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Warby Parker Seemingly Spilled The Beans On Google's Android XR Smart Glasses Launch
"Intelligent Eyewear" is apparently only a week away.
AI divorce advice burns business owner
A business owner whose company was generating strong sales but leaving him with little income turned to artificial intelligence for advice on reducing his spousal support payments.
Everforth awarded potential $200M+ federal contract
Everforth ECS has secured a contract extension potentially worth more than $200 million to help the National Geospatial-Intelligence Agency.
Clemson Business welcomed top AI and consulting industry leaders for Power of Leadership Speaker Series
For consulting teams using artificial intelligence, the first question remains a familiar one: What business problem needs solving? That was a recurring
AI For Science & The Fear Of Missing History
Bern, 1905. A young patent clerk named Albert Einstein published four papers that transformed physics. More than a century later, scientific breakthroughs still tend to be associated with exceptional people and years of painstaking work. Artificial intelligence (AI) may change the pace of that work. Models are beginning to help prove mathematical results, spot patterns […]
Motorola Signature 27 gets Pixel’s Gemini Intelligence features with Rambler, Create My Widget
Motorola is adopting Google’s Gemini Intelligence features in the first major expansion beyond Pixel, with Gboard’s Rambler and more supported....
South Korean director Yeon Sang-ho revisits AI themes in latest thriller 'Paradise Lost'
South Korean director Yeon Sang-ho explores artificial intelligence in his films, including his latest, “Paradise Lost.”
South Korean director Yeon Sang-ho revisits AI themes in latest thriller 'Paradise Lost'
South Korean director Yeon Sang-ho explores artificial intelligence in his films, including his latest, “Paradise Lost.” | 830 WCCO
In an AI World, Sport’s Competitive Advantage May Be Its Oldest One
Today’s guest column is from professors John Cairney and Rick Burton. We know with relative certainty, professional and big collegiate sport organizations are spending heavily on artificial intelligence. Player performance, ticket pricing, fan engagement—GMs and ADs alike believe that there is a new “secret sauce” available, and the evidence is pretty compelling. AI offers the …
When papers become currency: a Chinese debate about trust in science
Last Saturday, 31 October, I went to the University of Hong Kong’s Beijing Centre for its Talent Forum. It was the fourth stop in the series this year, following events in Shanghai’s Zhangjiang, Shenzhen’s Qianhai, and on HKU’s main campus. The programme covered a remarkable distance in one afternoon. HKU’s Xiao-Jing Wang discussed computational neuroscience, intelligence, and the prefrontal cortex—before even delivering his inaugural public lecture to students back in Hong Kong. His colleague Bing Zhang took us from the Big Bang to artificial intelligence. Tsinghua’s Xue Lan asked how AI might reshape the institutional infrastructure underpinning higher education. There were lively panels featuring industry leaders from JD.com, Xiaomi, Haier—and Kaiser Y Kuo on the closing panel discussing innovation and international connections. I cannot pretend to have followed every detail of the brain models or the astrophysics. The talk that stayed with me was closer to something we have covered before: Zhou Zhonghe 周忠和 on papers, evaluation, and scientific trust. Zhou is a palaeontologist, an Academician of the Chinese Academy of Sciences, and an international member of the US National Academy of Sciences. He is also editor-in-chief of The Intellectual (知识分子), China’s leading science commentary platform. His subject was uncomfortable enough for a room full of successful academics: what happens when the system for recognising good science starts rewarding something else? Part of my interest came from our sister newsletter, The East is Read, which covered the controversy around “Geng Tongxue” in May. A PhD dropout from Beihang University, Geng has publicly challenged papers by prominent researchers through detailed online allegations of manipulated images and fabricated data. At Tongji University, an investigation into a challenged Nature paper found academic misconduct, and the dean of its life sciences school was removed. The affair brought doubts about academic integrity well beyond laboratories and university committees. How could someone outside the established hierarchy spot problems in papers that had passed through prestigious journals and institutions? And what, exactly, had all that prestige guaranteed? Zhou approached these questions through two essays published in July by The Intellectual: one by Wang Xiaodong, director of the National Institute of Biological Sciences, Beijing, and a response by Wang Liming, of Shanghai’s Shangsi Institute of Natural Sciences. Both began with the controversy over Geng. Their arguments deserve a wider audience, so I want to put parts of them alongside what Zhou said on Saturday. Wang Xiaodong’s starting point is evolution. A research system selects for the behaviour it rewards. Grants, jobs, journals, talent programmes, and prizes collectively determine which scientists prosper and which practices get passed down from advisors to students. “Whatever the system rewards is what it will evolve to produce,” he writes, citing the ancient Chinese saying: The King of Chu loved slender waists, and many in the palace starved to death. That is a more demanding diagnosis than saying there are dishonest people in science. Punishing a fraudster does not change the selective environment that made a particular kind of research profitable. His account also gives scientific success an awkward role in the story. As Zhou put it on Saturday, science became a victim of its own success. Once research became central to industry, national power, and economic growth, universities and laboratories expanded, and public money followed. That created an administrative need for rapid decisions: who should get the next grant, the next laboratory, the next appointment? The difficulty is that the true value of a discovery may take years or decades of testing and building to become clear. Resource allocation cannot wait that long. Publication counts, journal prestige, impact factors, and citations therefore become convenient ways to make an instant judgement. “The real problem is not that these indicators have no value,” Wang Xiaodong writes, “but that they are trying to answer a question that only time can answer.” Telling administrators simply to stop counting papers will never be enough. They still have budgets to distribute and appointments to make. Something has to replace the shortcut. Zhou described the consequences inside publishing. Papers acquire ever more requirements: a complete story, polished presentation, extensive datasets, and accessibility to readers beyond the immediate field. Individually, those demands sound reasonable. Together, he warned, they produce formulaic “eight-legged” papers 八股文 and rigid reviewing, while genuinely original or non-consensus work struggles to get past journal editors—some of whom wield immense gatekeeping power despite having limited laboratory experience themselves. Meanwhile, the demands for sprawling, “complete” narratives have fuelled a wider replication crisis. Wang Xiaodong uses a mahjong metaphor to describe the result: put the complex papers published by the exact same lab over several years side by side, and they often look like “Thirteen Orphans” (十三不靠)—every tile completely unrelated to the next, and none of them reproducible by outsiders. Zhou also spoke frankly as an editor who has trouble finding reviewers. Everyone wants someone else to review their work; qualified senior researchers have little time to return the favour. The labour is anonymous, unpaid, and brings almost no institutional recognition, so it increasingly gets passed down to postdocs and graduate students. And while anonymity protects reviewers from rank and personal favours, hyper-specialisation means an author’s most qualified reviewer is often a direct competitor, shielded from accountability. The protection and the problem coexist. Wang Liming takes the argument a step further. The decisive change, in his essay, is the instant conversion of academic honour into material resources. Once a paper can be cashed in for funding, a job, students, commercial investment, or even a company valuation, scientists are alienated from their work: they must keep minting this “research currency” to keep their teams alive. He is careful not to romanticise the past. The old world of gentleman scientists—financially independent figures like Boyle and Darwin who needed nothing from external funders—was also a closed club that excluded people without inherited wealth, women, and much of humanity outside the West. Professional science allowed ordinary people, himself included, to become researchers. Nobody should want to undo that. The problem, in his account, is an exchange system that becomes too uniform and too difficult to correct. His most pointed Chinese example is the “hat” (帽子): the prestigious talent designation that brings continuing access to money, laboratory space, students, and institutional influence. “If papers are the circulating currency of the research system, then a ‘hat’ is a bond that pays interest over the long term,” he writes. A paper at least requires another round of peer review. A title in the Chinese context can lock in a judgement made years ago and turn it into permanent status rent, regardless of whether its holder is still doing good science. Here, official institutions hire, organise, and evaluate scientists. Unlike honours awarded by autonomous academic communities in the West, a Chinese “hat” blends non-scientist administrative power with state authority. Once granted, it turns a past judgement into permanent status rent that is nearly impossible to revoke—because taking a hat off would imply that official authority erred in putting it on. Both Wangs see China as experiencing, at compressed speed, pressures present across global science. Research expansion, rising investment, publication growth, and elaborate evaluation metrics arrived in China within two or three decades rather than two or three centuries. Zhou agreed with that international framing—noting that recent US White House science policy documents and the new president of the US National Academy of Sciences, a fellow palaeontologist who recently visited Zhou’s institute in Beijing, have raised strikingly similar warnings about consensus-bound peer review, short-term project grants, and the replication crisis. Still, Zhou emphasised that China’s traditional hierarchy, personal networks (guanxi), and the ethos that “a good scholar becomes an official” (学而优则仕) amplify those pressures at home. So what would change things? Here Zhou’s tone differed from the call for a revolution in scientific trust in Wang Xiaodong’s essay. As an evolutionary biologist, Zhou favoured patchwork repair (修修补补). Living systems survive through accumulated adjustments; dramatic mutations are usually fatal. Science cannot return to a small, slow-moving pastoral era, especially after the successive accelerations of professionalisation after the Second World War, the internet, and now artificial intelligence. But it can alter the local environment in which researchers work. He pointed to domestic experiments designed to support unconventional research and diversify funding, including Tencent’s New Cornerstone Investigator Program, the Xplorer Prize, and the Future Science Prize. He also mentioned participating in reviews for “major non-consensus projects” under the National Natural Science Foundation of China. One guiding principle he recalled was wonderfully direct: “Failure is allowed; corruption is not” (允许失败，不允许腐败). That distinction would mean quite a lot if taken seriously. An honest attempt that fails should not have to disguise itself as a success to survive an assessment. Wang Liming’s essay adds a sharp caveat to the optimism about new domestic funders. Multiple organisations do not automatically produce multiple ways of judging science. If the same pool of senior domestic scientists makes the decisions through the same two-round review procedures, it is still “fake diversity.” He argues for three distinct kinds of money: broad baseline funding governed by fair procedures; patient funding for questions that take ten years; and non-consensus funding based on individual taste, deliberately empowering a few genuine experts to make biased, contrarian bets on eccentric ideas that would inevitably be averaged out by a committee. Replacing one monopoly with another achieves little; a healthy ecosystem needs variance. Zhou’s proposals for publishing were similarly concrete: strip papers of their resource-allocation burden and return them to recording discoveries and establishing priority; separate dissemination from evaluation, as eLife and Peer Community In do; adopt more modular “micro-publications” and real-name endorsements; recognize the labour of editors and reviewers; and give replication work proper credit rather than treating it as an unpublishable dead end. AI makes every part of this debate more urgent—and Zhou wove it throughout his talk. In his own preparation, he used AI to survey the declining influence of academic monographs, noting how tools are already changing how scholars gather information. More fundamentally, he placed AI as the latest great acceleration in scientific history: just as the internet sped up the pace of research and tightened external resource pressures, AI is compressing the cycle again. In the essays Zhou cited, AI cuts both ways. Wang Xiaodong expects large language models to erase much of the English-writing disadvantage long faced by Chinese scientists, further swelling the flood of papers coming out of Chinese labs. Wang Liming sees AI accelerating both ends of the trust crisis: it lowers the cost of paper mills and fabricated images just as quickly as it lowers the cost of automated image forensics, data auditing, and code reproduction. “Whoever builds the verification layer wins,” Wang Liming writes. His proposed alternative to the static, packaged “story paper” is a continuously versioned “unit of discovery” containing a falsifiable claim, raw data, analysis code, experimental parameters, and an ongoing log of independent checks—where negative results and failed replications are recorded rather than buried. Zhou ended his keynote by bringing AI back to the question of scientific honour and credit. Citing a recent Intellectual discussion on “What AI Means for Mathematics” featuring Fields Medalist Deng Yu 邓煜—at a moment when AI is already batch-solving mathematical conjectures, as HKU Vice-President Lin Chen noted in his opening remarks—Zhou agreed that human researchers must remain the principal agents: AI is a tool that frees scientists from tedious calculation to think about deeper, messier real-world problems. Yet that partnership creates a thorny new evaluation problem. When a breakthrough is made with AI, what is the human contribution and what is the machine’s? And since AI models distil centuries of accumulated human scholarship, is it right that the credit and value flow so heavily to the private AI companies building the tools? The old struggle over how to assign scientific honour is acquiring a whole new layer. What I took away was a serious discussion of how science can remain trustworthy as its scale, incentives, and tools change. These were scientists with real standing inside China’s system questioning how that standing is produced, and proposing changes that would affect their own institutions and peers. That made the afternoon at HKU’s Beijing Centre worthwhile. At the close, Wang Feng, Director of Communications and Public Affairs at HKU, said the value of the forum series—which heads to Hangzhou in December and cities like Chengdu early next year—would lie in the real cooperation it helped bring about. The university has plenty to celebrate: this week it topped the QS Asia rankings for a second consecutive year, alongside its 11th-place position in the QS World University Rankings 2027. For me, bringing this debate to an audience beyond the people who review papers and allocate grants was a useful contribution. Share [ The East is Read PhD dropout’s exposé forces an academic fraud reckoning in China A former doctoral student turned blogger has shaken China’s academic establishment by publicly accusing prominent university scholars of fabricating data and manipulating papers, triggering investigations at several leading institutions and drawing rare attention from state media… Read more 5 months ago · 43 likes · 8 comments · Yuxuan JIA ](https://www.eastisread.com/p/phd-dropouts-expose-forces-an-academic?utm_source=substack&utm_campaign=post_embed&utm_medium=web&embedding_publication_id=47580)
AI threats challenge cybersecurity landscape
Artificial intelligence is accelerating cybersecurity threats against businesses and government agencies, allowing criminals to potentially impersonate employees, steal sensitive information, and compromise corporate networks faster than organizations can respond, warn some cybersecurity experts.
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.
Star mathematician Ma Xiao leaves the US for China – and a career pivot to AI
The 28-year-old helped achieve a breakthrough on Hilbert’s sixth problem and now he’s embracing the ‘new era’ of artificial intelligence.
Simonelli Group unveils Science Driven Coffee Cup programme
The Simonelli Group and the University of Camerino have unveiled the programme for the inaugural Science Driven Coffee Cup, bringing together leading researchers and coffee professionals to explore advances in coffee chemistry, neuroscience, artificial intelligence, sustainability, and quality control at the conference in Italy on 19-20 November.
Africa and Europe seek their own path in AI science
Africa and Europe are seeking to chart their own course in the rapidly accelerating global artificial intelligence race, with a partnership built arou...
Mapping the hidden hazards beneath our feet | Investors Lab
From unstable slopes to former industrial sites to hurricane alleys, some places are not safe to live in. Yet circumstances often force low-income populations into these areas, where natural disasters can have devastating effects. Rebecca Dzombak fills in the details for SN. And we have intel on advanced imagery and subsurface mapping that aim to provide early warnings before disasters strike.
Bradbury Science Museum Presents ‘Night With A Nerd: Neuromorphic Artificial Intelligence Wednesday Nov. 4.
BRADBURY SCIENCE MUSEUM NEWS RELEASE The Bradbury Science Museum will present ‘Night with a Nerd: Neuromorphic Artificial Intelligence’ 6-8 p.m. Wednesday Nov. 4. The community is encouraged t…
FSU Partners With Special Olympics Florida to Expand Access to Health Screenings
Florida State University students and faculty partnered with Special Olympics Northwest Florida to provide accessible health and wellness screenings for individuals with intellectual and developmental disabilities at The Arc Big Bend in Madison, Fla. on Sept. 23. The Healthy Hearing and Strong Mind event brought together audiologists and students from the School of Communication Science…
Waystar touts bigger health system wins and growing AI pipeline
Healthcare payment software maker Waystar again outperformed analysts’ estimates in the third quarter as the company builds out new artificial intelligence capabilities and drives growth by adding | Waystar again outperformed analysts’ estimates in the third quarter as the company builds out new AI capabilities and drives growth by adding new customers and selling more of its platform to existing clients.
Russia giving some missiles greater range, larger warheads: Ukraine
The new assessment from Ukraine's military intelligence said Russia has stockpiled thousands of ballistic and cruise missiles.
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.
HCPSS Teams Up with Student Group to Drive Student Interest in and Understanding of Coding, Computer Science, and Artificial Intelligence
Several years ago, Julie Wray and Carrie Trudden, Howard County Public School System’s (HCPSS) then- and current coordinators of Instructional Technology, began receiving emails from a group called Howard County Hour of Code (HocoHOC). At the time, Wray and Trudden were unfamiliar with the group and skeptical about its offer to partner with the district […]
Advito Announces Launch of AI-Enabled Consulting Intelligence Platform
Pilot will roll out through Q1 2027
Disney has fought AI firms over copyright. Now it’s testing AI lyric videos
Walt Disney Co. is experimenting with using artificial intelligence to test out different concepts for music videos showcasing the lyrics to its songs, starting with “Go the Distance” from its
AI boosts operational efficiency but not systemic climate action, global study finds
Artificial intelligence (AI) is widely promoted as a tool for sustainable development, but whether its operational gains translate into deeper ecological transformation remains uncertain. A new global assessment examines how AI diffusion relates to seven core United Nations Sustainable Development Goals (SDGs), covering health, education, clean energy, industry and infrastructure, sustainable cities, responsible consumption and production, and climate action. It also explores the channels behind
How the Stock Market Is Reacting to the Midterm Elections
U.S. stock futures fell as the first results came in, but later traded higher as investors assessed the outlook for artificial intelligence and other key sectors as the balance of power shifts in Washington.
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
But WHY Does Intelligent Design Predicts Ultimate Engineering?
A possible objection to Dr. Burgess’s case for ID might be that the apparent superb design of the human body is what you’d expect from evolution.
The Waiting Begins
Millions of Americans headed to the polls Tuesday for a midterm election that could shape the future of health care and artificial intelligence in the U.S. and will determine how much Democrats can try to constrain President Donald Trump during his last two years in office. The political contest has ...
Recon: Merck KGaA to exit CDMO business; Karyopharm to sell itself in bankruptcy
Welcome to Regulatory Reconnaissance, your regulatory news and intelligence briefing.
Bradbury Science Museum To Present ‘Night With A Nerd: Neuromorphic Artificial Intelligence’ 6-8 p.m. Wednesday Nov. 4
Bradbury Science Museum News:
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.
Apple’s latest watchOS beta includes its new always-on Audio Intelligence features
Siri is listening.
iOS 27.3 Beta Brings Back Apple's Redesigned Health App
iOS 27.3 reintroduces the redesigned Health app previewed in September. Apple tested the new Health app in the iOS 27.2 beta, but it was removed before the update launched. The Health app has been updated, and there are new Insights and Longevity tabs that show more information about your health and fitness metrics. The Insights tab includes your most recent health data, like Activity Rings, heart rate, sleep score, and vitals. Apple Intelligence will use that information to offer Personal Guidance.
Your Apple Watch Can Automatically Identify Songs Around You
Ever been in a situation where you hear a song in a cafe, a shop, or a car, and wonder what it is? On the latest Apple Watch models, your watch can work it out for you, and you don't even need to ask. The music doesn't have to be playing on the watch. It just needs to be audible around you. Apple Watch does the rest. It's all thanks to a new automatic feature in iOS 27 called Music Recognition, which is just one of Apple's new Audio Intelligence features.
OpenAI: ChatGPT Search Is About 20% of Google’s, 250 Million People Speak With ChatGPT, and Chat Is Merging With Work
OpenAI says ChatGPT growth is accelerating. The company added 200 million users over just the past two months, jumping from 1 billion to 1.2 billion weekly active users in 60 days. Its search volume, it says, is approximately 20% of Google’s. Its users create 1.5 billion images with it per week. And its voice mode, between dictation and speaking with ChatGPT, now has 250 million combined weekly active users. ChatGPT consumer product lead Adam Fry told me this during a brief fireside chat at an OpenAI-hosted dinner in New York City on Monday night. “The growth is just going big,” he said. During our conversation, Fry also revealed plans to merge ChatGPT’s Chat and Work modes (so that confusing toggle up top will soon go away), agreed with feedback that ChatGPT-created restaurant signage looks too uniform, and addressed the cost to serve its personal assistant Dots. Here’s a look at our full conversation, lightly edited for clarity and length. We also cover image generation, interactive interfaces, advertising, education, and public unease about AI: Alex Kantrowitz: OpenAI has analyzed ChatGPT’s search volume and compared it with Google’s. What did you find? Adam Fry: ChatGPT has been growing quite quickly. ChatGPT’s volume of search-like queries is now roughly one-fifth the volume of queries of Google. It’s a pretty amazing statistic when you think about it, mainly because we started just about a year and a half ago. It’s hard to remember this, but ChatGPT at a time didn’t have any up-to-date information. You’d ask a question, it would say something like, “It’s past my training date,” and nobody had any idea what that meant. You’re saying ChatGPT’s search volume is one-fifth of Google’s. Bing’s share of the search market is around 5%. Does that make you multiple times larger than Bing? To say that more directly, ChatGPT has gone from one billion to 1.2 in just the last two months. That’s roughly 100 million users in 30 days, and another 100 million in another 30 days. The growth is just going big, and we’re staying very focused on how fast ChatGPT is growing. What counts as a search? I might have 20 prompts back and forth in a ChatGPT conversation. Does each one count as a query? Story continues below… A MESSAGE FROM OUR SPONSOR Move upmarket without a rebuild Moving upmarket means saying yes to what enterprise buyers ask for first: SSO, directory sync, and audit logs. WorkOS gives you all three, so your team keeps building the product instead of the checklist. Get enterprise-ready. Continued from above…. Adam Fry: What people came to ChatGPT for to start, if you go back about two years, is really writing help and coding help. Those wouldn’t be things that you really search for. What we’ve seen over the last year is people have grown into all these other topics. Things like you’re looking up a person or people or a place or a thing, and it requires up-to-date information. That’s a key element. Another big part is finance questions or health questions, where you really want to make sure it’s up-to-date information that’s quite factual, and we’ll do a web search for those questions. It’s basically anything that a user would have traditionally gone to a traditional search engine for, but is now deciding to go to ChatGPT for. Does each conversation with a search embedded count as one search, or does every question that requires a web search count? Every time you ask a question and go search the web, that’s a search. You used to work at Google. How would you say the nature of search is changing? One of the things that people really love about ChatGPT is this personalization and this understanding of the broader journey. If you look in the past, people would go for a Q&A box, and you’d ask a one-off question, like how tall is the Eiffel Tower. But there’s so much context in our lives around, “Maybe I’m taking a trip to Paris.” It’s actually helpful for ChatGPT to respond, “Here’s how tall the Eiffel Tower is, but I know you’re going on a trip to Paris.” It’s that ability for people to actually maintain context over time that has led them to start using ChatGPT in this way. You’ve also announced 250 million weekly active users of voice input, including dictation and conversations with voice mode. We are at 250 million weekly active users. What’s amazing about this is, if you look at those 250 million, what we’re seeing is it’s actually also incremental to the product. It’s no longer just a novelty. People aren’t just trying out voice. We’re seeing them use voice input day to day in their daily life, and it’s grown so quickly. Those 250 million users are using voice input, and that could just be a quick question or a long conversation. We’re seeing both behaviors. We’re seeing people just not want to type. You just hold it and start talking to it, or it turns into a longer conversation where people just sit with it. The big reason for this is the frontier capabilities of our models that we’ve shipped. We shipped what we call GPT Live 1, and then we shipped GPT Live 1.1. The big change with this is that it’s much more natural to speak with. It’s something we call backchanneling and interruptibility. Mhhhm… What you just did there is what the model does, because it’s really good at having a bidirectional conversation. That push to the frontier—we were the first to really release this at scale—has made it so much more natural for people to chat. That’s still only about 20% of the user base. From the outside, that looks small. Why is it exciting from the inside? It’s very exciting for a few reasons. We see from the data that when people start using voice, they stay around using voice. One of the things, transparently, we can do a better job of is bringing more people into discovering it. We see that the number of people who discover it could be a lot higher. But once they do, it’s a really new way for people to interact with AI. We’ve made a lot of improvements to it too, where we’re just going to see the number go up. You have background mode, so now by default, if you close the app, it’ll show, “Hey, the conversation’s still going.” If you want to walk around the house or you’re putting away the groceries, you can still have a chat. That kind of continuity has also changed how much people use it. Dots launched on September 29 and is about a month old. How’s it doing? It’s doing great. We talk to people about their Dot, and the first thing that everyone says is, “I love my Dot.” One of the reasons we’re seeing this is the proactivity, its ability to reach out to you and figure out things. The biggest use case I see all the time is, “Did I forget something today that I was supposed to do?” You can just say to your Dot, “Remind me of things at the end of the day that I may have forgotten. Somebody emailed me or messaged me,” and it’ll do that. This Dot changes the paradigm of the relationship with AI, where it’s actually helping you and giving you time back. You’re more conservative with permissions than some competitors. I tried to give my Dot my credit card, and it wouldn’t take it. With the creator of OpenClaw in-house, why are Dots’ permissions more limited? This is a space where we want to be very thoughtful and conservative because it’s a new space where you’re entrusting your Dot to take care of things on behalf of you that matter and are meaningful. We’ve just talked about how you go to this 1.2 billion user growth and asking questions. Now we’re asking people, “Can you ask ChatGPT or your Dot to help get things done?” But when you ask it to help get things done, it’s a whole new level of trust. We’ve built up a tremendous amount of trust in giving the right answers to your questions, and we want to make sure that when we build this new product, it has a high degree of trust. It’s a long game. We think this is going to be a new market for a very long time, and we want to make sure that we’re being this trusted player in the market to start. I initially thought Dots would be available on my $20 plan, then realized I had to spend $100 a month to get it. I upgraded, but I’d like to downgrade at some point…. We hope to keep earning your business and make the value good enough. It’s a really good point. ChatGPT’s goal more broadly, which I think is unique for us in the market, is we are focused on bringing intelligence to everyone safely across the entire world. For Dots in particular, there’s no difference. We don’t have anything to share tonight about the date of when Dots will go to a broader set of users. But you can rest assured we’re working very hard, and there are a couple things we have to get right. Over time we’re going to work on more efficient, agentic models that are able and capable of doing things for you. The first one is, right now we wanted to make sure that your Dot is actually really helpful. We started with some of our best models, but over time we’re going to work on more efficient, agentic models that are able and capable of doing things for you. The second thing is around proactivity. We’re still dialing in: How much do I message you to help you get things done? We can always play around with that to understand how much usage should a user use to have a successful Dot. We’re going to work on both, and our goal and ambition is to bring it out to all users. How much background computing does a Dot require? I’ve read estimates that serving a $100 subscriber might cost OpenAI $3,000 in compute, and mine seems to read every email that arrives. How often is it checking connected data sources? Right now, Dots are on the compute-heavier side, and this is one of the things we want to make sure that we can dial back over time. But the goal is to give you the best product. This is our premium, always-on personal assistant. Once we nail that, we’ll figure out how to dial back the compute needs. The thing we’re seeing also from our models, as we train frontier models, is just how—I can’t even keep up with how quickly we have a new model, and each new model is more efficient. If you looked at 6.1 Sol when it came out relative to Astra, much more efficient. I’m very excited about the next set of models we have coming too that’ll be even more efficient that we can use. Elon Musk said the cost of serving Grok Bot was unsustainably high relative to revenue. Is efficiency the key to winning against competitors, or is there more to it? There are a couple of factors that go into it. One is the cost side, but the other is the revenue side. On ChatGPT and on our consumer business, we’ve been extremely proud of our ability to diversify different revenue streams in a way that’s growing really fast. To give you another stat, we announced several months back that we had passed 50 million subscriptions, and the number has grown considerably since then. On ads, within 200 days, we reached more than a billion in ARR, and that business is growing extremely quickly. We also have credits. Rather than paying for subscriptions, users can buy more credits. Between credits, subscriptions, and ads, I’m confident that the value of a Dot is going to be able to pay for itself. Elon has subscriptions too. This is clearly an expensive product to serve. One thing we continue to bet on at OpenAI and ChatGPT more broadly is that the models will continue to get much better and much cheaper, way faster than any of us think. If you look back at the historicals on it, it continues to be true. From what I’ve seen, I see no slowing down of that. What is Meta doing right with Muse? It has spent a lot of time atop the App Store charts. There are a couple of things they’ve done well. The first is people have been craving simplicity in their products. One of the things we have stated publicly as well is, at times AI products can be too complex. We’ve announced we’re merging ChatGPT’s Chat and Work. We’re making it much more simple in the products. You don’t have as many toggles or sliders between efforts and different model pickers. One of the things they’ve done well is this simplicity. You just go in and you can ask a question. You don’t have to think too much about what you’re doing. That’s one thing that seems to have resonated with users, which we also believe in. Second, there’s something really nice about being able to access your agent across many different channels. We just announced and launched Dots on iMessage. Whether you talk to your Dot on email or iMessage or within the ChatGPT app, the ubiquity in getting your assistant out there is going to be key. Those are two things that, if I had to be nice to our competitors, I would say were two big ones. So Alexandr Wang’s meme-tweeting strategy isn’t something you’re going to emulate? I have a different tweeting style. ChatGPT still asks me to choose between Chat and Work, even though some people call this technology AGI. Will that distinction go away? Couldn’t agree more. We have to solve this. Asking users to choose — we’ve heard repeatedly from all of our users it’s too much to understand. When should you go to Chat? When should you go to Work? It’s solvable. There’s real research to solve to make sure the models can understand what your intent is. Are you asking for a fast, quick reply? Are you asking me to go do a long agentic task? We’re going to solve it, and the plan is to bring Chat and Work together. So one interface will work out whether I’m asking for a conversation or a task. When? Coming soon. You told me the majority of ChatGPT’s 1.2 billion users are using image functionality. What are they doing? Today, 1.5 billion images are created per week. It’s hard to even wrap my head around that number. They’re doing a wide range of things. One of the most interesting things they’ve started to do is really use it for productivity use cases. The first thing it started with—I don’t know if you saw the ’80s trend, but it was quite fun. You could take a photo of yourself and say, “Put me and my family in ’80s outfits.” I did this for my family, and it was a really fun poster. But what we’re also seeing is businesses for their menus, or posters for birthdays. People are using it almost in a productivity-like fashion to create visual diagrams of what they would like to see. We’ve actually brought the latency down on images as well, so it’s gotten a lot faster. It went from a very high number of seconds to getting much faster. As it comes down even further, the number of images people are creating and the number of use cases is going to grow a lot. My feature request: When a restaurant makes a menu or sign, prompt it for more specificity so it doesn’t look like every other AI-generated restaurant sign. Or offer a shuffle button for a different style. I agree with the feedback, and we’ll take it to the team. I’m seeing ChatGPT generate little interactive interfaces within its answers. What’s the direction for Intelligent UI, and how are people using it? I am extremely excited about Intelligent UI. If you go back a couple years, these chat apps were all text, and now that’s changing. They’re going to be visuals. Intelligent UI and our first attempt at this with GPT-6 Intelligent UI is really just the beginning. As you go to the extreme, what you can imagine is, let’s say you’re using voice mode, and you just say, “Show me how offside works.” Immediately it should show you a visual diagram, and you say, “No, I don’t quite understand it.” It should just repopulate. That’s where the future of this technology goes. It’s almost like you’re talking to a canvas that will update and change. People are finding it helpful for a lot of things. My favorite one is—it’s hard to admit that I had no idea how hearts work. The last biology course I took was in high school. I just asked it, “How does the heart work?” and it instantly gave me a beautiful interactive diagram of where blood flows and how it goes through. I’m really, really bullish on this for learning in particular. You’ve described useful applications in finance, health, and learning, and ChatGPT has 1.2 billion users. If people are getting so much value, why is public polling on AI so negative? The reality with AI is it is going to change the nature of how work gets done. For a lot of people, that is something that can feel scary at times, and we’ve all felt that. But I remain confident that when people actually use the product and they use AI, it very quickly changes how they perceive it. We’re at 1.2 billion, but there are still a lot of people who haven’t used it. The more people use it, get familiar with it, how it can actually help you in your life, it’s going to feel a lot less scary. Big Technology is a reader-supported publication. To receive new posts and support our work, please consider becoming a paid subscriber. Thanks!! Subscribe
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I spend my working life on both sides of a business sale. As an M&A advisor based in Wilmington, I help lower middle market owners across North Carolina, South Carolina and Virginia sell what they've built. As the founder and CEO of IQExit, I work with the bankers, wealth advisors, CPAs and attorneys who sit beside those owners for years before a sale ever comes up. From both seats, I keep seeing the same thing. The market is ready for our owners. Too many of our owners aren't ready for the market. Start with our own backyard. Wilmington's metro area added more than 12,000 residents between July 2024 and July 2025, a 2.6% jump that ranked seventh in the country. It also posted the highest job growth rate of any metro in North Carolina. I see the same pattern in the growth markets I work in across the Southeast. New residents mean new customers and new workers. They also mean new buyers: people who moved here from corporate jobs and want to own something, private equity groups looking for add-ons in HVAC, electrical, landscaping and other trade services, and younger entrepreneurs who would rather buy a running business than start one from scratch. The ownership numbers are hard to ignore. About half of North Carolina's small businesses have an owner who is 55 or older, and roughly 85% of those owners have no succession plan, according to the North Carolina Employee Ownership Center. Nationally, more than half of Baby Boomer business owners have no documented transition plan. That isn't a crisis yet. It's a timing problem. A lot of businesses will change hands over the next decade, and the prepared ones will get the better buyers and the better terms. Buyers have money and they're using it. But an active market doesn't mean a fast one. The IBBA's latest Market Pulse survey found that Main Street deals took six to 10 months from engagement to close in the second quarter, and lower middle market deals took 11 to 12 months. That clock starts after the owner decides to sell. In my experience, the real preparation has to start years before. Two clocks, two playbooks When I sit down with an owner or their advisor, my first question isn't “What's it worth?” It's “When?” The answer puts the owner in one of two groups. If the owner wants out within 24 months, we're in execution mode. There's no time to rebuild the business. The work is cleaning up what a buyer will test: three years of financials that tie to the tax returns, an add-back schedule you can prove, customer and vendor contracts in writing, a lease that can be assigned, and a price the market will actually pay. The goal is certainty to close. If the exit is two to five years away, the owner has something worth more than time. They have choices. That window is where most value gets built, and most owners waste it by waiting. What kills deals In the lower middle market, deals rarely die over the headline price. They die in diligence, or they get retraded late in the process. I see the same causes again and again. The owner is the business. If every key customer relationship runs through the owner's cell phone, a buyer sees risk and a lender sees more of it. The books don't hold up. Buyers spot weak accounting fast. Statements that get revised mid-process, a revolving door in the bookkeeper's chair, revenue tracked on a side spreadsheet, and margins that slip while sales climb with no clear reason all make a buyer nervous. So does an owner who pushes back when asked for documents. And plenty of private owners run personal expenses through the company. Add-backs are normal. Add-backs you can't document turn into a lower offer. The real estate isn't sorted out. Many owners hold their building in a separate LLC. That's fine, but the lease terms or the property sale should be settled before a buyer shows up, not halfway through diligence. Revenue sits in too few hands. Picture a contractor with 40% of its work coming from one builder, or a service company running on handshake deals. Buyers will still look, but the terms shift toward more seller financing, an earnout, or less cash at closing. Owners and advisors: if your exit is three to five years out • Get a baseline now. Find out where the business really stands today: what a buyer would pay, who that buyer is likely to be, and which gaps would hurt you in diligence. You can't fix what you haven't measured, and the answer is often different from what the owner expects. • Make yourself less necessary. Spend the next few years building a manager or small team that can run daily operations and own customer relationships. Put handshake deals into written contracts. A business that runs without its owner is worth more and is far easier to finance. • Clean the books for three straight years. Buyers and lenders look back at least three years. Separate personal expenses, keep the financials tied to the tax returns, track every add-back as you go, and settle the real estate plan early. A word to CPAs CPAs often see a sale coming before anyone else, and you're usually the most trusted voice an owner has. Year-end meetings are the right time to start this conversation. Here's what I'd ask every CPA to do this season with business owner clients over 55: • Ask the timing question. “Do you see yourself owning this business in five years? If not, who takes it over?” Write down the answer and bring it up again at next year's meeting. • Run a proof of cash and review revenue recognition. Make sure the cash in the bank ties to the revenue on the books. Pay close attention to contractors with work in progress and to companies selling memberships or service plans. These issues are easy to fix with years of lead time and painful to fix a month before closing. • Pair the tax return with an exit readiness review. Invite clients to complete IQExit's Exit Readiness Intelligence (ERI) process as part of year-end planning. It gives you and the client a shared picture of readiness gaps and an indicative value range, and the results come back to you as the advisor. That turns a tax meeting into a planning relationship, and it puts you in position to bring in tax and M&A help early, when choices like purchase price allocation can still save the client real money. Changing the conversation For bankers, wealth advisors and attorneys, the hard part is raising the subject without seeming to nudge a client toward the door. I understand that. Nobody wants to tell a 60-year-old owner it's time to sell. So don't. Ask a different question: “If a buyer called tomorrow, what would they find?” That's a planning question, not a pitch, and it's useful whether the exit is two years out or 15. It's the question we built IQExit to help advisors answer. Whether you're in Wilmington, Raleigh, Charleston, Richmond or anywhere else, owners have a real opening right now. The buyers are out there and so is the capital. The owners who come out ahead will be the ones who start getting ready before they're ready to sell.
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But several parents and teachers who attended an evening forum on the state’s proposed science standards criticized the proposal by the Florida Department of Education, which Mack runs, specifically the handling of evolutionary science.Joshua Garrett, a teacher in Orange County Public Schools, said the state’s proposed standards “danced around” evolution.State standards are benchmarks for what students should learn in their classes kindergarten through 12th grade, guiding what lessons are taught, what subjects are in state-approved textbooks and what concepts are tested on state exams.Florida’s current science standards call evolution a “big idea” and “the fundamental concept underlying all of biology.”The proposed ones barely mention evolution — a scientific theory explaining change in living species over time from natural selection that has been a cornerstone of biology for more than a century. The term “evolution,” in fact, is never defined in the new high school life sciences standards, though the concepts are sprinkled across several units.The education department has made revisions to its proposal, officials said, but it was not immediately clear at the Tuesday forum, held at Lake Buena Vista High School in southwest Orange County, what those changes were.Mack interjected during Garrett’s comments asking him where specifically he wanted the state to mention evolution more prominently. Then he said, “Consider it done.”Mack also asked Garrett to join the state’s faculty committee on the standards.The state released the new standards in September, and several experts quickly criticized them, saying they lacked depth and skirted around both evolution and climate change, important topics for Florida students to learn.The state has since hosted a “listening tour,” including a virtual forum and two other in-person meetings in Walton and Hillsborough counties. Tuesday’s meeting drew a small crowd of about 30 people, six of whom spoke.Mack described the state’s public forums as a chance for “myth busting” and to make clear Florida’s position.He wants to students to learn “above ideology,” he said, and added that evolution isn’t settled science. TNCMS.AdManager.render({region: 'fixed-big-ad-middle-asset', slot: 1, fold: "span"}); “I want to cultivate skeptics in the healthy sense of that term. As a result of these standards, students should not be walking away convinced of anything other than the rigor of the scientific method,” Mack said.The new standards would replace the current set, adopted after a monthslong, statewide fight over the inclusion of evolution as a required subject of study. Those who opposed the 2008 standards argued creationism, or the biblical story that God created all living things, should be taught alongside evolution.Thomas Simon, a Winter Park parent, compared Florida adopting the new standards to Nazi Germany ignoring the findings of Jewish scientists — which in part lost them World War II.“If this board could explicitly name communism in the history standards last year — because words matter — you must explicitly name the foundations of modern medicine, agriculture, and energy,” Simon said.Florida last year adopted new standards that require students learn about the “dangers and evil of communism.”Teri O’Connor, an elementary science teacher in Orange County, urged Mack to curtail the total number of standards, as they list more than 1,000 benchmarks teachers are to cover during their classes.“It’s only 180 days of school. That right there is an issue, especially if you want to dive in. If we need to teach the rigor of it and to critically think about each one, that’s difficult,” she said.Kathleen Murray, who spoke on behalf of the conservative political group Citizens Defending Freedom, said she was in “strong support” of the proposed standards. Science should teach children how to think, not what to think, she said.“When teaching the origins of life and the universe, the curriculum owes it to the Florida students to be honest about the limits of what scientific models can actually prove,” Murray said.State education officials said they’d received about 1,800 comments on the benchmarks, and they have updated them based on that criticism.With Tuesday the state’s final scheduled public forum, Mack said his biggest takeaway was that the standards needed to be more explicit about teaching students how to evaluate scientific studies and evidence and how to understand the scientific method.“We might have to do a better job of helping students question more as a result of these standards,” he said.In his original statement when the standards were announced in September, Mack wrote in a social media post that “science begins in wonder, not in ideology,” and “the classical tradition taught that nature is intelligible because it is ordered — that the book of creation can be read by reason.”Some read that to mean he was trying to interject religion into public school education. 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A golden age of science is coming
It’s almost hard to remember, but just a few years ago, a lot of very smart people thought that science was in terminal decline. Respected researchers were writing papers with titles like “Are Ideas Getting Harder to Find?”, “Papers and patents are becoming less disruptive over time”, and “Slowed canonical progress in large fields of science”. In 1996, science writer John Horgan even published a book called The End Of Science: Facing The Limits Of Knowledge In The Twilight Of The Scientific Age. The fundamental idea was that the Universe only has so many insights to discover, and that we’ve picked the low-hanging fruit. It was the idea that Joseph-Louis Lagrange expressed when he said that “we cannot find more than once a system of the world to establish.” As the big ideas and the simple ideas get mined out, we’re forced to throw ever more brains at the mysteries of nature just to keep scientific progress going at a fraction of its old pace: It looked like science was hitting a wall. Then along came AI. It’s far too early to tell how much AI will eventually speed up the growth of total factor productivity. But already we can see signs of a speedup in science. Anthropic Founder Jack Clark assembled some of the hopeful data points in a recent lecture: [ Dror Poleg@drorpoleg "If you pick different parts of science, you see the same pattern show up: Progress is accelerating, and it has started to accelerate in the last two years." Jack Clark @jackclarkSF I’m speaking at the IMF in an hour about the singularity and why I believe it has begun and the choices we'll need to make. Watch live here: https://t.co/Hj9HzcXx5h 6:08 PM · Oct 18, 2026 · 21.2K Views 2 Replies · 10 Reposts · 123 Likes ](https://x.com/drorpoleg/status/2111882070376980529) Anthropic is accelerating its investment into drug discovery, and is already announcing some impressive preliminary results: [ BioTender@Junioryu136689 BREAKING: Anthropic unveils early results from six Claude-powered drug development collaborations. • 70% shorter CAR-T signaling domain • ~35,000 antibody designs • >10× tighter antibody binding • 89% less batch-record review time Still largely preclinical. Anthropic @AnthropicAI Over the past few months, Anthropic scientists have engaged in deep research partnerships with six organizations developing new medicines. We've also begun drug discovery efforts of our own. In doing so, we’ve learned much more about how Claude can support the many stages of 9:43 PM · Oct 27, 2026 · 11.2K Views 3 Replies · 24 Reposts · 205 Likes ](https://x.com/Junioryu136689/status/2115197821150495150) Claude also appears to be enabling rapid progress toward a treatment for food allergies. OpenAI, meanwhile, announced a possible breakthrough treatment for neurodegenerative disease. Meanwhile, Google Cloud Chief Scientist Pushmeet Kohli has a list of recent scientific advances that have been enabled by its new Gemini model, including breakthroughs in nuclear fusion reactor design, rare disease research, and quantum computing: [ Pushmeet Kohli@pushmeet Gemini 4 Argon is a step change in model performance. In recent weeks, it has had a transformative impact on the Science projects in our team at @GoogleDeepMind and our external collaborators, and helped make a number of remarkable advances across scientific disciplines. 🧵👇 4:47 PM · Oct 13, 2026 · 36.5K Views 8 Replies · 41 Reposts · 497 Likes ](https://x.com/pushmeet/status/2110049807385702623) But as cool as these stories sound, they’re just very minor, preliminary efforts. There are a number of reasons to think that AI is going to propel us into a golden age of scientific discovery. That age might not last forever — AI might still run into diminishing returns eventually. But the next few decades are probably going to be an absolutely amazing time for human science. The number of “effective researchers” is no longer limited The most important input into science is scientists themselves. Until this year, that meant human brains — talented, highly trained human brains. The number of trained human brains could be increased by sending more people to grad school, but only to a point — the smartest people tend to be sent into the hardest research fields first, and if you 10x the number of researchers, the average quality will go down. This constraint was already starting to bite, as Ekerdt and Wu argued in 2024: We argue that a substantial portion of the downward historical trend in U.S. research productivity is a composition effect resulting from self-selection in researcher ability and the dramatic expansion of the research sector…[W]e find that this composition effect has lowered average researcher ability by between 37% and 53%[.] And of course, low fertility was making the problem worse, because fewer children meant fewer people who were able to become future scientists. AI just blows up this constraint completely. If an AI agent can function as an autonomous researcher, there’s no constraint on the number of researchers in the world — you can just build more of them, by building more computer chips and data centers that enable the running of more AI agents. Whether or not massive hordes of agents can collaborate to solve individual problems much faster, increasing the number of agents allows them to tackle many different research questions in parallel. What’s more, these AI “researchers” can be trained incredibly quickly compared to human beings. A human scientist takes decades to train, and more years to reach the frontier of their research field by diligent reading and conversation. This is the “burden of knowledge”, and it’s commonly believed to be one reason ideas have been getting harder to find — by the time human scientists can get up to speed, they’re often so old that their ideas are no longer novel. AI just annihilates this constraint — it can assimilate the entire literature and reach the frontier of research extremely quickly, and it can also tell human scientists where the frontier lies without forcing them to discover that for themselves. It can also pivot to new research directions more easily than humans can. And there’s also a self-reinforcing economic cycle here. Because scientific advances enable higher GDP, they enable more resources to be invested into the creation of yet more electronic “scientists”. Now that doesn’t mean the amount of scientific research goes to infinity — there may still be some essential bottleneck task that human researchers are still needed to perform. There are some indications that AI’s progress on open-ended conceptual research tasks is slower than in other dimensions, and it may be that humans are still needed to provide some of this long-term conceptual thinking. I’d bet against this being true for much longer, but it might be true for a long time. Even in this most “pessimistic” case, though, the amount of effective capital that scientists can use to leverage their work just went up by a lot. AI eliminates the theory bottleneck Science proceeds by an iterative process of theory and experiment. The theory part of that cycle is typically short, but non-trivial — it took a couple of years after the blackbody radiation spectrum was observed for Max Planck to come up with the quantum hypothesis to explain it, and decades longer before quantum theory as we now know it was created. It took about a year for Watson and Crick to figure out the shape of DNA after the requisite data was gathered. AI will basically eliminate the theory bottleneck entirely — or at least shorten the turnaround time between experiment and theory by an order of magnitude. AI is an absolute beast at math. Recently, an OpenAI model solved some of the most important open problems in computer science, math, and theoretical physics, and no one even batted an eyebrow because that sort of triumph is becoming increasingly commonplace. One of those solutions was a proof that the Standard Model of Particle Physics could be mathematically well-defined — a problem that human theorists had so far been unable to tackle. Not all research relies equally on theory, but understanding novel experimental results almost always involves some important component of either mathematical or spatial reasoning. That part of the task has now been made much, much faster. And on top of that, science is now open to a greater number of humans who aren’t naturally good at theorizing. AI can read the whole literature Much of the work of human scientists involves reading and understanding endless amounts of papers and going to endless conferences and seminars. The purpose of that work is to understand the existing scientific literature. Understanding the literature allows scientists to do the following things: Come up with new ideas for hypotheses to test Avoid doing research that someone else has already done Synthesize approaches from different areas or fields AI can reduce this effort to almost zero. Consider this excerpt from OpenAI’s blog post about their new research into neurodegenerative disease: The team divided the analysis among thousands of AI agents powered by GPT‑Rosalind, an approach we call distributed biological reasoning. The agents examined a whole-brain CRISPR atlas…a dataset showing how gene disruptions affect different types of cells across the mouse brain. Each agent investigated a gene disruption, analyzed cellular responses, searched scientific literature, and proposed explanations for the results. Their reports were combined into a reasoning atlas linking genes, cellular effects, relevant publications, and possible biological mechanisms. The research team estimated that comparable human analysis and literature review, conducted one perturbation at a time, would take months to a year. The agents reproduced findings from human analyses while uncovering additional leads. Their assessments of novelty and scientific value also aligned with judgments from blinded human experts. These findings help prioritize further experiments needed to test the proposed biological mechanisms. Some of these insights point to new leads for understanding brain disease. [emphasis mine] This is one case in which parallel work by a large number of agents really does make a huge difference: [ Haider.@haider1 this is actually insane openai deployed thousands of GPT-Rosalind agents to analyze millions of mouse brain cells in 32 minutes, work that could take human researchers months to a year also the agents uncovered new leads for brain diseases like Alzheimer's and Parkinson's OpenAI @OpenAI GPT-Rosalind surfaced a new lead for drug discovery: could restoring cholesterol transport protect neurons from degeneration? DCTN1 is a gene linked to Perry syndrome, a rare neurodegenerative disorder. In mouse-brain data, disrupting it increased expression of genes involved in 2:41 PM · Oct 21, 2026 · 51.5K Views 26 Replies · 95 Reposts · 1.26K Likes ](https://x.com/haider1/status/2112917197903540516) Not only does instantaneous literature review vastly speed up the research process, but it allows AI to make novel conceptual leaps from combining approaches from wildly different fields of research. AI regularly does this in math research, combining results, approaches, and theorems from far-flung places that no human mathematician would have thought to combine. It can do it in science, too. Much of scientific innovation, historically, has been “recombinant” innovation — scientists applying insights from one field to another. With AI, essentially all research is now cross-disciplinary — AI can read not just the literature on a particular subject, but the literature on a million other subjects that might have produced useful techniques or insights for the problem at hand. Superscience So far, all of the factors I’ve listed are ways that AI can speed up science as it’s already done. I’ve basically described how AI can send gigantic hordes of superhumanly fast, superhumanly well-read, superhumanly mathematically gifted researchers to re-accelerate scientific discovery. But it’s also possible that AI will enable types of discovery — and ways of understanding the natural world — that no network of humans, no matter how large or how brilliant it was or how long it had to work, would ever be able to discover. In a post back in July, I wrote about how that might work: What if there are lots of…aspects of the Universe that [are] too complex to understand in terms of simple laws, but not so complex that they just dissolve into unknowable chaos? It’s possible that we can reliably control these complex phenomena with AI, even if we never reduce them to the kind of principles that we can teach a grad student in a textbook… Another way of saying this is that there may be laws of the universe that humans can’t understand but AI can. I call these “cloud laws” — causal regularities that can be exploited by technology, but which are too diffuse and complex for an individual human being to either intuit or communicate… Perhaps social sciences like economics, sociology, and political science obey similarly complex regularities, and AI can help us find them. Perhaps there are physical processes — plasma, or topological materials, or aerial turbulence, etc. — that obey cloud laws instead of chaos?… [H]uman collaboration is bottlenecked — it’s limited by our ability to intuit patterns at an individual level, and to communicate these patterns from one individual to another. AI, being a computer, just doesn’t have this sort of limitation; it can work with vast, diffuse patterns without having to break them into pieces or simplify them in order to compress them into the tiny pipelines of person-to-person explanation… If much of the Universe turns out to be ruled by cloud laws, we could be on the precipice of a scientific renaissance. In order to discover cloud laws, AI will need huge amounts of data about the natural world. AI figured out human speech by assimilating essentially all of the text humans have ever written. If it’s going to figure out hidden patterns in biology, neuroscience, materials science, etc., it’s going to need similarly massive amounts of data from physical instruments. That data will be far, far more than humans — individually or collectively — would be able to parse, label, characterize, or model. But AI was built to do this. If scientists simply build huge arrays of sensors to measure complex phenomena like the brain, the cell, the economy, etc., they may be able to feed this flood of data to AI directly and bypass human cognitive intermediation entirely. AI would then learn about the world directly, and it might learn things that humans never could. This “superscience”, if you want to call it that, would actually go beyond the kind of science humans have done heretofore. Many of the technologies it yielded would be beyond human comprehension — indistinguishable from magic. But to the AIs, they would make perfect scientific sense. In any case, it seems clear that the scientific slowdown that threatened human progress will now be delayed — at least for a while. Unless humans have somehow found most of the actual exploitable regularities in our Universe, we’re about to enter a new golden age of science. Subscribe Share
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