasks that most consumer GPUs struggle with. With AMD AI Playbooks, getting started is simple, allowing you to explore the frontier of AI at home.
Hackers Exploit Plex2Shell Flaws to Install Crypto Miners and Linux Proxy Backdoor
Attackers exploit four Plex Media Server flaws chained as Plex2Shell to deploy XMRig miners and reverse proxy software after PoC release.
3 tips to manage soaring health costs
Marsh McLennan Senior VP Marybeth Gray shared ideas to keep employees healthy and costs down.
The RTX 5090 Isn’t 'Alive' if It Costs $10,000, No Matter What Nvidia Says
Nvidia's flagship GPU may still be in production, but that isn't saying much for the state of Nividia's RTX 50-series lineup.
South Brunswick BOE Approves High School Computer Science Research Study Involving ChatGPT
South Brunswick High School students will participate in research exploring how ChatGPT and AI tools can support computer science education.
GTA VI will run better on Xbox Series X than on the base PS5.
A technical analysis of the hardware specifications of current gaming platforms indicates that the anticipated crime action game Grand Theft Auto VI...
Expert ‘bat wranglers’ put GPS trackers on California’s bats
These winged mammals can cover a lot of ground during their seasonal migrations.
Central Illinois Flea Market vendors explain why they’re in business
The Central Illinois Flea Market wrapped up its season at Peoria Speedway this weekend, giving shoppers one last chance to browse a variety of unique finds.