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Spotify's is giving you the keys to its recommendation algorithm with US launch of 'Taste Profile'

Spotify is rolling out Taste Profile to Premium users in the U.S., letting listeners see how the streamer understands their tastes and use natural language to reshape their recommendations

Sep 23 • 9:00 AM EDT • Technology • techcrunch.com

As 100 ISRO Scientists Quit, Retire Early, Centre Tightens Exit Rule

An official directive acknowledged that there has been a "spate of requests" for voluntary retirement and resignation from Group 'A' Scientific/Technical personnel of ISRO.

Jul 16 • 3:52 AM EDT • Science • ndtv.com

Carisa Hendrix Tour: How Ticketing Algorithm Fueled Lucy Darling's Rise

Carisa Hendrix talks creating her own ticketing algorithm to fuel the rise of her Lucy Darling touring act, today on 'Comedy Means Business.'

Sep 7 • 4:30 PM EDT • Business • deadline.com

Viewpoint: Unlocking Europe’s wildfire data

Europe is already investing in the science, technology and infrastructure needed to understand, prevent and fight wildfires. Copernicus provides satellite-based Earth observation and environmental information, while the European Forest Fire Information System provides information on fire danger and forest fires across Europe. Weather and climate systems add forecasts of heat, drought, wind and humidity; historical fire databases show where fires have occurred; and researchers generate increasingly detailed knowledge about fuels, fire behaviour, forecasting, modelling and risk management. Meanwhile, new European research projects are adding digital twins and other tools to this growing ecosystem.However, these data do not necessarily sit together, nor are they necessarily described, structured or governed in ways that allow them to be used together. Much of the information is public, but some is commercially held. Some is sensitive. Some is subject to legal, security or operational restrictions. Even when data can be legally accessed, different systems may use different formats, terminology and rules.Europe therefore faces an unusual paradox: we have vast amounts of relevant information to respond to the wildfire challenge, but not the means to bring it together. For example, a wildfire researcher or emergency service might need to ask whether a particular combination of vegetation, drought, wind and topography has historically been associated with rapid fire spread. The relevant information may exist in several institutions, using different systems and subject to different conditions of access.The challenge is to conduct an authorised analysis that interacts with these sources, combines the results and respects the rules imposed by each data custodian.An important part of the answer can be found in the Fair principles, the idea that data should be findable, accessible, interoperable and reusable. Developed more than a decade ago, these principles have become a central reference point for modern data stewardship, making it easier for people and machines to find data, understand what it means and determine whether it can be reused.Readability by machines is essential, since AI systems will be key to meeting the data challenge. Bring analysis to the dataThe proposition is not to give an AI system unrestricted access to sensitive databases. Quite the opposite. The goal is to make it possible for approved algorithms to go to the data, rather than requiring sensitive data or large datasets to be copied to wherever the analysis happens.Imagine an analysis asking a series of questions of different data stations. At one station it might examine weather and climate data; at another, historical fire records; at another, experimental measurements of fire behaviour. The algorithm does not need to download everything. It can operate within the environment permitted by each data custodian and return only the results that the rules allow to leave.This is sometimes described as data visiting, and related approaches already exist in areas such as federated analysis and federated learning.Sensitive data should therefore not be treated as a free-for-all for machines. This requires appropriate governance at multiple levels. Access needs to be authenticated and authorised. The purpose of an analysis should be defined. The code or model being run should be controlled. Data custodians should be able to specify what can be queried and what results can leave their environment. Activities should be auditable and human oversight should remain where the consequences require it.In other words, the objective is not to trust AI with the data. It is to design systems in which AI never has more access than it needs.As open as possible, as closed as necessaryFair data should also not be confused with open data. Some wildfire data should be openly available. Other information may legitimately need to remain restricted because it concerns critical infrastructure, security, commercial interests, privacy or operational capabilities. The principle should be: as open as possible, as closed as necessary.The important point is that restricted data should not automatically become unusable data. If machines can discover what information exists, understand its meaning, establish where it came from and determine the conditions under which it can be used, authorised analyses can potentially be carried out without the underlying data ever leaving the environment in which it is protected.Restricted data can still be Fair and machine-actionable.This is also an important question of data sovereignty. European institutions should not have to surrender control of their data to a handful of technology platforms in order to benefit from AI. Nor does Europe need to start again by building one giant repository for every piece of wildfire information. The better approach is to connect what already exists.This is the principle behind emerging approaches such as Tryangle, developed through the Leiden Initiative for Fair and Equitable Science. Rather than creating another centralised data warehouse, the approach seeks to provide an interoperable layer through which approved algorithms can visit distributed data sources while respecting the rules of their custodians.In this way, a data station can remain where it is. Its custodians can retain control. Existing databases and applications do not have to be abandoned. What changes is their ability to communicate with other systems through common, machine-readable descriptions, access rules and interoperability mechanisms.But approaches like this need data that meets the Fair principles. Only then can an interoperable ecosystem safely begin to turn fragmented information into collective intelligence.Data infrastructure is wildfire infrastructureThe implications of this approach are entirely practical. Firefighters could gain better intelligence about local and evolving risks. Land managers could test prevention strategies using more comprehensive information about fuels, weather and past fires. Researchers could compare evidence across ecosystems without repeatedly moving huge datasets between institutions. Smaller regions and resource-constrained teams could access analytical capabilities without having to build large data infrastructures themselves.Europe is already investing heavily in satellites, databases, research programmes, forecasting systems and scientific expertise. The question is whether we will connect those investments well enough to turn them into actionable intelligence.That requires policymakers and funders to treat data infrastructure as seriously as physical infrastructure. Data generated or supported through European public funding should be Fair, verifiable and machine actionable by design, wherever legally and ethically possible.Related articlesData Corner: Europe’s burning need for wildfire mitigation researchClimate monitoring must remain a global enterprise, experts sayIn practice, that means funding not only the production of data, but also the infrastructure that makes data usable across systems: metadata, provenance, interoperability, access mechanisms and governance. Where data must remain restricted, we should ensure that it can still be securely discovered, verified and used for authorised analysis.Initiatives such as the European Open Science Cloud offer an existing framework through which these principles can be put into practice. What is needed now is to accelerate implementation and make them a standard part of how Europe funds and builds research and innovation systems.We do not need another generation of disconnected databases. The data is already there. The opportunity is to make it work together, securely, responsibly and quickly enough to help prevent the next fire, rather than simply document the last one.Jean-Claude Burgelman is professor emeritus at the Free University of Brussels (VUB) and editor-in-chief at Frontiers Policy Lab. For more than 20 years he held leading roles in research and innovation policy at the European Commission.

Sep 9 • 8:00 PM EDT • Science • sciencebusiness.net

MySpace Is Coming Back. I Owe It My First Business

The old network is growing again as people flee the algorithm. It's a reminder of something every business gave up.

Aug 4 • 11:34 AM EDT • Business • inc.com

Scientists Find Evidence That Earth's Core Is Still Leaking Hydrogen, Helium and Tungsten

When Earth first formed, around 4.5 billion years ago, it was an extremely hot, largely molten planet. As it cooled, heavier materials sank to form the core, while lighter materials formed the mantle and crust.

Oct 18 • 3:34 AM EDT • Science • ndtv.com

How to decipher smells like a fruit fly

In the animal kingdom, energy conservation is essential for survival. That’s why our brains have evolved to be extremely energy and resource efficient at storing and processing information. Since the 1980s, computational scientists have tried to mimic brain structure and function, but have not yet succeeded in creating functional, low-memory solutions that work in real-world scenarios. Published in Neuromorphic Computing & Engineering, a new fly brain-inspired algorithm developed by researchers at the Okinawa Institute of Science and Technology (OIST) demonstrates promise for achieving practical applications under realistic constraints.

Aug 23 • 8:00 PM EDT • Science • eurekalert.org

Apple iPhone 18 Pro Launch Timeline: New Date Reportedly Set For Pre-orders

Apple is expected to unveil the iPhone 18 Pro series on September 9, with pre-orders reportedly opening later than usual on September 12 and sales beginning on September 18.

Sep 1 • 5:42 AM EDT • Technology • ndtv.com

Trillions Of Particles Pass Through You Every Second. What They Are

Neutrinos have always fascinated scientists as they pass through everything that's there on the plant, like Marvel's fictional character Juggernaut (though not like a wrecking ball).

Oct 13 • 5:11 AM EDT • Science • ndtv.com

Judge holds Justice family business reps personally responsible in multimillion-dollar dispute

That conclusion, to 'pierce the corporate veil,' would allow the court to hold the owners personally responsible for the companies' liabilities.

Jul 11 • 12:02 PM EDT • Business • wvmetronews.com

Can AI Become Conscious? The Science Behind A Question At NDTV World Summit

As AI consciousness becomes a subject of scientific and philosophical debate, Swami Sarvapriyananda will explore whether machines can possess a mind of their own at the NDTV World Summit 2026 on October 22.

Oct 12 • 7:52 AM EDT • Science • ndtv.com

The science behind Cavan Sullivan’s custom Adidas cleats: Foot scans, algorithms and 3D printing

The Athletic visits Adidas' German HQ to find out more about how it designs and manufactures bespoke footwear for U.S. soccer's new star

Oct 3 • 12:05 AM EDT • Science • nytimes.com

Dolby Vision 2 Is Officially Available On Select Hisense TVs

Dolby Vision 2 includes a new algorithm that optimizes content on a scene-by-scene basis.

Aug 6 • 9:00 AM EDT • Technology • engadget.com

September Monthly Review: Health MCP Guy?

For all the time I spend talking about APIs and data exchange, this newsletter has had embarrassingly little interoperability of its own. It’s old-school SaaS, baby - UI or bust. Not all of that is my fault! Substack, for all its popularity, is (as mentioned before) really frustrating as my system of record. The primary user it optimizes towards is the reader, whose attention feeds the app, the Notes feed, and the recommendation network that Substack is actually selling. Health API Guy is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. Subscribe They’re falling into the same trap that Medium once sprinted into, albeit at a much more glacial speed. Younger generations may not remember (or never knew) it, but Medium once lured publications like The Ringer and Signal v. Noise onto its platform, only to lose them as it rebuilt itself around a paywall and an algorithmic feed. Substack’s original pitch circa 2020 was the antidote (you own your list), but every new feed feature chips away at it. I get it: writers are generic supply in the eyes of the Substack product team. Generic supply follows demand, so the consistent pressure on the PMs is to generate demand, which leads to every product cycle going toward discovery (Notes, recommendations, the app, live video). The end result lackluster author tools (laggy and broken analytics are truly crippling) all despite the premium price point. The archive is where it crosses from authorial gripes to actual reader impact, though. Across Substack and LinkedIn, I’ve published 1,218 posts and nearly 900,000 words since 2018. Substack’s native search can see only the 464 posts that live on Substack, and even there it’s woefully inadequate at surfacing relevant content (even for the guy who wrote it)! Readers have the same problem, writ large. Ask the Archive Fixing search (for both my readers and my own use) turned out to be extremely non-trivial, unfortunately: There’s no public API for readers to access content RSS exists for new content notification, but it only carries the 20 most recent posts and cuts paid ones off at the preview The only Substack MCP is an admin tool for analytics The export exists for authors, but it’s painfully awkward and slow. So I built something to work around that: the Health API Guy MCP. It’s a private MCP server holding the full text of everything I’ve written across Substack and LinkedIn. Add it to Claude or ChatGPT as a connector and you can ask things like: It pairs keyword search (for acronyms and case names, of which we have many) with semantic search. Every post is also labeled by topic, form, and court case, so you can pull a lawsuit’s (or multiple cases’) entire docket across Substack and LinkedIn in one query: Hail, Healthapiguyhydra This is also a bit of me eating my own cooking. I’ve spent the year arguing that headless healthcare is arriving and that developers want primitives over abstractions. Last week in Hail, Benihydra, I watched Salesforce plug its platform into Claude and bet that the records underneath outlive whatever interface sits on top: Salesforce must believe that the Hydra’s immortal head is the records, permissions, and business logic (in that they will survive whatever happens to the interface). It felt a bit hypocritical to keep my own archive behind a Substack search bar. The archive is the body. Substack is one head, and now whatever tool you might use is another. Will the EHRs follow my lead? Probably not imminently, but one can hope! Why Founding Members Only The MCP is available to Founding Member subscribers. A few reasons: It costs money and time to run. Hosting, the database, Clerk usage and AI compute for search and labeling are significant operational costs (that increase with usage). Substack gives me no way to automate access. There’s no API to check whether someone is a paid subscriber, so every user is manual operations for now. It’s a beta. We’ll need to improve things and parts will break. I’d rather they break for a small group of dedicated readers who will tell me about it. The Founding tier deserved a real benefit. Until now it was mostly a generous tip. Now it comes with something. If you’re already a Founding Member, reply to this email with the address you’d like provisioned. If you’re not and want in, upgrade here. Larger teams (such as companies) can get access too: group subscriptions at the Founding tier come with the group discount. The admin for the subscription can reply with the list of addresses to provision. Get 20% off a group subscription Pricing Founding membership is now a flat $200 a year, replacing the old suggested $150 that readers could adjust. Existing Founding Members will have access grandfathered in at their previous rate. Month in Review Articles Published Make Networks Dumb Again (Sep 11): AI is making old analog networks cheaper to use and flooding them with machine traffic in the process. A plea for agent-native rails with the intelligence at the endpoints, and for Washington to drag them to ubiquity. Interoperability, Unbuttoned (Sep 29): Interoperability is a series of workflows in a trenchcoat, and this post finally takes the trenchcoat off. A color-coded map shows which provider workflows went digital, which are fragmented, and which still run on fax. Video Content Antitrust’s System of Record Problem (Sep 3): The FTC’s investigation into Epic prompted a ninety-minute webinar, now posted with chapters and a smoothed transcript. We get into why market definition, missing B2B share data, and the SSNIP test leave antitrust poorly suited to systems of record, plus why information blocking may be doing more than any pending lawsuit. The Information Exchange: Cerner Milking Edition (Sep 18): This was so fucking fun. Ryan Tucker, Ryan Brickner, Brad Thorson, and I try an Around the Horn format, complete with points, penalties, and a mute button. We debate whether Oracle Health is dead, who would actually buy Cerner (IBM enters the chat), p(Doom), and why the front desk still hands you a clipboard. Regulatory Ask Not What Your Network Can Do (Sep 23): CDC’s RFI on public health data intermediaries lists use cases already served, unevenly, by eCR, lab reporting, IZ Gateway, and syndromic surveillance. The real opportunity sits in one question about AI: a network that can handle the next emergency without another interface project. Court cases Particle v. Epic: The Naughty Schoolteacher (Sep 4): After a summer-long vigil, the answer on Epic’s early summary judgment bid is two paragraphs and a remedial assignment. Particle advances to the next grade, and market definition gets another year of discovery. The Epic Discovery Files (Sep 8): Texas’s motions to compel arrive with a 251-page appendix of requests, objections, and meet-and-confer letters with Cravath. Inside: Epic using generative AI for document review, denied interoperability requests in scope, and Noerr-Pennington raised against lobbying discovery. Epic v. Health Gorilla: “Working as Designed” (Sep 9): Epic wants nothing to do with the data breach MDL, and explaining why puts fresh discovery on the public docket. The spiciest allegation: Health Gorilla told GuardDog to scrub its law firm business from its website instead of cutting off access. Veeva v. Epic: Legal Bypass Surgery (Sep 15): Veeva asks the Wisconsin Supreme Court to skip the Court of Appeals, while law professors and three policy groups line up as amici. Meanwhile, Epic’s lawyers apologize for another round of citation errors, including a quote that never existed. Texas v. Epic: Behind the Black Bars (Sep 17): The court portal served up Epic’s unredacted filings, revealing a $5 million document review budget and the AI workflow behind it. Also inside: the 200-plus company names Texas wants searched, and one very expensive Zoom invitation. OpenEvidence v. Doximity: Consequences Will Follow (Sep 25): OpenEvidence tells the court it will not comply with a discovery order, and its own lawyers at Quinn Emanuel say they won’t defend the choice. Add years of Slack reported unrecoverable, and Rule 37 sanctions come into view. Health Tech Keeps Choosing Violence (Sep 28): Doximity moves to end OpenEvidence’s case, Epic asks Texas to show its work, and a sleepy Audacious Inquiry patent fight sprouts antitrust and information blocking counterclaims. The main event is a long-sealed Cognizant v. Infosys ruling that hints where system of record antitrust claims can survive. EHRs One Verb for Oracle Health (Sep 14): Oracle’s earnings call quantifies every data center to the decimal point, while Oracle Health gets a single verb: “accelerate.” Panning the few healthcare mentions turns up an agentic care management system, a research-to-care ledger play, and the question of whether healthcare gets Larry’s patience. Epic’s Forretress Under Siege (Sep 30): Judy Faulkner tells a panel Epic paused hundreds of projects to harden its software and calls it a “shame,” while a spokesperson insists the roadmap hasn’t changed. Project Glasswing, a Pokémon-branded QAN sprint, and a trove of Jodel grievances reconcile the two Epics and preview the Jevons paradox coming for all software. Industry Analysis Scanning for a Network (Sep 1): Scan.com raises $220M to build the imaging network labs never got, speedrunning Zocdoc’s consumer-to-B2B pivot. Its wedge to network density may be the most American one available: personal injury litigation. The Front Desk Wants the Damn Whole Building (Sep 16): Hello Patient buys Converse Health, pushing its AI front desk into referrals, chart work, and prior auth. The copilot categories keep blurring, and your distribution partner is still your final boss. Cross-industry Comparisons A Different Patchwork Quilt (Sep 10): Healthcare loves to envy open banking, but the US version has no mandate, a toll booth at Chase, and data quality problems of its own. The grass isn’t greener; it’s another American patchwork quilt, and perhaps a worse one. Hail, Benihydra (Sep 21): Salesforce brings its platform into Claude, Slack, and its own agent, inviting the interfaces that might replace it. Cut off one head and more grow back, so long as the records underneath stay put. Living on A Trade Secret Prayer (Sep 22): Six credit unions are suing Fiserv, the Epic of core banking, by claiming their own member records as trade secrets. Banking has no Cures Act, so its lawyers reach for legal alchemy that healthcare has already tried. Other News None this month External Media Nabla Accelerate NYC: I kicked off fall conference season at Nabla’s Accelerate event in New York, where Delphine Groll opened by asking the room to picture the company it wants to become and the one it doesn’t. I liked her framework of bureaucracy, fear, and ego as the blockers. I also love vendor conferences/events in general - different vibe than industry biggies. Healthcare 101 with Nikhil Krishnan: I briefly guest lectured Out-Of-Pocket’s Healthcare 101 class. Appreciated Nikhil’s takeaways cover whether AI doctors can query HIEs, why healthcare’s data-liberation rules beat most industries’, and the hi-res graphic is in the comments. HCN: Epic Probed by the FTC: I joined the HCN guys to talk through the FTC’s investigation into Epic. Cedric’s new hairdo was an unexpected plot twist. The episode also covers MFN drug pricing, healthcare M&A and Eli Lilly’s acquisition streak. Fall Conferences: At eHealth Exchange’s Annual Meeting in Austin on Oct 27, I’ll moderate a panel on the tensions between AI, HIEs, and timely patient access with Jean Ross (Primary Record), Therasa Bell (Kno2), Byron Crowe (Doctronic), and Michael Marchant (Freenome). Here’s the rest of my schedule if you want to meet up: CommonWell (Redwood Shores, CA): Oct 13-14 Will be participating in a fun Jeopardy event Open@Epic (Madison, WI): Oct 20-23 Will be enjoying Verona, Willy Street, and other local adventures eHealth Exchange and Sequoia Project (Austin, TX): Oct 27-29 Yeehaw HLTH (Vegas): Nov 15-18 Groundhog Day, Venetian Edition RSNA (Chicago): Nov 30-Dec 2 See me at “Governing AI in Breast Imaging: What Health Systems Are Actually Doing” Digital Health Counsel 2026 AI Summit (Seattle): December 2-3 Will likely be speaking to a bunch of lawyers about information blocking Posts I Liked ChatGPT’s Epic integration: game changer or nothing burger?: Joshua Liu, MD offers five thoughts on OpenAI’s Epic launch, starting from the point that it rides the same open APIs available to anyone. He argues read-only access leaves ordering to Art and the ambient startups, and asks why HCA is piloting it alongside Commure. He’s sorta a must-follow at this point - consistently excellent, thought-provoking takes. Epic, Lilly, and the Discovery pass: Seth Chaney reads Epic’s first major Discovery deal (with Eli Lilly), as well as its revised developer terms on commercial displays, as the pipe finally opening between EHRs and pharma. Life sciences and health continue to collide. Scott Rossignol on EHR payer platforms and ePA fees: Scott argues the top ambulatory EHR vendors are turning the prior auth mandate into a per-member-per-year toll on payers, for an API whose operating cost has nothing to do with covered lives. His fix is two lines of regulatory text: put the CRD/DTR/PAS certification criteria in the Base EHR definition, and bring payer-facing ePA connectivity under § 170.404’s cost-based fee rules. It’s a good solution. Health API Guy is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. Subscribe

Oct 1 • 4:36 AM EDT • Health • healthapiguy.substack.com

"Playhouse For Predator": Epstein Used 'Fake' Science Office As Prison Escape

The Florida Science Foundation was presented as a legitimate organisation focused on supporting research. But a review by The Telegraph of more than 1,000 documents connected to the company painted a different picture.

Jun 27 • 9:16 AM EDT • Science • ndtv.com

How lip-syncing to AI let Forced Entertainment ‘play the game of theatre’

Today we have a fascinating piece by long-time Forced Entertainment fan Ben Kulvichit, speaking to Tim Etchells about their latest show Cold Sweat, how they are integrating lip-sync and AI voices into their work, the question of ‘authenticity’ on stage, and how lip-sync has even opened the door for them to make “something a little bit like drama”. This piece is a paid partnership with the Southbank Centre, where you can see Cold Sweat next month – we’re very grateful to the Southbank Centre for their ongoing support for Exeunt. Holly Williams ‘It was immediately interesting and uncanny’ **By Ben Kulvichit** “Is the microphone on?” So goes one of the opening lines of Signal to Noise, delivered by a human performer lip-syncing to an AI-generated voice. The voice is American, cheery but sluggish, slowed down into a gelatinous drawl. It repeats ad infinitum in a farce of stuckness; an answer never materialises. I find myself asking the same question as I hop on a Google Meet call with Tim Etchells, the artistic director of Forced Entertainment. We spend five or ten minutes frowning at each other, muting and unmuting, me gesticulating in semaphore into my webcam, checking if I am coming through now, restarting the laptop. To Etchells, I appear exaggeratedly mouthing words, my voice missing. In this gloopy purgatory of broken communication, life imitates art imitates life imitates art. We try a Zoom instead. Forced Entertainment, the pioneering British theatre company who have been at the avant-garde of performance since the 1980s, turned an interesting artistic corner with Signal to Noise, created on the occasion of their 40th anniversary. The whole performance employed this lip-sync technique to AI voices; on the face of it a left-field move from a company whose identity has long been grounded in the earthy, immediate presences of its performers and its unassuming, low-tech approach to theatricality. The technique, however, turned out to provide the company with a “rich new seam” of artistic enquiry, and has now spawned a trilogy of works. The second instalment, Cold Sweat, plays at Sheffield Theatres, Southbank Centre, and Lancaster Arts in October, while the third part, Everything Must Go, has been touring in Europe and is slated to come to the UK in 2027. As is often the case with Forced Entertainment shows, the main conceit for this work didn’t arrive until quite late. They were “four or five weeks into rehearsals for Signal to Noise,” Etchells recalls, and getting nowhere. Away from rehearsals, he had been playing around with feeding “weird texts” to AI voices to see how they would cope with them – “basically for my own amusement” – and was compelled by the uniformly “serviceable, benign, good-natured” tone they would read in, no matter how poetic, fractured or experimental the writing. On a hunch, he brought some examples into rehearsals and the performers gave lip-syncing a go. “It was immediately interesting and uncanny,” he recalls, with the performers having to deal in the moment with the problem of mapping themselves onto another artificial ‘performer,’ which itself was dealing with the problem of a very human text. “Almost all the writing for these projects would be useless to us unless it was done in this way,” says Etchells. “We wouldn’t know what to do with it. It’s too poetic. It’s too fictional. It’s too tangled.” But exploring lip-syncing along to the text has opened “an enormous reservoir of ways of thinking about writing and performing.” Subscribe to Exeunt, and get two in-depth pieces on theatre a week, direct to your inbox Subscribe Forced Entertainment have long been sifting through the detritus of popular culture, from daytime TV to end-of-the-pier vaudeville, magic acts and clown shows, to find frameworks for their own unusual entertainments. Lip-syncing, then, makes sense for them to experiment with. As a mode of performance, it’s wired into popular consciousness through pop stars trying to convince stadium audiences of their liveness, or those same voices as channeled through the made-up lips of contestants on RuPaul’s Drag Race, or movie dialogue spreading virally through the host bodies of TikTok users. For Etchells, lip-syncing is often about “approaching real voices and trying to take on their aura,” subsuming that original human presence into yours. “There’s a very basic, elemental sort of theatre magic to it, produced in this very easy way.” This broad approach resonates with instances of lip-syncing within contemporary performance, too. In works like Re-Member Me and Showmanism, performance artist Dickie Beau acts as a kind of spirit medium for archival recordings (this process of re-membering – as in, giving corporeal form to voices – relies on his masterfully precise actor’s technique), while Frankie Thompson is similarly possessed by a host of characters ranging from a nine-year-old Jacob Rees-Mogg to Cliff Richards, all of whom she finds compellingly nauseating, in the exorcism ritual that is her latest show, Horrible Things. What’s different about Forced Entertainment’s use of lip-syncing is that here, there is no original presence to be channeled. The performers are instead taking on what Etchells describes as “the voices of nobody,” or rather the “phantasmic, aggregated echoes” of the many many voices on whose data the AI has been trained, and which are now conjuring into existence words they never actually said. There is a sort of mournful, empty quality to these voices, having “washed up” far away from the people they originally belonged to, to find themselves in stranger’s mouths listing objects, wondering if anyone can hear them and glitching in interminable loops. There is a cold, sad anonymity to it all. Etchells remarks that occasionally the software company he used has notified him that particular people are withdrawing their voices from the platform, instructing him to cease use of those voices. Using these technologies in performance certainly brings up ethical questions around digital rights, consent, identity and ownership, although in the grand scheme of things, Etchells is fairly blasé about it: of all the purposes someone’s voice might be put to without their knowledge, “an avant-garde performance is probably the least of their worries.” [ ](https://exeuntmagazine.substack.com/p/frankie-thompson-horrible-things-essay-mental-health-compulsive-behaviours-pleasance-courtyard-soho-theatre) [ Frankie Thompson: How I manage my own ‘horrible things’ at the Edinburgh Fringe ](https://exeuntmagazine.substack.com/p/frankie-thompson-horrible-things-essay-mental-health-compulsive-behaviours-pleasance-courtyard-soho-theatre) Exeunt · Aug 25 [ Read full story ](https://exeuntmagazine.substack.com/p/frankie-thompson-horrible-things-essay-mental-health-compulsive-behaviours-pleasance-courtyard-soho-theatre) For the performers, though, lip-syncing to these strange, artificial voices is an odd task, requiring them to bend their live, human presence into the shapes produced by machines. Politically, it mirrors processes that are already at play in the continued computational automation of human life. Etchells brings up James Bridle’s writings on new technologies as a reference point: one example Bridle gives is of “chaotic storage”, a logistics technique employed by Amazon fulfilment centres whereby items are stored not according to traditional methods of categorisation or alphabetisation, but in a way that an algorithm determines to produce the most efficient routes for ‘picking’ based on customer purchase patterns. This appears completely random and incomprehensible to humans, with paddling pools sitting next to USB sticks sitting next to blowtorches, and so for the warehouse workers, the process of picking items becomes completely reliant on technology which tells them where to go. Our world is increasingly being designed not with humans in mind, but rather machines. As Etchells puts it, “technology malforms the space, and then you end up having to live in it.” Initially, Forced Entertainment tried their best to live in the world of machines: “I was in the it’s-got-to-be-perfect school of lip-sync,” says Etchells. But with their aesthetic being so tied to the failing act, the slipping mask, the shoddy costume, “it’s no surprise really that that didn’t last.” Instead, the performers got to enjoy being late for a line, or playing games with each other, “stealing” each other’s voices. Etchells compares it to the principles of ventriloquism, in which a free-floating voice unattached to a body is fundamentally discomforting and “sort of traumatic”. That discomfort is equally as alluring to Forced Entertainment as the satisfaction of precision, the pretence of authenticity only interesting if shown to be a pretence. Lip-sync has also opened up new avenues for the company. Having been allergic all their lives to Plays-with-a-capital-P, with Cold Sweat Forced Entertainment find themselves approaching something a little bit like drama. Rather than the presentational arena of Signal to Noise’s open white stage, Cold Sweat places us more decisively in a “fictional locale”: what appears to be “the aftermath of some disastrous party or backyard drinking binge” where three figures – family members, or neighbours, perhaps – are the last ones standing. “It’s all quite acrimonious and accusatory, everyone’s talking over each other.” For Etchells, this more fictional mode is “a door that we’ve never been able to open. We’ve just never really gone there.” Perhaps it is the technique of lip-syncing, an extra buffer of mediation, that allows them to venture away from the here-and-now without feeling that they have to commit to the “psychological claptrap” that comes along with acting; to be able to “play the game of theatre, without getting one’s hands too dirty.” As I wonder how else the impulses behind Forced Entertainment’s work have changed over time, it occurs to me that over the last ten years of new creations, with the exception of If All Else Fails, the company’s performers haven’t ever spoken on stage: there were two shows without any language at all, Out of Order and Under Bright Light, and then this trilogy of lip-sync works. Etchells agrees it’s a shift away from earlier productions, that were so often about “the actual voices of the performers and their actual presences. We had a lot of money, in some ways, on an authentic approach to what it might mean to speak on the stage.” But this sense of authentic presence itself had a fakeness to it; these days, Etchells can’t stand that mode of being onstage where performers “are here to be cheeky and witty versions of themselves. I don’t want to be charmed.” Pulling back into a more anonymous space has resulted in works that feel slightly sharper-edged, more brutal in a way. “All of these shows are post-Brexit, post-Trump, post-truth, post-everything,” Etchells notes. Speech, charisma, authenticity – these things feel politically loaded, and perhaps warmth and openness just don’t feel like the right tools to reflect the world we’re living in. “It feels like the space is closing down, and we’re trying to deal with the fact that the system, the structure that you’re in, is more oppressive and more powerful. It probably always was, but I think we’re just more tuned to it now.” Yet even without their voices, the performers’ distinctive presences – ones that many audiences have followed around for years, if not decades – still, inevitably, shine through as they physically navigate whatever unending task the system of the piece has set for them. They’re still trying to keep up with the onslaught of text, still running about, changing costumes, moving stuff around. The human is always stuck in the teeth of the machine, jamming the smooth, frictionless cheeriness of the not-real, gesticulating silently, trying desperately to communicate. Cold Sweat is at Sheffield Theatres, 2 to 3 October; Southbank Centre, London, 9 to 10 October; Lancaster Arts, 14 October [ ](https://exeuntmagazine.substack.com/p/exeunt-recommends-the-best-autumn-theatre-2026-lives-of-others-standard-of-living-our-friends-in-the-north-lanny-) [ Exeunt recommends... the best autumn theatre ](https://exeuntmagazine.substack.com/p/exeunt-recommends-the-best-autumn-theatre-2026-lives-of-others-standard-of-living-our-friends-in-the-north-lanny-) Exeunt · Sep 16 [ Read full story ](https://exeuntmagazine.substack.com/p/exeunt-recommends-the-best-autumn-theatre-2026-lives-of-others-standard-of-living-our-friends-in-the-north-lanny-) This piece was paid for by the Southbank Centre. Such support helps Exeunt to keep publishing the most interesting writing, about the most exciting theatre. Interested in partnering with Exeunt? Visit our Advertise with Exeunt page, where you can get more information, download an advertising pack, and find our contact details.

Sep 22 • 5:02 AM EDT • Entertainment • exeuntmagazine.substack.com

Best practices for launching albums and series in a crowded entertainment marketplace

In this era of personalized algorithms and nonstop content, getting an audience to notice a particular album or debuting TV show, let alone keep coming back, is harder than ever. And the line between a release being a drop in the ocean and a cannonball splash is razor thin — yet not impossible, This new special […]

Aug 27 • 11:00 AM EDT • Entertainment • luminatedata.com

Zapata Quantum Adds Peter Shor and Three Scientists to Advisory Board

Zapata Quantum has appointed Peter Shor and three researchers to its advisory board, adding expertise in quantum algorithms, error correction

Oct 20 • 8:31 AM EDT • Science • thequantuminsider.com

Space tech: an orbiting market opportunity | Investors Lab

While AI models are excellent at parsing massive amounts of data, they struggle to pivot when faced with unprecedented events — the kind that frequently occur in the vacuum of outer space. Space operations are a zero-tolerance environment where the cost of a software miscalculation can be a total loss of assets or human lives. The solution isn't to abandon the technology, but to pivot toward human-assisted systems where an AI algorithm handles the heavy computational lifting while human experts serve as guides on the ground and remain the ultimate decision makers in aerospace technology. SN's Aaron Tremper takes us there.

Aug 12 • 8:00 AM EDT • Science • sciencenews.org

AI cuts hospital system's MRI wait times by more than 50%

The health network, which operates 37 hospitals in California, drastically reduced scheduling delays after integrating an FDA-cleared algorithm designed to speed up imaging acquisition times.

Jun 30 • 1:55 AM EDT • Business • radiologybusiness.com

Amazon Researcher Claims Quantum Algorithm Could Challenge PQC Foundations

The Quantum Insider (TQI) is the leading online resource dedicated exclusively to Quantum Computing.

Aug 6 • 1:42 PM EDT • Technology • thequantuminsider.com

We’re still writing p3ri0d

What algorithmic suppression of women’s health content is actually costing us.

Jun 29 • 9:28 AM EDT • Health • fastcompany.com

How to Navigate Social Media’s Data Evolution as a Small Business Owner

As the digital media landscape evolves rapidly, small business owners need to stay informed and learn how to leverage emerging platforms to reach their audience. Max McCoy, a paid Media Specialist for Circulus Digital Media, says Meta’s recent algorithm adjustments have changed how it gathers user data. He says Meta increasingly relies on first-party data, […]

Sep 20 • 2:03 PM EDT • Business • wishtv.com

What Could Save Civilisation From Collapse? Scientists Simulate 1,000 Years

A new 1,000-year computer simulation explores how technological civilisations could survive disasters and recover from collapse.

Sep 19 • 5:58 AM EDT • Science • ndtv.com

Apple Top Executive Doesn't Want You To Use Screen Protector On iPhone: 'It Makes My Skin Crawl'

Apple's new Vice President of Hardware Engineering, Tom Marieb, dislikes iPhone screen protectors, saying it makes his 'skin crawl'.

Sep 19 • 8:43 AM EDT • Technology • ndtv.com

FIFA Let Politics Win Over Football, Iran Paid The Price For Political Hypocrisy

Football's governing body has long sold itself as apolitical - like the IOC - an organisation above borders, above diplomacy, above conflict. But this World Cup has challenged that idea and changed perceptions about FIFA.

Jun 29 • 3:33 AM EDT • Politics • ndtv.com

WVU Health System settles with federal government in controlled substance mismanagement case

$4.1 million fine and three-year improvement agreement announced.

Jul 13 • 1:12 PM EDT • Health • wvmetronews.com

Who Really Controls Your AI And Data? Why Every Business Should Care

As businesses become dependent on AI platforms and cloud services, questions over who controls their data, algorithms and digital assets are becoming impossible to ignore

Sep 18 • 1:23 AM EDT • Business • forbes.com

The anti-science movement is a quack with a bigger megaphone

The lone quack of 1979 reached patients one pamphlet, one waiting room, one late-night radio spot at a time. Today the same claims are amplified by algorithms.

Jul 24 • 11:00 AM EDT • Science • seattletimes.com

Brazil: Lessons from a Disaster

By Antonio Martins (Outras Palavras) Defeats become deeper and more prolonged when we misunderstand what caused them, avoid correcting course, and embrace “solutions” that only compound our mistakes. Lula’s government and the Brazilian left suffered a disaster in Sunday’s election. Contrary to every poll, the president finished the first round 2.2 million votes behind Flávio Bolsonaro—and, more significantly, 3.4 million votes below his total four years ago, when the machinery of the state was working against him and in favor of his opponent. His vote share declined in 98 percent of Brazil’s municipalities. The PL, the main party of the far right, elected 121 deputies and 28 senators, becoming the largest caucus in both houses of Congress. The parties aligned with the Centrão—Brazil’s bloc of transactional center-right parties—were somewhat diminished, but will still hold roughly 250 seats in the Chamber of Deputies and 35 in the Senate. That will make it easier to assemble the majorities needed to amend the Constitution or impeach Supreme Federal Court justices. If elected, Flávio Bolsonaro could appoint four members of the Court, substantially altering its internal balance of power. What produced these results? How can the far right be prevented from consolidating them into a prolonged political hegemony? And what is the essential change of direction the left now needs? Explaining the Disaster Across the commercial media and the exuberant chaos of digital platforms, interpretations are multiplying. Some focus on specific episodes: Lula’s absence from the Globo debate, his very close relationship with Alexandre de Moraes, or his emphasis on issues supposedly remote from most people’s everyday concerns, such as rare earth minerals. Among the more systemic interpretations, three seem especially questionable. One argues that the principal defeat was not the left’s, since its congressional representation declined only slightly. The decisive movement captured at the ballot box, according to this view, was a shift within the right itself—from the more moderate and transactional forces of the Centrão toward the more ideological and radicalized forces of the PL and Novo. A second interpretation holds that the nature of electoral and political processes has changed. The material interests and aspirations of society, it argues, no longer play the central role they once did; they have given way to forms of subjective self-identification that the left has yet to understand. And a third concludes that, because of these two developments, we should expect a political winter: a prolonged period in which the far right and the broader right dominate Brazilian political life. There are important elements in all three interpretations. The problem lies in what they deny or leave out: Lula’s third government was a small-bore government. It offered the majorities that elected it neither a clearly better life nor reasons to believe again in politics and its power to transform society. These failures flowed from two fatal accommodations, which I will examine below. As a result, the government suffered a slow but steady erosion of support. And, as has happened in so many Western countries, it gave the far right the opportunity to present itself as anti-system and to benefit from popular frustration and distress. The interpretation that emphasizes the migration of votes from the center-right and traditional right toward the far right has been advanced, among others, by the courageous journalist Leonardo Sakamoto. In his UOL column on Monday, October 5, he highlights the miserable behavior of the traditional conservative parties. Over the past four years, they repeatedly joined forces with the “neo”-fascists, helping to “normalize” them—a criticism that could equally be directed at almost the entire corporate media. Believing they could use the far right as a stepping stone, they instead ended up partly devoured by it. This was especially true in the Senate, where the PL’s gain of 13 senators and Novo’s gain of two came at the expense of the PSD, which lost nine; the MDB, which lost two; the PSDB, which lost two; and the PP, which lost one. Sakamoto’s observation is accurate and necessary, but it leaves uncomfortable questions unanswered. Why did the erosion of the Centrão benefit the far right rather than, for example, producing growth for the left—as has happened so many times before, both in Brazil and elsewhere? The answer is not difficult. Throughout its four years in office, Lula’s third government was almost invariably associated with the Centrão. Here lies its first accommodation: accepting the limits imposed by conservative institutions; avoiding, as much as possible, any attempt to strain those limits through popular pressure; and believing only in the modest changes that could be squeezed through openings in Congress. The failure of the campaign to end the six-day workweek is emblematic. Lula could have electrified the presidential race by pairing it with a popular campaign for passage of the measure. Instead, he chose to place his hopes in the hands of Senate president Rodrigo Alcolumbre. He generated no mass mobilization, did not force his opponents onto an uncomfortable defensive, and won no victory. In September 2025, the government squandered the greatest of its many opportunities to shake up conservative power in Congress. Popular opposition to the so-called Shielding Amendment—which, if approved, would have restricted criminal proceedings against members of Congress—brought crowds into the streets, mobilized primarily by artists and social movements. This opened the possibility for a broader political education: a campaign that could begin exposing the limits and dysfunctions of Brazil’s political system while putting Bolsonaro’s movement on the defensive for its predictable support of political privilege. The amendment was defeated. But the executive branch then backed away from the fight. It remained associated with an institutional world widely seen as corrupt, elitist, and indifferent to ordinary people’s struggles. In doing so, it forfeited its credibility as a vehicle for the protest vote. On Sunday, it reaped the bitter fruits. Are We Doomed? Taking a different approach from Sakamoto, the philosopher Moysés Pinto Neto and other writers have drawn attention, in their first analyses of October 4, to the fragmentation of the public sphere produced by Big Tech platforms. Through their algorithms, these platforms present each person with a different view of social reality. As a consequence, it becomes much harder to identify common material problems and demands—for example, the fight for labor rights or for a properly funded public health system. Voters no longer embrace such demands, these authors argue, because they no longer see themselves as part of a collective. Instead, they tend to value the identities assigned to them by neoliberal subjectivity—the role of the entrepreneur, for example. On this interpretation, the left’s possible error lies in clinging to outdated illusions. Moysés’s warning is highly relevant and resonates with arguments such as Naomi Klein and Astra Taylor’s “Apocalyptic Fascism and the Billionaires’ Bet.” Unlike a century ago, the far right no longer promises a radiant future. It concentrates instead on resentment—especially that of young men. “They belong to a generation that fears it may never own a home, but compensates with the pleasure it gets from constantly provoking progressives online. Convinced their future has been stolen, they share memes romanticizing historical authoritarian regimes,” the authors write. But Moysés seems to confuse this danger with inevitability. He fails to see that the left’s answer cannot be adaptation. On the contrary, it must strengthen the possibility of politics as collective action. Nor does he recognize that precisely such a reversal is already becoming possible in some of the most promising mobilizations of the twenty-first century: the election, at the end of 2025, of the young Muslim Zohran Mamdani as mayor of New York, on a platform centered on the intensely material fight for an affordable city; the Left Party’s victory in Berlin two weeks ago, built around the demand to expropriate private real-estate corporations; and the very recent wave of demonstrations in Spain—including an occupation of Madrid’s Puerta del Sol—in which tens of thousands of young people mobilized by the Tenants’ Union have demanded limits on rents. By putting itself in the straitjacket of the “fiscal framework” in the first months of 2022, Lula’s third government adopted the second fatal accommodation of its term. Faced with conservative institutions and a country devastated by four years of Bolsonaro, increased public investment was the government’s remaining weapon for offering a more dignified life to the majority and forging an anti-conservative pact with them. The same state that disburses 1 trillion reais a year to enrich a small class of rentiers could instead spend money to extend the Family Health Strategy to every Brazilian, at a cost of 30 billion reais; provide full-day public schooling and childcare to all adolescents and children—and, especially, relief to their mothers; achieve universal sanitation while cleaning up polluted urban rivers; multiply metro systems; begin transforming the urban peripheries; and undertake many other urgently needed projects. In April 2023, an article in Outras Palavras warned that the fiscal framework could “shrink the Lula government and preserve the forces that condemn the country to inequality and regression.” A year later, another article showed that restraints on public spending were strangling policies vital to ordinary people and undermining the president’s popularity. In November 2024 came a reality check, in the form of the government’s humiliating defeat in the municipal elections. Even then, the fiscal lock was neither removed nor loosened. On the contrary: throughout the government’s four years, fiscal restraint remained central to its political narrative. The administration proudly proclaimed that it had “put the state’s accounts in order,” without grasping either how misleading that phrase was—given the payment of 1 trillion reais in interest—or how badly the message was hurting it politically. Lula finally woke up in September 2026. The increase in Bolsa Família benefits, after four years of being frozen; the announcement that anti-obesity drugs would be offered through Brazil’s public health system; and the ban on online betting showed that the state has the power to act decisively on behalf of the majority when those in government are willing to confront obstacles. The huge marches that closed the campaign in Fortaleza, Salvador, Rio de Janeiro, and São Paulo on the eve of October 4 showed how such initiatives can mobilize activists and the broader public. But it was too late—and too little to change the outcome of the first round. History has not ended The possibility of a turnaround in the three weeks leading up to November 25 cannot be ruled out. It will not be simple or easy. Lula must not only make up a deficit of 2.2 million votes; he must also offset the movement of voters who backed other right-wing candidates in the first round, most of whom are likely to move toward Flávio Bolsonaro. Pulling that off will require a broad, grassroots effort to win over voters, potentially reinforced by new initiatives from Lula. The coming hours and days will be decisive. If Bolsonaro’s movement also wins the presidency, the result will create a high-risk situation for Brazilian sovereignty and democracy. The enormous challenges posed by such an outcome would require the left to reexamine, in depth and with a genuine willingness to engage in self-criticism, its political projects—or its lack of them—and its practices. But would that mean we are condemned to the long winter of far-right hegemony that some analyses are beginning to forecast? It is far too early to say, suggests a recent text by Álvaro García Linera, the thinker who served as vice president of Bolivia under Evo Morales. His central hypothesis is that this political current will prove short-lived. At a moment when the entire world is once again talking about sovereignty and protecting national economies, the far right offers a stale, recolonizing neoliberalism. Its submission to the United States is as blatant and pathetic as Flávio and Eduardo Bolsonaro’s overtures to Donald Trump. Its project amounts to something like every man for himself: greater inequality, capitalism without limits, devastation, and supremacism. Will it work? In the United States, just two years into his second term, Trump himself is reaching record levels of unpopularity and may be on the verge of losing Republican control of both the House and Senate in November. Every new beginning is difficult. Whatever the outcome of the second round, the defeats of October 4 show how profoundly the Brazilian left will have to rethink and rebuild. But just as it would be foolish to underestimate the seriousness of its mistakes, it would be equally foolish to conclude that rebuilding a political project is no longer possible. History has not ended. The times are moving faster than ever. Originally published in Portuguese by Outras Palavras. English translation by Eric Blanc; I’ve added the subtitles. More We can’t stop the far right at home or abroad without a revitalized labor movement, which is yet another reason why you should become a sustaining supporter of the Emergency Workplace Organizing Committee (EWOC) — one of US labor’s pivotal bright spots. Relatedly, Jacobin published a short piece of mine reflecting on a decade of Bernie Sanders inspiring a whole new generation of young worker organizers.

Oct 6 • 10:08 AM EDT • Politics • laborpolitics.com

'Move Imran Khan To Hospital': Pak Top Court Amid Health Concerns For Ex-PM

The 73-year-old cricketer-turned-politician has been in jail since August 2023 after being convicted in a string of cases that he says were politically driven following his ouster in 2022.

Aug 18 • 4:20 AM EDT • Health • ndtv.com

Why Australia chose the world's biggest political stage to reveal OpenAI hack

Australia, which has strict social media restrictions and has proposed controls on algorithms and smart glasses, announced the breach at the UN.

Sep 24 • 2:06 PM EDT • Politics • bbc.com

As Duke Health implements AI, oversight initiatives try to ensure ethical practices

At the center of Duke Health’s approach to an ethical use of artificial intelligence in healthcare is the flagship ABCDS Oversight Initiative, which evaluates and governs any algorithms, including AI ones, that are used in patient care.

Jul 27 • 10:13 PM EDT • Health • dukechronicle.com

Scientists Can Predict Where the Next Devastating Earthquake Will Hit

Although predicting the timing of seismic catastrophes remains tricky, these geophysicists’ algorithm could help communities located near subduction zones.

Sep 14 • 9:30 AM EDT • Science • popularmechanics.com

Astrophysicists win AI grant for 'Holy Grail' project to automate laborious calibration process

Anthropic's AI for Science Program has awarded the UC Santa Cruz team $50,000 in API credits to fund the development of an automated wavelength-calibration algorithm for astronomical spectra.

Sep 25 • 1:33 PM EDT • Science • news.ucsc.edu

EU-funded researcher awarded 2026 Balzan Prize for The Social Science of Digital Technology

Sociologist Elena Esposito has won the prestigious 2026 Balzan Prize for her pioneering work on how algorithms participate in social...

Sep 16 • 6:02 AM EDT • Science • cordis.europa.eu

The new "science golden age" looks suspiciously like a grift

This edition of PN is made possible by paid subscribers. Become one ⬇️ 🚀 Subscribe to PN 🚀 The Trump regime is trying to usher in a “Golden Age in Science” where scientific research is run like a Trump family business, artificial intelligence is all that matters, profit is everything, and expertise is for chumps. Or at least that’s the vision emerging from a new 123-page “Report to the President” from Michael Kratsios, the Director of the White House Office of Science and Technology Policy. In a normal administration, you could probably skip over learning about a science policy document — but this is no normal administration, and this is no normal science policy document. Instead, it’s Trump’s attempt to end the actual golden age of science that was kicked off in 1945 with the publication of '“The Endless Frontier” by Vannevar Bush. [ ](https://www.publicnotice.co/p/harmeet-dhillon-portland) [ Harmeet Dhillon demonstrates danger of posting through it ](https://www.publicnotice.co/p/harmeet-dhillon-portland) Liz Dye · Jul 29 [ Read full story ](https://www.publicnotice.co/p/harmeet-dhillon-portland) Bush, at President Franklin Delano Roosevelt’s request, laid out a vision of federal support for science that centered on funding universities to do basic research, saying that as long as those institutions were “vigorous and healthy and their scientists are free to pursue the truth wherever it may lead, there will be a flow of new scientific knowledge to those who can apply it to practical problems in Government, in industry, or elsewhere.” Kratsios’s report is supposed to be the successor to Bush’s, and even apes the format, but this isn’t really a document about science policy. It’s one about ending science research as we know it in favor of shoveling cash at big tech companies. While it might be temping to wave this away as an aspirational policy document, Trump has already begun to shift billions in research funds to AI projects and has taken a hatchet to science funding across the board. He’s teed up the shift Kratsios proposes here, and since neither Congress nor the Supreme Court seem inclined to stop him from doing whatever he wants with taxpayer money, it isn’t doomering or hyperbole to say we are staring down the barrel of a world where the only “science” the government intends to support is for-profit tech and defense advancements. Science for dummies Part of how you can tell this isn’t a document about science is that you are immediately faced with an absolute wall of business-speak of the worst sort. Did you know we must “tightly couple our science and technology enterprises to ensure that groundbreaking ideas invented in the United States are rapidly prototyped, tested, manufactured, and scaled domestically”? You do now! That tone isn’t surprising, because Kratsios has no background in science or research. He has a bachelor’s in political science and spent most of his career working for Peter Thiel at Thiel Capital. After a stint in the first Trump administration as Chief Technology Officer, where he also did not do any science, Kratsios was tasked in 2020 with overseeing the Pentagon’s research budget for a brief time, a position that, as the Wall Street Journal put it, “carries a great deal of power in terms of dictating investment allocations.” He spent the Biden years at Scale AI as “head of strategy,” which you will also note is neither a science nor a research job. But Scale AI has contracts with the Department of Defense and, as the Washington Post wrote in 2023, “has been aggressively pitching itself as the company that will help the US military in its existential battle with China.” And now you have the necessary background for our new golden age, which is going to be a golden age of taking money away from scientists and universities and giving it to companies like Scale AI. A note from Aaron: Working with brilliant contributors like Lisa takes resources. If you aren’t already a paid subscriber, please become one to support our work. 👇 Subscribe In a genuinely galling move, Katsios spends a lot of time slamming universities because of “administrative burdens on researchers” and the “well-documented inefficiencies” in academia, the message being that those damn universities waste the time of amazing researchers with paperwork. Except it’s the administration that is attempting to impose inefficient, unnecessary burdens on those researchers with a proposed Office of Management and Budget rule that would require senior political appointees to approve every science grant and ensure it aligns with Trump’s personal priorities. Researchers would also have to seek prior government approval to attend conferences or publicize their research. And political appointees could terminate any grant at any time if they feel like it. [ ](https://www.publicnotice.co/p/omb-rule-science-funding) [ The murder of expertise ](https://www.publicnotice.co/p/omb-rule-science-funding) Lisa Needham · Jun 11 [ Read full story ](https://www.publicnotice.co/p/omb-rule-science-funding) But when it comes to AI, we’re going to “restore permissionless innovation.” The report proposes to give lucky ducky innovators a pile of cash up front for grants lasting five years or longer, saying “awards should be fully-funded in year one, with all resources earmarked upfront, to minimize administrative burdens and reduce pressure for researchers to generate intermediate results to secure continued funding.” Yeah, wouldn’t want to have to make the Very Special Tech Bros show results or get money parceled out year by year like the plebes at universities. The report also proposes that we should have “fast grants” with “simplified applications requiring just a few pages of writing” because that is definitely how science works, just writing a bit off the dome and getting some cash. [ ](https://www.publicnotice.co/p/trump-ai-action-plan-altman-llms) [ Tech elites are turning AI into ChatGPTrump ](https://www.publicnotice.co/p/trump-ai-action-plan-altman-llms) Paul Waldman · July 29, 2025 [ Read full story ](https://www.publicnotice.co/p/trump-ai-action-plan-altman-llms) Oh, and we should have prizes and challenges, where multiple teams compete. The proposed prizes include incentives like “advance procurement commitments,” which means the federal government agrees to buy whatever is being developed. There’s also “regulatory fast-tracking,” making sure all those great AI thingies don’t have to wait too long to be unleashed on a market. These aren’t incentives to do science. These are incentives to develop things with tax dollars that you can then sell to the government for more tax dollars. In the suggestion that might be the most unhinged, the most very opposite of how both government and research work, our new age will include “golden tickets.” That’s where an individual reviewer in an agency — yes, just one person — can “recommend unconventional proposals that may not pass consensus-driven review panels” because somehow this will “surface valuable ideas too divisive for committees and attract higher quality reviewers by empowering them to exercise independent scientific judgment.” Yes, that’s saying that a random person in an agency should be able to shovel money at some idea even if it can’t pass a review panel, and that the opportunity to do so will attract better employees because apparently all the best minds in science are chomping at the bit to be allowed to unilaterally fund AI research based on their personal vibes. But that isn’t at all how actual science works, and actual scientists aren’t unhappy about review panels. They work collaboratively, not siloed in some secret race to create the next AI chatbot. A 2020 study by the National Bureau of Economic Research examined whether peer review panels succeed in helping agencies predict which research ideas will best advance science. Peer review was better at finding “diamond in the rough” applicants than a single government program official. How about that. Trump always wants a taste It’s painfully clear that what Katsios is trying to do here is turn the whole of government science research and funding into the sort of “move fast and break things” paradigm that is the hallmark of big tech. That method works to line the pockets of tech executives, but it isn’t science. There’s a reason the government funds different kinds of research than private tech companies do. What Katsios either fails to understand or deliberately ignores is that the federal government is the main funder of basic research precisely because it can take a long time for it to bear fruit and is not intended to be profitable as such. “Basic” here does not mean rudimentary or simple, but rather research that is designed to advance scientific knowledge instead of achieving a specific outcome or product. Big tech companies don’t widely fund that sort of research, because it doesn’t make them money. Industry dollars account for only about 20 percent of basic research funding, while the government contributes around 60 percent. Big tech companies and startups do, however, rely on and benefit from that basic research. A 2019 study found that 35 percent of all patents obtained by venture-backed startups from 1976 to 2016 cited federally supported research. Basic research funded by the NIH contributed to every single drug the FDA approved from 2010 to 2016. [ ](https://www.publicnotice.co/p/trump-saudi-nuclear-deal) [ The Saudi nuclear farce and governance by whim and meme ](https://www.publicnotice.co/p/trump-saudi-nuclear-deal) Noah Berlatsky · Jul 27 [ Read full story ](https://www.publicnotice.co/p/trump-saudi-nuclear-deal) Unlike inventing the next ChatGPT, actual science is slow and iterative. Government funding of research that, on its face, would not appear to lead to any big new invention is actually what underpins those big new inventions. If the government hadn’t funded research into desert lizard venom, we wouldn’t know about semaglutide, and we wouldn’t have Ozempic. If research scientists hadn’t studied how bees optimize nectar foraging within a colony, we wouldn’t have the algorithm that assigns internet traffic across computer servers. The report assiduously ignores that a symbiotic, profitable relationship between the federal government, universities, and private companies already exists. Each federal research dollar generates about $1.50 to $3.00 in long-term economic benefits. The $36 billion the government awarded in NIH research funding in fiscal year 2025 generated $94 billion in new economic activity. Trump’s proposed new golden age is intended to strip those billions from universities, resulting in the dismantling of departments and the shuttering of labs. But Katsios has a plan for that: Universities should just become trade schools to churn out no-show STEM degrees! “Require universities and community colleges to embed practical technical training and externships into STEM curricula. Let hands-on experience and industry credentials count toward degrees.” It’s quite the feat to, on the one hand, strip schools of funding for scientific research while simultaneously demanding to control what is taught and, oh, also, colleges need to give degrees to employees at big tech companies just for being employees at big tech companies. The new golden age would also exacerbate the brain drain Trump’s policies have already caused. Nature found that in the first quarter of 2025, US scientists submitted 32 percent more job applications abroad than in 2024. A March 2025 poll by Nature of roughly 1,600 scientists found that over 1,200 said they were considering leaving the country. Europe is investing $565 million to make it a “magnet for researchers” and provide funding for scientists who relocate. The new golden age is also about embedding Trump’s xenophobia into the fabric of American science. The report complains of an “over-reliance on foreign students” that “creates a security challenge our institutions are ill-equipped to address” and that a “reliance on foreign talent sidelines American students.” This is, to put it mildly, nonsense. International students pay far higher tuition and often receive no financial aid. Their tuition dollars are what make it possible for American students to get financial aid. In the 2023-2024 school year alone, international students contributed $43.8 billion to the US economy and supported over 378,000 jobs. Those 1.1 million international students accounted for only six percent of the total number of students in higher education that year, but were such an economic engine that for every three international students, one job was created or supported. It’s impossible not to wonder whether part of Trump’s loathing for universities and their leading role in scientific research is that there is no way for him to corrupt the process and make money off it in the same way he can by giving millions and billions to Silicon Valley types who, in turn, give him millions in cash. There’s no way for his children to invest in universities that the government then gives no-bid contracts to, fattening the family’s bottom line, which is pretty much the Trump sons’ current venture capital practice. Trump and Katsios’s view of science is narrow, cramped, and wrong. We’re not in a crisis, we’re not a failed state. We’ve been a science superpower for decades, and the only thing that threatens that is Donald Trump. Tech bros are already living high off the hog in this administration. They don’t need any more of our tax dollars. Hopefully this new golden age never comes to pass. Thanks for reading Public Notice. This post is public so feel free to share it. Share We’ll be back with more tomorrow. If you appreciate today’s PN, please do your part to keep us free by signing up for a paid subscription. 🚀 Subscribe to PN 🚀 Thanks for reading, and for your support.

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Sundar Pichai: Google’s Frontier Imperative, Compute Scarcity Dilemmas, Future AI Products and Businesses

Alphabet CEO Sundar Pichai has little patience for doubts about Google’s willingness or capacity to compete at the frontier of AI. His company’s latest Gemini 4 Argon model — long-promised and recently delivered — beats OpenAI and Anthropic’s latest public models on coding, math, science, and knowledge-work benchmarks and has placed Google’s AI back among the world’s best. Amid chatter that Google was fine with good-enough lightweight ‘Flash’ models, Pichai pushed back hard against any suggestion that company is limiting its research ambitions. “We won’t do it any other way,” he told me in a Big Technology Podcast interview recorded this week at Google’s Mountain View headquarters. Pursuing cutting-edge research is costly for Google, requiring compute and chips that otherwise could be sold at a high markup by its cloud business. But Pichai said allocating resources to frontier AI development is “ground zero” for Google. “Our cloud business is at any given moment in time forgoing short-term revenues because we are making compute trade-offs to support frontier AI development,” Pichai told me. In our conversation, we discussed Argon (which Pichai says processed three times as many tokens as Gemini 3 in the 72 hours after launch), AI’s role in developing better models, the AI interface of the future, competition with OpenAI, Google’s partnerships with Anthropic and Apple, data centers in space, and the nature of his optimism. You can read the full conversation, lightly edited for length and clarity, below, or listen to it on Apple Podcasts, Spotify, YouTube, or your podcast app of choice. Alex Kantrowitz: Google is back at the frontier with Gemini 4 Argon, which beats OpenAI’s and Anthropic’s public models on benchmarks spanning coding, math, science, and knowledge work. The conventional wisdom was that Google had given up on the frontier to focus on fast, good-enough models for its products. That was wrong? Sundar Pichai: Very wrong. Look, we are so committed to being at the frontier. It’s in our DNA. We won’t do it any other way. We’ve been thinking about the AI frontier for well over a decade now. The frontier is very dynamic. It’s very competitive. It’s very exciting. I couldn’t be more pleased with how Argon has come together. We took our time with it. We made sure we tested it safely and deployed it carefully. We took a bit longer than our other models, given its capabilities. Across the board, be it for agentic work, knowledge work, coding, creative tasks, Argon is one of the really good frontier models. The first 72 hours since we launched, it’s almost up to 25 trillion tokens used, which is about three times what Gemini 3.0 had in the first 72 hours. And we are capacity constrained as we are serving it. Excited, but there are always areas….. You know, the frontier is very dynamic, and you have to keep evolving the models. There are areas which we can see we need to improve as well. We’ll be at work on all of that as well. Which areas? On any given model, Argon would be across the board better at everything if you just look even two months back. But the space is so evolving. Even on coding, for example, there are pockets where we know we need to do better, and there’s clear feedback. We’ll make it better. The argument for standard models is that they’re already good enough to build great products, with the frontier valuable only for certain tasks. Meta built Muse with good models rather than the best, and Google could put standard models into Maps, Gmail, and Search. Why isn’t that sufficient? It’s definitely not how I have thought about it at all. We are deeply committed to driving the state of the art and actually pushing where this technology can go, all the way from transformers. That’s a journey we’ve been on. You will see us continue to do that. When you’re at the frontier, you get an earlier look into the emerging capabilities, earlier than others, which helps you better prepare for the future. We are fortunate to have a wide set of opportunities across consumer and enterprise. Some of our cloud customers, for sure, want the model at the frontier. The frontier models will be better at developing the next set of models in an era of recursive self-improvement. I would argue it matters there. Internally, we have definitely seen a steep point of inflection by deploying Argon to our developers. It definitely tackles problems which our previous models could not do. Being at the frontier puts you at the innovation frontier and helps you drive that as well. There is room for models of all sizes and capabilities. That’s why we did Flash, Flash-Lite, etc. I agree with you that you can develop very good product experiences with not necessarily the most frontier model, because they tend to be slower and more expensive too. But we care about the Pareto frontier, and we’re committed to doing all of that. You said developing frontier models gives you a look at what’s coming. What do you see on the horizon? What makes this moment so exciting is you build something which you think is a culmination of months of work, and you put it out. You already see clear headroom ahead. We’ve all been working on these agentic, long-horizon capabilities, but clearly the frontier is going to have multiple of these agents working together to do things. That’s an example of an exciting paradigm underway. It’s also clear to me that users, both in consumer and enterprise, will have access to some multi-agent framework for them, working all the time doing things. It feels like a moment of infinite possibilities. Sergey Brin said one of Argon’s core innovations was relying more on the model to help develop the model. Yet incidents involving OpenAI’s bots breaking containment and Google’s bots accessing a site they shouldn’t have suggest these systems still aren’t perfectly aligned. Isn’t there reasonable cause for concern about accelerating toward recursive self-improvement? There are three parts to your question. Let’s first talk about RSI itself. You’re right, Sergey mentioned RSI being a critical contributor as we are developing Argon. But to be very clear, today, in a continuum, we are pretty far from the end state of RSI or something like that. The right way to describe it is you have RSI loops along with AI researchers in various aspects of model development. You almost have agents helping AI researchers in some automated way work together to build these models. It’s good to at least understand where we are today, and it’s super exciting. I expect that continuum to keep making progress as we go into next year. The second question is around safety. Security comes from defense in depth. You’re talking about alignment, obviously. We all are doing a lot of work to make sure the models are as aligned as possible. But it’s an active area of research, and there are clear gaps. That’s not the only thing we rely on. You have the work you do on alignment, but beyond that, we put a lot of effort into real-time monitoring as these models are working. Making sure you have good telemetry into what’s happening so that you can intervene in real time if you see unexpected behaviors. How you do that, the architecture with which you do that, how you make sure it’s all sandboxed and isolated in a way that it can continue doing its job during real-time monitoring — these are all areas of investment. Obviously, when we deploy these models, giving customers, enterprises, control, both with real-time monitoring, but their ability to decide where they deploy, what type of data which models ever have access to, etc. All that work is very, very important, and we are doing a lot of that. As an industry, we need to constantly up our game and take the time to do all this stuff, including dedicating the compute required to make sure you can safely deploy these models. The third part is: Is it all enough? This is why there are a lot of conversations in the industry, and I think it’s reasonable to be worried. The right way to approach something you worry about is early on thinking about what more we can do. As an industry, you’re definitely seeing increased levels of work on alignment, testing for safety, etc., as we deploy these models. The next step would be developing better standards so there are cross-industry standards of how we do these things, including the ability to evaluate using independent evaluators. If you look at areas like financial controls, we have accounting standards, we have independent auditors. Those are all the next natural steps for us as an industry to work toward, and regulation will end up playing a role as we go through all of that as well. Google pursues both language models and world models, while some other AI companies like OpenAI have focused more heavily on LLMs as the path to AGI. Why is world-model research important to you? As humans, you obviously perceive the world around you, and you’re taking all those inputs. Making sure these models are able to understand the laws of physics, simulate the world in high fidelity. All that, I think, are key enabling blocks for intelligence. We are working across all possible applications of this. Take an area like robotics. It’s not an accident: If you look at embodied reasoning benchmarks, Argon is state of the art. That all comes from our experience building world models. Inherently, I would expect there will be architectures which will converge all of this in the future. But for us, we view this as, today, this jagged frontier that is intelligence. We want to be at the frontier of all the possible approaches, and that will help us both drive better research, better insights, and develop a much better AGI model, is how we think about it. Some people argue Google should focus on selling its computing resources rather than using them for AI research. Cloud wants to sell compute at a high markup, while researchers need a very big computer to build the best models. How do you balance those demands? Ground zero for us is making sure we are investing in the compute that’s needed to be at the AI frontier. In 2015, I set the company to be AI-first. AI is that key horizontal ingredient across everything we do, all the way from Search to Cloud to Waymo. That is ground zero for us, making sure both for research and developing the models that we have enough compute needed. Beyond that, we have these independent businesses which each have their own long-range plans. They’re using those long-range plans to project forward, and we plan for compute for all of them. The cloud team, they have a business and they have a projection and they have demand signals. They have committed contracts. With that help, we can plan on a long-term compute trajectory for them. We’re fortunate to have this wide set of opportunities. There’s no company in the world which isn’t making trade-offs at any given moment in time. But the first principles we operate on is making sure we have enough compute to invest in the frontier, and that’s non-negotiable, and everything else works on top. Remember, we are building our own compute, our own data centers. There are long lead times. The compute we have today is a function of decisions I made in 2024. Even if you think back to a year from today, last year the agentic coding takeoff hadn’t happened. Today, obviously, when you sit and look at anything, everyone is short—not just us, other companies—and everyone wishes you had two times what you had planned for. That comes with the territory. At any given moment in time, we are planning for what we would need two to three years out in the future. You’re going to find some decisions you make may look suboptimal two years out, depending on where the state of the art is. I don’t spend too much time thinking about it. The way I think about compute allocation is making sure Google DeepMind, both for research and frontier model development, is cutting edge in terms of compute access. After that, we plan for all our businesses. It’s great we can provide our compute to other people too, like we have done. It allows us to scale that up, get real tests for it in the widest application set possible, test it in the real world, and make sure we are at the cutting edge of technology at every layer of our AI stack as well. Ben Horowitz relayed on a podcast that Andy Jassy had said compute was sold out until 2028. Are you seeing the same thing? If today a customer is coming out and asking us for compute in ’27, that is incredibly difficult to do. Even last year we went out and leased some short-term compute to accommodate some customers and help them out. But yes, we are all dealing with similar timelines. Every major technology shift seems to involve overbuilding, from railroads to fiber. Do you think that’s happening here? It depends on the time frames you’re looking at. This level of build, if I take a long enough time frame, it makes complete sense to me. We are at early stages of the most consequential technology ever, and this is not like fiber. It’s not dark. We are using it. We are capacity constrained on a daily basis. The applications, the underlying technology is improving rapidly. But running a company, the way I work through the moment is you look at your demand signals. You look at your committed revenue opportunities that you’re getting in the form of contracts. You balance that with the underlying technical capability, or internally you balance how we are using it internally, the model trajectories. Based on all that, you take a disciplined approach—a confident, aggressive, but disciplined approach—to plan out compute. But when you talk about the broader economy as a whole, you’re having people operate with various levels of risk. I focus on what I can control as a company and plan through it. If your question is, can there be an overbuild? Yes, because you can have some constrained components. Remember, what matters is a set of everything you need to build out that compute. Memory, electricity, electricians…. Land. Like 2,000 components or more. You could get constrained at some point. There could be things which cause bottlenecks, and then you could have overbuilds. There could be external shocks to the economy. There are macro risks. All that is possible at any given time. But am I confident that the compute we are planning for, we will have uses for it given enough time? Absolutely, yes. I was in the room when you said AI would be as profound as fire and electricity. If you believe that, shouldn’t you give researchers the biggest possible computer, even at the expense of today’s cloud profits? We’re all doing some form of that. Our cloud business is at any given moment in time forgoing short-term revenues because we are making compute trade-offs to support frontier AI development. What you’re seeing is true already. But you want to be thoughtful. Even our AI researchers have some constraints, which are also good. That’s what inspires creativity. That’s what helps you come up with a more efficient algorithmic breakthrough, maybe to use less memory. What we are committed to is to invest what’s needed to stay at the frontier. OpenAI President Greg Brockman told me a few months ago that the ideal AI product might have no interface at all—just as natural as conversation. What do you think the interface looks like in the age of AI? Computing historically has been humans adapting to computing. With AI, it’ll be the other way around. The technology will adapt seamlessly to how humans live and go about their day-to-day life. You want to be able to speak to that AI when you want to. It has context. It can be persistently running somewhere, give you continuity. You’re not dealing with a lot of complexity. It’s natural. All that makes sense to me. That doesn’t mean it won’t have any interface, however. Nowadays, even on my phone, Gemini voice models are so good. I can just press the microphone button. I can just ramble. That’s why we call it Rambler. I can ramble. It intelligently corrects with my personal context. Even my friends’ names, weird names, it gets it right, even if it first guesses it wrong. It’s amazing. I’m not going in, backspacing, editing once I finish saying it. It’s remarkable how good it’s gotten. But there are going to be moments you will need an interface. You may want to entertain yourself. You may want to watch a video. You may ask your AI to create something, and you want to watch it, consume it. The way I think about it is, you want it to be as seamless and as natural as possible, and that you are not doing the hard work adapting to whatever the interface is. The technology is adapting to how you’re working at any given moment in time. When I asked Sam Altman about competition from Google, he argued that OpenAI was building AI-native products and implied Google was bolting AI onto existing ones. Do you accept that framing, and can a “bolt-on” compete with an AI-native product? First of all, it’s a very dynamic moment. We are all pushing each other. OpenAI has pushed us as a company too. This is not just we are doing something to them. Far from it. Yes, Google went into its own version of a code red too. In the AI moment, all of us have to respond with varying levels of urgency at any given moment in time. We are constantly building new products as well. Gemini was built from the ground up as a product—the Gemini app I’m talking about. Or tomorrow, as all of us are adapting to agents, are you bolting agents onto ChatGPT, or are they building it natively? How would you think about that? You can always put these questions back. I don’t think of it that way. I think of it as you want to build world-class product experiences that are AI-native and they are winning. Some of it is adapting products, some of it is rethinking new experiences. They will all be part of how you approach this moment. How do you expect to make money from these AI interfaces? If an agent recommends a product and I buy it, should it take a cut of the sale, even if that’s disclosed? I’m going to put enterprise aside. Consumers are going to increasingly value this technology. Take software developers today. Developers are willing to pay for these coding models because it really helps make them more productive. Nobody would want to code the way they were coding three years ago, or even the same time last year. When you’re delivering value, there’s value to be had. I’ve never seen examples where consumer products really scale to billions of users, where people are using it intensely, and haven’t had monetization to go along with it. If you’re providing value to people, there is inherent value to be had. The nature of it may change. You’re asking a scenario in which agents are doing stuff for you. At the end of the day, you will have commercial needs. The agents need to fulfill those commercial needs. The providers on the other side are going to see value in that. There’s going to be some mechanism by which it gets fleshed out. That’s what an efficient system does. But what matters is that you’re building those world-class consumer experiences. I promised myself one product feature request: Can you make Gemini-powered search in Gmail as good as searching Gmail through ChatGPT? What’s going on here? One of the exciting things we announced recently is the unified Gemini agent for enterprise, and we’re bringing that to consumers. These are persistent agents which run 24/7 in the cloud. We’re spinning up a micro VM effectively for every query, and from the runtime, the harness, the model—it’s all a headless service. It’ll work everywhere, and it has your context with you. You can address this agent whether you’re in Workspace, in Search, in Gemini. Agents are going to drive this unification from a customer experience, which people are looking for. When you have a broad product portfolio and you’re moving forward fast, there are always areas where you’re moving forward faster than some other areas. That’s valid feedback. But we are putting a lot of time and effort into making sure, as we build out our agentic experience, that unification and that capability works well across all these services. People have asked where Google’s equivalent of Muse or Dots is. Do you anticipate creating a new product to compete with them? Our goal with Gemini is to be a personal, proactive, and powerful assistant. That’s what we are working toward. You saw us describe the early versions of it at Google I/O this year with Spark. Effectively, already, if you open the Gemini app as a Pro or an Ultra user, you see Chat and Task, and they start giving you that agentic experience. With Argon now, that capability is dramatically accelerated. What we announced recently in our enterprise event, we are bringing that to consumers as well. Soon in Gemini, you will be able to ask something, and it’s a continuum. You don’t need to think about whether you’re asking a chat or an agentic task. It’ll just automatically do the right thing, and it can work persistently in the background securely and get things done for you. Already, empirically, when I look at what’s coming into Gemini in our app, last year you would have tagged them and most of them would have been chats. But a steadily growing percentage is already agentic tasks in Gemini. Yes, we are going to bring personal agents to our consumers. It’ll work in the Gemini app. It’ll work in Google Search. It’ll work in Gmail. You can address it, call it, etc. We want to make sure we’re getting it right. It’ll be powered by Argon, and we are very excited to bring it to our users. Google has invested and committed tens of billions of dollars to Anthropic. What do you see in Dario Amodei that makes you want such a deep investment and partnership? Very early on, when we looked at the founding team of Anthropic, it was very clear to us this was a world-class team which was involved in some of the earliest GPT models. We were quickly able to assess that they would be at the frontier. We’ve always tried to identify and partner with companies which would be at the cutting edge of technology. We’ve done that with SpaceX. We’ve done that with Anthropic. That’s what we saw, and we had a chance to partner. Is your partnership with Apple to license Gemini for Siri expanding, contracting, or standing still? For both us and Apple, that partnership was about translating the capabilities of AI into better experiences for users. I thought Apple has done a phenomenal job of incorporating—they customize these models and build it into Siri—and it’s been great to see the reception. We are committed to doing more. You’re going to do more with them? There’s nothing specific to comment, but it’s a partnership. It’s working well. What have you learned so far about the feasibility of data centers in space? It’s a very exciting project. Projects like this remind me—I still recall that moment in the desert for the DARPA Grand Challenge, watching the Stanley car compete in it, which ended up being the basis for Waymo. With all these technologies, you’re at a moment in time where it looks a bit like sci-fi, but 10 to 20 years down the line, it’s actually working in a meaningful way. I feel the same in space. We’ve always believed in betting big on these technologies. Quantum has been a great example of it. We’re probably 10 years into the journey. Waymo, we are almost 20 years into the journey. Space, we are two to three years into the journey. It’s clear to me that it’s an approach which will make a lot of sense in the future. Being in sub-synchronous orbits with 24/7 access to the most abundant form of energy we have access to will make a lot of sense for this exponential increase in compute that we are going to need. Having said that, this is a challenging project. The team has disciplined milestones. It was a super exciting milestone to send the TPUs up to space. Sometime, as we are speaking, we are expecting a “hello world” moment from them. I look forward to seeing that happen, and we’ll continue investing here too. You previously said 25% of the code written within Google was written by AI. What’s the number today? That number in our world today is meaningless in the sense that every part of what we now touch is influenced by AI deeply. What we are tracking with Argon, for example, it’s been a point of inflection. People are using Argon to really go tackle complex projects, including projects which affect millions of lines of code. In the four weeks since we have launched Argon, we have seen a 30% increase in CLs, and more importantly, almost a similar increase—a one-third decrease in rollbacks. People are writing code more productively and better code with it. We are tracking it with newer metrics now. How are you dealing with opposition to data centers? If you can’t build in the U.S. the way you want to, where else do you go? We’re very committed to building our data centers very responsibly. When I look at our largest data centers in some of the communities where we have been the longest, I visit them, I talk to people. There are people who come and tell us their kids have gone to college because of our investments in these communities, and we’ve grown in those communities. To me, this is about doing it right. Engaging with the communities early, doing it transparently. We are committed to making sure we are bringing incremental power, and we cover 100% of the cost of our power needs for these projects. It’s understanding the impact on neighborhoods and making sure you’re working thoroughly to address them. Done rightly, you can continue making progress. But we are constantly taking feedback into account. I do worry not everyone operates the same way. People have been surprised by data centers showing up in their backyard. Those are all important. As an industry, we should continue to do better. But part of the reason, in a 20-year time frame, we are thinking about things like data centers in space is making sure we are able to put these data centers where maybe the trade-offs will be different. What unproven area of AI is Google working hardest to crack? Is it continual learning—getting models to keep updating after release—or something else? There are multiple parallel paths. You saw with Argon we have a million output tokens. With our Gemini models early on, we had a one-million context window on the input side. These are all examples of breakthroughs we are constantly working on. How do you think about synthetic data? How do you think about continual learning? How do you think about more efficient architectures? We do dedicate a meaningful percentage of our compute to these research bets too. It always goes hand in hand, including our world models. Those are all bets we are placing. Sometimes, as part of these bets, there are ideas that emerge which just make their way into the mainline models. Maybe we don’t fully talk about it, but we have multiple steps in these areas. Working in technology requires optimism that building something will improve the world, but those bets can leave real consequences when they fail. I recently returned to a town in El Salvador where enthusiasm for Bitcoin had given way to unfinished construction and something resembling a ghost town. How do you maintain the optimism you spoke about in your Stanford commencement address while accounting for those risks? I genuinely have a deep conviction that improving technology and improving access to technology makes people’s lives better. It’s been my personal experience in my life, out of which comes that deep conviction, optimism. But with that, I’ve always felt humanity’s journey is always about working hard to harness that technology to make it work well for society. The optimism is not an optimism that you just build whatever you want, whenever. You work hard to make sure you’re building technology in the service of humanity, to make the human condition better. That’s got to be the defining goal as we are approaching it. With that framework, I’m very, very optimistic. We are in very early stages of the kind of progress that we are going to see on many areas. We’re all talking about areas like healthcare. A lot of the progress is ahead. The ability to make things in a fundamentally cheaper way on many things where things have been getting expensive for a long, long time, discovering new materials, etc., which really bend the curve—it’s tough to step today and understand the impact of all that, but they’re going to be very profound. I’m very optimistic, but we have to be rightfully concerned about all the ways this technology can go wrong. Sundar, I know you don’t do many of these. Thank you. Thanks, Alex. Appreciate it.

Oct 20 • 1:47 PM EDT • Technology • bigtechnology.com

'I’m very optimistic there's life out there.' This scientist says the search for alien life is only getting started | BBC Sky at Night Magazine

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Scientists Discover Strange New State Of Matter. What Is It?

It is too early to predict practical applications. For now, the main importance of the research is that it expands scientists' understanding of how matter can behave under extreme conditions.

Oct 19 • 1:44 AM EDT • Science • ndtv.com

The Science Behind Erling Haaland's Sleep Routine: Do You Really Need These Sleep Hacks?

Elite footballers spend countless hours perfecting their game, but one of the biggest factors behind peak performance happens away from the training ground. Er

Jul 6 • 4:26 AM EDT • Science • ndtv.com

Microsoft Adds $450 Billion In A Day, Beats Nvidia. What's Behind The Surge

Azure, Microsoft's cloud computing platform, reported stronger-than-expected growth as companies continued spending heavily on AI infrastructure.

Jul 31 • 12:55 AM EDT • Business • ndtv.com

Samuel Williams Honored with E. O. Lawrence Award

Awarded for creating transformative performance modeling tools and algorithms that help supercomputers run more efficiently.

Oct 13 • 12:57 PM EDT • Science • newscenter.lbl.gov

The Big Holes in Wearable Heart Rate Variability And Readiness Scores

On September 9th, Apple announced it was revamping its Apple Watch Health Sensing System, rolling out a Readiness score (0 to 10), and increasing the frequency of heart rate variability (HRV) outputs 24-fold. This can be viewed as upping its competition with various other consumer wearable sensors. These “readiness scores” as a composite of multiple metrics, with heart rate variability (HRV) being front and center for most. The majority of Americans are now using wearable sensors, which equates to well over 100 million adults. HRV and Readiness scores are increasingly being marketed as a measurement of autonomic nervous system health, a digital marker for future disease, a clock for biological age, and a holistic metric to promote healthspan and even longevity. (Apple also introduced a new longevity tab and “Health Age”.) None of this has been proven. In this edition of Ground Truths I am going to review what we know about heart rate variability and readiness scores. Heart Rate Variability HRV is the variation in normal heart cycle timing. The variability of the heart rate, the barely perceptible millisecond changes in time between consecutive heart beats (see R-R intervals in the Figure below, left panel), is due to interplay between the sympathetic and parasympathetic (vagal nerve) inputs. Distinct from heart rate, individuals with the same heart rate can have very different HRVs. It is a rough reflection of the autonomic nervous system (ANS) activity, inadequate to say whether a person’s ANS function is abnormal. For more than three decades, heart rate variability (HRV) has been measured and several studies have found an association of low HRV and clinical outcomes, particularly a link with higher all-cause and cardiovascular mortality. There have also been less well established links of low HRV to risk of early cognitive impairment, dementia, mental illness, Type 2 diabetes and substance abuse. An important reminder is that HRV is a surrogate marker without any established cause-and-effect relationship. If you increase your HRV, that doesn’t mean it will improve health outcomes. In fact, there is no hard evidence for that. All that work linking to health outcomes was done with electrocardiogram (ECG) derived HRV. Now, in the era of consumer wearables, this is getting assessed differently, by optical pulse (yes, the lights you see) plethysmography (PPG) or what is called pulse rate variability (PRV). They are not the same, as shown below (right panel) and only concordant when the delay between the ECG and pulse is kept constant, which basically means at rest. I should mention there’s also what I will call MPV, a mattress mechanical movement sensor, a derived heart rate variability, that companies like Eight Sleep use, even further away from directly measuring HRV. HRV has not one uniform measurement but many different types of quantification, such as RMSSD, the magnitude of difference between successive R-R intervals of normal sinus beats (N-N) or SDNN, the standard deviation of NN intervals, both in milliseconds. SDNN is one of the so-called frequency domain HRVs (others are LF, HF, LF/HF). Different wearable sensors use different metics; Apple has relied on SDNN and nearly all of the others use RMSSD, which is generally considered the more accurate metric. There’s also the different length of time measured, such as for a matter of minutes, all day, or an overnight’s sleep. Short measurements are especially problematic since they don’t capture enough of respiratory modulation and other factors that influence HRV. Share Ground Truths How well does HRV correlate with PRV? There are very limited studies, especially independently done. One that is commonly cited was conducted by Air Force researchers in only 13 healthy adults assessing Oura ring 3 and 4, Whoop 4.0, and Garmin Fenix 6 and showed a correlation coefficient of 0.88 to 0.97 and a mean absolute percentage error from 6 to 10%. The correlation is not a perfect 1.0, but there’s at least a fairly high level of correlation. HRV is supposed to increase during the night due to takeover of the parasympathetic nervous system, and higher during deep sleep. A recent example of my 1 week, all day “HRV,” and one during sleep is shown below. As you can see, the N of 1 data are inconsistent for the same days from different sensors (Oura, AppleWatch, Fitbit Air, and Eight Sleep) by patterns, absolute numbers, and comparison with prior days and weeks. The largest study in over 8 million Fitbit users (the old version, not Google Fitbit Air, introduced in May 2026) gives you a sense of the effect of age, sex, and the 2 different main HRV (here PRV) metrics, with RMSSD on the left and SDRR (=SDNN) on the right below. That study, from data collected in 2018, is a major outlier, since all the more recent ones are tiny with respect to sample size. Many of the companies have not had independent evaluation of their HRV, such as Eight Sleep, but have published a low standard error on their website. There are some other published studies on the correlation between HRV and PRV, but they are all small and only in healthy adults. A scoping review emphasized the lack of study in underrepresented individuals, including the aged, people of color (which affects the PPG signal), and individuals who are underweight or obese. Add the typical adult age 60 plus with one or more chronic diseases. For example, one study in over 900 adults found poor correlation of HRV and PRV, non-uniformly underestimated across many chronic diseases (cardiovascular, endocrine, neurological, respiratory, and others), concluding PRV is “an invalid surrogate for HRV.” A recent systematic review of 43 studies comparing HRV and PRV found reasonable pooled absolute standardized error (HRV as gold standard) but only 10 of the studies provided quantitative synthesis in ideal conditions. Their main conclusion was similarly cautious: “PPG-derived HRV [PRV] should not be regarded as universally interchangeable with ECG-derived HRV across all devices, populations, and recording contexts.” Factors Affecting HRV and PRV That gets me to the long list of factors that affect HRV (and PRV) besides the device, the type of measurement (RMSSD, SDNN or others), the person’s signal, the sensor site, the duration of data capture, if weighting by sleep stage is used, how artifact is processed and corrected. And this list is not complete!: Oura puts out data from their community of users (who input data) on what affects their overnight HRV. The factors currently provided are: no alcohol (increase 8%), melatonin (increase 2%, float tank (increase 2%), wine (decrease 4%), and party (decrease 14%) in overnight HRV. Must be some big parties! Subscribe What is a PRV measurement good for? It has been falsely characterized as an index of “autonomic balance” and a specific indicator of stress. A 2018 review of the studies available for HRV and its relationship to stress, not using any of the current wearables, found that stress can lower HRV. But so can many other factors. The non-specificity of the signal, indexed to the table above, is striking. Evidence from a UK Biobank study of over 46,000 participants with actual HRV looked at genetically predicted HRV, a genetic risk score, that failed to show the expected HRV-mortality link, indicating that _HRV is likely not causa_l, but rather a reflection of person’s physiologic state. A review of consumer wearable HRV data from 5 longitudinal studies showed that nighttime PRV was not associated with perceived stress, and surprisingly higher HRV, in the largest cohort (N=717 participants), was correlated with higher stress. An Oura ring cohort of 525 first-year college students found a link between overnight PRV and perceived stress, but that was also seen with resting heart rate, sleep, and respiratory rate. Several very small studies have examined the relationship of HRV and athletic injuries or guiding training with mixed, and predominantly negative results. HRV biofeedback training with paced breathing had no significant effect on reducing stress or raising HRV, as demonstrated with sham controlled trials. When HRV for multiple days showed a decline in conjunction with body temperature, the Oura ring published data for prediction of Covid. The WHOOP company sponsored an observational study, published in 2026, of 30,000 users for 72 weeks, without a control group, that reported reduced alcohol intake (5.8 % points) by self-report. That doesn’t tell us much, and particularly about the merits of HRV for behavioral change. If you use the same device and conditions as longitudinal trends for multiple (at least 2-3) weeks that may be the one way to get something useful from the measurements. Data for overnight sleep with minimal motion and using RMSSD is the best proxy for real HRV. The reason to look at trends rather than any given night is that it more likely represents something, even though you won’t know with certainty what the “it” is. Keep in mind there are no data, no peer-reviewed evidence, to show that HRV fluctuation in-person has any correlation with health outcomes. Share Readiness Scores These are proprietary scores that integrate different metrics for each of the wearables: no algorithms have been disclosed. They are unvalidated against health outcomes. In a review of 14 composite health scores of readiness and recovery, HRV contributed 86% to the scores, followed by reading heart rate (79%), physical activity and sleep duration (both at 71%). That review noted the substantial variability in measurement protools and lack of standardization. Sleep staging is notoriously inconsistent and inaccurate by these sensors, which adds further to the HRV uncertainties for what the scores, which use sleep stage data, mean. Only resting heart rate has been shown consistently across devices to be extremely accurate. I’ve made a Table to summarize what we know about which metrics are included, the scores, any peer-reviewed studies that compared the readiness score with health outcomes, and the corresponding (if any, NA-not available) citation. You will note that some companies do not use the term “readiness," such as WHOOP for recovery, and Garmin, which has 2 different scores, one of which is Body Battery. Eight Sleep uses the term “Fitness Score.” They all include HRV; Apple includes a new metric they call “Recovery HRV” which among other components uses 7-days of sleep, but it is unclear what this means or how it is differentiated from other scores (there are clearly no data for outcomes). We have no knowledge of how the different components are weighted or whether any of these scores are better than resting heart rate, HRV alone, physical activity, or any other single metric. Since none of these are standardized, they are not interchangeable, so if you get a 90 for Oura that has no relationship to a 90 on a Google Fitbit Air. Notably, the company can update its algorithm for readiness score at any point without notification to device users. Without any useful evidence of actionability for these scores or established relationship with health outcomes, it is hard to make a case for their value. At the Apple recent announcement they showed their Readiness score (0-10) on the watch (Figure below) but there are no published data on this score, not even on their website. It’s available only on their new Watch Series 12 or Ultra 4 [of course, ;-)]. That exemplifies the problems with these scores, lack of data and evidence for being meaningful to promote health. Perhaps the best study (which isn’t saying much) is the WHOOP Recovery for golfer performance, because it did correlate with an objective outcome, even though there was no control group and the authors were all from the company. Among the 389 pro golfers, an absolute 10-per cent point increase in Recovery score was associated with about 0.5 fewer strokes per round. But that’s hardly a health outcome! WHOOP is also conducting a study in over 2,700 runners to see if their recovery score will be linked to less injuries and improved performance, but that is not yet published and has no control group or randomization. Putting This in Context For two decades I’ve been enthusiastic about the potential for digital health and particularly wearable biosensors. Over the years, we’ve seen some great progress for their ability to promote physical activity and accurately detect atrial fibrillation (the first FDA cleared deep learning AI for consumers). That work was the subject of rigorous research. But there are holes in the data and evidence for other metrics. One notable one is the “VO2 max” story that I wrote about earlier this year. At that time many subscribers asked me to cover heart rate variability, which I finally got to here. When I dived into the research and publication for HRV and readiness scores, I expected to find at least some that were of high quality and demonstrated their utility by linkage to health outcomes. To my surprise, I found none. The wearable sensor measurements for HRV (PRV) are, for the most part, accurate, but that validation work has only been done in small studies of healthy adults and does not take into account the long list of factors, from the device, software side, and the user side, that affect HRV measurements. Moreover, this metric chiefly relies on optical sensing and, as we have learned for heart rate PPG sensing, may be less accurate in people of color. Keep in mind that all of the health outcome association evidence comes from ECG-derived HRV; none are from wearable sensor data. I will repeat the key point: there's no peer-reviewed evidence to show that in-person HRV fluctuation—or efforts to raise your HRV— has any correlation with health outcomes. For those of you who look at your HRV on awakening, or even 2+ week trends of it being low, I hope this context helps to relieve any anxiety. Yes, low HRV (not PRV) has been shown to increase risk of some diseases as summarized above. But efforts to raise your HRV—a surrogate metric— has not been established for improving any health outcomes. HRV does not have any evidence of causality (the genetic evidence actually goes against this possibility). In this summary, I have not included data for other wearable sensors such as Polar, Samsung, Withings, Suunto, Amazfit, Coros, Ultrahuman, or additional mattress sensors. These are beyond my first-hand experience, and as far as I know from my in-depth review none have any peer-reviewed published data that differ from the 6 sensors I’ve reviewed here. From the points I’ve gone over above, we’re not ready for readiness scores. Besides being proprietary, they are predominantly based on metrics that have their own issues. It’s compounding the problem, like building a house without a solid foundation that has never undergone a rigorous inspection, and then selling it. Like I mentioned for PRV, you can look at trends over weeks rather than any single day, to get a handle, but even that may not be helpful. There’s simply no evidence that these scores meaningfully relate to health outcomes. I’d emphasize they might, but that requires doing prospective or randomized studies to prove it. None exist. There’s great promise for HRV/PRV utility_. For example**,**_ Prof Maiken Nedergaard, who discovered the brain glymphatics that are essential in eliminating metabolic waste products from the brain during sleep, has posited that HRV could be a non-invasive marker for neuromodulator oscillations, brain-body regulatory circuits, and brain clearance. That would be extremely useful, but like everything else on HRV and readiness scores it requires solid research and validation. The lay media isn’t helping much to get the story straight. Earlier this year The Economist published a piece entitled “The most useful indicator of your overall health” which ordained HRV as an “accumulated stress score.” That’s akin to the false assertion about VO2max: “V02 max is the singular most powerful marker for longevity.” As I’ve summarized here, that is not established. The fact is that so many things can lower HRV, including physical exercise (especially an intense workout), reduced sleep quality, stress, the list above, no less the device, signal, and software. Whatever fluctuations observed have not been correlated with any health outcome. Sadly, “datamaxxers” are widely using HRV and readiness scores that have never been validated to mean anything. We already know that for some people using the sensors for sleep metrics, it can induce “orthosomnia,” an obsession to get high sleep quality, with associated high levels of anxiety. In an experiment done by a company to promote sleep quality for its employees, “For those employees who did use the trackers, many reported feeling perfectly rested until their tracker told them they had had a terrible night. Others were told that they had slept like a baby when they had actually been lying awake worrying about the quality of their sleep. “ The same problem can result from preoccupation with HRV or readiness scores, with anxiety that would lead to further reduction in both. It you are using a wearable like >100 million American adults, it’s OK to look at these data, but contextualized with the major caveats reviewed here. If you are one to require evidence that HRV or readiness scores are linked to health outcomes, you may not even want to look. The companies make it hard to turn them off! Let me end with the companies that make and sell wearables. Apple’s doubling down on HRV (24-fold more reporting and heart rate very 5 seconds) and introduction of a Readiness score tells us that consumers have bought into these metrics and they are joining the club. However, all of this is occurring with a backdrop of tens millions of users, claims about the data that are not backed up by adequate evidence, marketing way out in front of whatever limited data exists, and not being transparent about their readiness score algorithms. The companies can well afford to do the research that is needed to connect these metrics with health outcomes show, once and for all, that increasing HRV or using readiness scores promotes our health. If they believed and invested in the products they are selling, we’d not be in this position of not knowing. That’s essentially where we are with HRV and readiness scores. Perhaps someday this will change and we’ll have good reason to embrace them. NB: I wrote this post. No AI. I have no conflicts of interest with any of its content. Loading... Ground Truths has 215,000 subscribers from every US state and 214 countries. There are over 300,000 followers of Ground Truths so more than 90,000 folks who can easily convert to be free subscribers. Your subscription to these free essays and podcasts makes my work in putting them together worthwhile. If you’re not a subscriber, please join! If you found this interesting PLEASE share it! Share Ground Truths The proceeds from all voluntary paid subscriptions go to support our summer internship program. It enabled us to accept and support a record number of 62 summer interns that joined us in 2026! These are high school, college and medical students selected from thousands of applicants. We couldn’t do this expanded program without the funds coming in through Ground Truths. Thank you!

Sep 19 • 10:05 AM EDT • Technology • erictopol.substack.com

Robots and AI find a hidden reaction inside 135-year-old chemistry

Chemical reactions are often written as simple equations: starting materials go in, and a product comes out. In reality, the samesubstrates can follow many different pathways depending on their concentrations, temperature, catalysts, and other conditions. Exploring all of these possibilities by hand is practically impossible. Now, advances in laboratory automation and chemical artificial intelligence are allowing researchers to systematically map this vast “reaction hyperspace”—and uncover chemistry that may have remained hidden even in reactions studied for more than a century. To address this question, scientists led by Prof. Bartosz A. GRZYBOWSKI, Director of the Center for Algorithmic and Robotized Synthesis within the Institute for Basic Science (IBS) used an automated robotic platform to explore 960 different sets of conditions for the Biginelli reaction, a classic multicomponent reaction first reported in 1891.

Sep 15 • 8:00 AM EDT • Science • eurekalert.org

Stony Brook Researchers David Miloschewsky and Supartha Podder Resolve a Decades-Old Puzzle About the Limits of Quantum Computing

Finding a path through a maze is hard, even for quantum algorithms. A quantum computer can find the exit of a certain kind of maze far faster than a classical computer. But can it also give you directions? David Miloschewsky and Supartha Podder of Stony Brook University’s Department of Computer Science have proven that finding

Oct 22 • 1:50 PM EDT • Science • news.stonybrook.edu

Cyborg Politics 3.0: AI and the Algorithms of Unpeace

How AI is transforming politics, warfare, and peacemaking, creating a new era of cyborg power and algorithmic "unpeace."

Oct 8 • 11:39 AM EDT • Politics • worldpeacefoundation.org

AI trained to 'think' like human pathologists may be better at spotting cancer

Researchers trained AI on how expert pathologists look for signs of cancer in patient samples, improving the algorithm's effectiveness.

Sep 12 • 8:00 AM EDT • Science • livescience.com

This 'adversarial' pattern can prevent surveillance cameras from detecting you

A security researcher has designed an algorithm that can create computer-generated patterns capable of hiding people, faces, and vehicles from detection by surveillance cameras.

Aug 9 • 10:00 AM EDT • Technology • techcrunch.com

Efzimfotase alfa demonstrated improvements in bone health in treatment-naïve pediatric patients with hypophosphatasia achieving median difference of 1.67 vs placebo in RGI-C Score at week 25 in MULBERRY Phase III trial

Positive results from the MULBERRY Phase III trial showed that efzimfotase alfa (ALXN1850), an investigational enzyme replacement therapy, demonstrated a statistically significant and clinically meaningful improvement in bone health in children (2 to

Jun 28 • 3:45 PM EDT • Health • astrazeneca-us.com

WVU Medicine to make "transformative contribution" to health services in Monongalia County

Announcement set for Tuesday morning in Morgantown.

Jul 27 • 5:39 PM EDT • Health • wvmetronews.com

John Mayer explains why algorithms can't replace great radio

Give John Mayer 12 minutes and you might find yourself hooked. A mere 720 seconds to experience “Life with John Mayer,” his distinctive SiriusXM channel heading toward its third anniversary.

Aug 21 • 10:30 AM EDT • Entertainment • centraloregondaily.com

Scientists and algorithms: Finding the right balance in modern drug development

The future of drug development is not scientists versus algorithms. It is scientists and algorithms working together.

Sep 9 • 4:30 AM EDT • Science • pharmaphorum.com

Long Reads for the Long Weekend

(Welcome to the Entertainment Strategy Guy, a newsletter on the entertainment industry and business strategy. I write a weekly Streaming Ratings Report and a bi-weekly strategy column, along with occasional deep dives into other topics, like today’s article. Please subscribe.) Happy Fourth of July weekend! As has been a tradition of mine going back to the beginning of the EntStrategyGuy website, I’m sharing my favorite long reads of the last year, like I did in 2019, 2020, 2021, 2024, and 2025. Let’s dive right in! Subscribe “Different Prices for the Same Ride: How Uber and Lyft Use AI to Get More Money Out of You“ by Derek Kravitz in Consumer Reports I don’t think surveillance pricing, like the kind described in this brilliant Consumer Reports article, is long for the world—it’s toxically unpopular—but we’ll see if either party takes on this issue. If I ran a political party focused on affordability, I would focus on a few pro-consumer and pro-market policies: Banning non-compete agreements. Ending surveillance pricing Eliminating junk fees (read more here...) Ensuring “right to repair” for consumer (and military!) products Capping excessive app store fees Stopping digital/algorithmic price-fixing tools. Preventing automatic subscription price increases. And more. If you want to fight inflation, there are a lot of great ways to do it quickly. Speaking of higher prices... “Secret Documents Show Pepsi and Walmart Colluded to Raise Food Prices Across the Economy“ by Matt Stoller in BIG Again, I think a political party should focus on lowering prices for consumers. And deals like this—Walmart making a deal/forcing Pepsi to offer the lowest prices at its stores and nowhere else—hypothetically lower prices at Walmart...but raise them everywhere else. “15 DUIs, still driving: California’s failure to take repeat drunk drivers off the road“ by Robert Lewis and Lauren Hepler in CalMatters CalMatters does terrific reporting in California, something the Golden State desperately needs more of, and this article is a great example of that. “PugLips: The next insanely popular thing you’ve never heard of…” by Simon Carless in Game Discover Co I thought that this parody article (and yes, it’s a parody, but I was definitely unsure at first) was hilarious and totally nailed how much tech/business journalism feels these days. Actually, three of my favorite articles this year were on hype in the video game industry, including Georg Zoller’s “The Video Game Industries Very Dark Night”—which I linked to recently and wrote a follow-up here—and Owen Mahoney’s “Derek Zoolander, Videogame Exec”. In particular, I like Mahoney’s focus on user experience as a guide to what trends will actually grab hold of customers. “The Matrix Lies“ by Evan Shapiro in Media War & Peace Evan Shapiro’s first person account of his experience with AI was hilarious. I think LLMs capabilities have significantly grown from last summer, and yet...my editor/researcher just had an abysmal experience using an LLM to do a quite basic task (which our LLM normally does each week) and the LLM made a mistake on every single data entry...along with seven different mistakes affecting twelve of the twenty or so entries, including mistakes that are explicitly spelled out in the prompt. That’s a brutal failure rate. Again, if you use AI/LLM for data collection, you need to take a lot of extra steps to verify and check the data. “Ghosts in the Balcony: A Cross-Country Trip to 58 Theaters Fighting to Survive“ by Matthew Frank in The Ankler I thought Matthew Frank’s pitch to head across America to visit theaters sounded insane aggressive at the time—at least it felt that way to someone a decade older than him; man, to be in your twenties again, am I right?—but I knew the result would be great. And it was, a terrific feature by a great young writer. Also, I loved Matthew’s recent article on who’s getting the precious few entry level jobs in Hollywood. (Hint: nepotism.) “Disney erased FiveThirtyEight“ and “Did Las Vegas get too greedy?” by Nate Silver in Silver Bulletin I love media history and analysis, and there’s no one better to get the history of FiveThirtyEight than from Nate Silver, who wrote about his personal experience in one of my favorite pieces from this year. I also loved Nate’s breakdown of what’s causing Vegas’ woes, but I’ll offer one criticism, summarized as “Might I suggest antitrust?” Many centrists/center-right/libertarian thinkers (Nate is self-described as the latter) often don’t consider consolidation or antitrust as factors in policy issues like housing, America’s military readiness, or, in this case, Las Vegas. Even if libertarians want to debunk antitrust/consolidation concerns, I’d love to read that debunking. In this case, Vegas is incredibly consolidated (though it also has been for a long time) and I’d have loved to read Nate at least contending with this factor, since he addresses almost every other potential factor for Vegas’ decline. “Why Microsoft’s Carbon Removal Pullback Is Such a Big Deal“ by Robinson Meyer in Heatmap News I’m a big supporter of carbon removal efforts, but this nascent industry faces a lot of headwinds, including this big move most recently. I think it’s clear that solar panels and batteries should be able to get the world to its carbon footprint goals, but effective carbon renewal remains necessary to prevent the worst impacts of global warming. “AI is killing the cheap smartphone“ by David Oks LLMs and AI will impact the world in ways we’re not ready for, and already, third world countries are seeing the prices of cheap cellphones skyrocket due to the cost of chips skyrocketing. This article by David Oks is a terrific breakdown. “100 Days of Madness: Netflix, Paramount, Warner and the Future of Hollywood“ by Wade Major at Hollywood Heretic Wade Major dove deep into Hollywood/streaming’s financials in this must read piece. Not that I agree with everything in this article (read my take on Warner Bros./Paramount here) but the analysis is top notch. I think many people discount how quality thinking, regardless of the argument/conclusion, will make you a better thinker. “Erased by the UFC: Frank Shamrock, The MMA GOAT of the 20th Century“ by Nate Wilcox in The MMA Draw Newsletter Growing up playing the UFC fighting game on the Dreamcast, Frank Shamrock was my favorite MMA fighter, and this history/tribute to him was great. Other Fun Reads “The Hardest Part Of History To Tell Is How It Felt“ by Craig Fehrman at Defector “Unmasking the Sea Star Killer“ by Craig Welch at bioGraphic “What Could Save the Industry? Fin-Syn“ by Richard Rushfield in The Ankler Alex Rollins Berg in Underexposed on movie bumpers, pillow shots, Disney trash cinema, cult films, and Casablanca. David H. Montgomery in YouGov’s The Surveyor on punctuation marks, condiments, dinosaurs, and comic strips and kids media. “How Medium finally pivoted its way to profitability“ and “Why the best journalists on YouTube are all former Vox employees“ by Simon Owens in Simon Owens’s Media Newsletter “It’s Not Just You, Netflix Shows Have Gotten Slightly Shorter: Here’s What the Data Says“ by Kasey Moore at What’s on Netflix “Numlock Awards: How Diane Warren became the biggest loser in Oscar history“ by Nathaniel Rakich and “Numlock Awards: The Oscar Bait era is over. The Oscar Chum era is here.” by Walt Hickey & Michael Domanico at Numlock Awards

Jul 3 • 2:38 PM EDT • Entertainment • entertainment.substack.com

FDA clears AI-enabled CCTA plaque analysis tool from Keya Medical

The company now has multiple FDA-cleared algorithms for evaluating CCTA results.

Jul 2 • 3:16 PM EDT • Business • cardiovascularbusiness.com

Cornish-Adebiyi v. Caesars Entertainment: The Third Circuit Deals Algorithmic-Pricing, Hub-and-Spoke Theory Another Hand

Antitrust scrutiny of information exchanges and algorithmic pricing tools has intensified recently—from public enforcers and private plaintiffs alike—as companies across industries increasingly...

Aug 4 • 8:00 PM EDT • Entertainment • jdsupra.com

Algorithm allows cancer patients to avoid CT scans for pulmonary embolism

There has been increased interest in finding alternative methods to diagnose pulmonary embolism in this patient population.

Jul 13 • 10:51 PM EDT • Business • radiologybusiness.com

McDonald’s faces antitrust suit over AI-driven pricing

The lawsuit alleges the company violated antitrust law by working with independent franchisees to fix prices using algorithms.

Oct 6 • 1:13 PM EDT • Business • chicagobusiness.com

Chinese scientists say they spotted possible Israeli electronic warfare units

PLA scientists publish algorithm used to estimate sources of GPS spoofing around Israel using flight data.

Oct 14 • 9:00 PM EDT • Science • scmp.com

Mental health workers say algorithmic triage is hurting patients

Therapists, clinicians, academic experts and advocates say hasty integration of AI is eroding patients’ access to timely mental health care.

Aug 21 • 4:45 AM EDT • Health • wisconsinexaminer.com

Amid Questions Over Mojtaba Khamenei's Health, An Iran President-Supreme Leader Meet

Pezeshkian, who took office in July 2024, had said in early May that he met with Khamenei, also without specifying the date, but the supreme leader's office did not issue any statements at the time.

Aug 9 • 2:39 PM EDT • Health • ndtv.com

AI for breast cancer risk prediction goes DTC, regulatory cloud over Utah sandbox, and AI psychosis

In this edition of STAT's Health Tech: The story of a startup taking its algorithm for breast cancer risk prediction directly to patients, and more.

Oct 8 • 11:43 AM EDT • Health • statnews.com

Is Fury v Joshua about meritocratic sporting excellence or just algorithmic celebrity power?

This long-awaited fight is happening exactly as intended: at the sweet spot where commercial potential meets a genuine absence of jeopardy

Oct 8 • 3:00 AM EDT • Sports • theguardian.com

Algorithms of Hate: How AI Is Rewriting Identity Politics

Identity-based hatred has evolved into a profitable digital industry driven by social media algorithms, political polarization

Aug 2 • 11:22 AM EDT • Politics • fairobserver.com

AI Is Great At Science But Not At Being A Scientist. This Professor Explains Why

In a guest post published on Anthropic's research site on Thursday, physicist Matthew Schwartz argues that researchers get the most from AI by giving it problems suited to its strengths.

Oct 2 • 1:39 AM EDT • Science • ndtvprofit.com

WhatsApp Launches Web Browser-Based Audio And Video Calling Feature

According to a WhatsApp blog post, the new web calling feature supports both one-on-one and group calls and includes access to screen sharing, reactions, and a dedicated calls tab for history and favourites.

Jul 28 • 2:54 PM EDT • Technology • ndtv.com

This era’s public health threat is digital (Opinion)

We addressed car safety through design standards, such as airbags. Policymakers could treat infinite scroll, autoplay, streaks, algorithmic recommendations as design choices potentially subject to similar public policies.

Oct 12 • 6:00 PM EDT • Health • denverpost.com

The new kings of the news business are in your inbox

Ryan Broderick has watched digital media companies flame out – beholden to a single source of funding or a fickle algorithm.

Jun 27 • 8:00 PM EDT • Business • spokesman.com

People-Powered Research With a Twist

Verify the work of an algorithm to unlock the secrets of spiral galaxies! Anyone with a smartphone or laptop can do this project.

Jun 23 • 9:00 AM EDT • Science • science.nasa.gov

Kaiser used an algorithm — not clinicians — to triage mental health patients, a union alleges

A therapists' union alleges Kaiser's e-visit tool screens mental health patients and issues care recommendations without real-time clinician review, which the union says violates state law.

Jul 29 • 8:00 AM EDT • Health • calmatters.org

HHS spins a tale of a would-be content creator child

The next DSM and the algorithm, an unexpected discovery on alpha-gal syndrome, and more health news from Morning Rounds

Sep 2 • 7:59 AM EDT • Health • statnews.com

Dealroom.co | The algorithm of love: Match Group's CEO on the new science of dating

Key takeaways from the In Good Company podcast hosted by Nicolai…

Oct 16 • 7:18 AM EDT • Science • app.dealroom.co

Buy A Shop, Earn Rent, Retire Early? We Did The Math Behind The Dream

Buying a shop can require a large upfront investment, and the rent may not be enough to cover the loan EMI.

Oct 17 • 3:45 AM EDT • Business • ndtv.com

The Beauty Of Settled Science

Commenters convinced me I made a subtle mis-step in my framing of Psychology Research Is Mostly Fine. My framing emphasized that social priming studies had failed to replicate, but that many other subfields of psychology hadn’t. This naturally led commenters to point to failed replications in other subfields. Although none of these were as bad as social priming, few or none were entirely problem-free. Instead of emphasizing the difference between social priming and other subfields, I should have emphasized the difference between settled science and novel research. Here are some of my favorite psychology findings: The brain is sculpted by reinforcement learning, and a key driver of this learning is a prediction-error-based algorithm located in the nucleus accumbens and implemented through dopamine. Psychedelic drugs exert their effects through 5-HT2A receptors, pointing to a role for these receptors in some sort of construct like the balance between priors and new evidence. Certain cognitive functions are localized to certain brain areas (for example, impulse control to the frontal lobes; linguistic structure to Broca’s area, linguistic meaning to Wernicke’s area. But injure one of these areas, or render a certain function useless (eg a blind person having no more use for their visual cortex), and these mappings can gradually shift - suggesting that neurons are general-purpose compute whose specialization is a contingent fact about convenience and efficiency rather than a necessity. Most personality traits and general capabilities are about 50% genetic and 50% random, with little role for family environment or childhood history, with a few notable exceptions like partisan preference. Recent research complicates this story, but the further you go from this basic picture, the gnarlier the chains of indirect causation you require. Neurons fire together in “brain waves”, whose exact frequency corresponds with the character of consciousness; These divide neurons into natural subpopulations and linked graphs. The gamma rhythm corresponds surprisingly well to items in conscious awareness. GABA receptors seem to be involved in something like relaxation. By hitting different GABA receptors in different ways, you can modulate the exact type of relaxation you produce. Hitting GABA-A directly is too powerful and can cause dangerous levels of sedation, but allosteric modulation of GABA is an incredibly fruitful strategy that underlies drugs from benzos to barbituates to alcohol. If you know your GABA-A subunits, you can target specific subtypes of relaxation; for example, Ambien hits GABA-A1 and induces sleep. Memory divides somewhat fuzzily into bins of immediate-sensory, short-term, and long-term, with separate retrieval and storage functions, and separate systems for declarative vs. procedural learning. These can be harmed selectively, usually by hippocampal injury, so that some patients can remember their life stories but not form new long-term memories, or learn new skills like bike-riding but not new facts. The exact substrate of memory is unclear but seems to involve new synaptic connections between neurons in at least some way. The ability of modern psychology and psychiatry to treat mental illness is real but limited. SSRIs work for half or slightly fewer than half of patients; antipsychotics work with extreme side effects; benzodiazpines work but produce addiction and tolerance in many (not all) people. Most supplements are stuck in Poor Quality Study Replication Crisis Hell, but melatonin and l-theanine stand out as especially promising. Psychotherapy works with moderate effect size, but (with a few exceptions) seems to be more about the relationship with the provider than the details of the therapy itself. Mental hospitalization can sometimes defuse immediate crises but work less well than mental hospitals would like, with some studies showing overall negative effects. Schizophrenia is mostly genetic, but risk also increases with prenatal infection (especially rubella) and obstetric complications; there is suggestive evidence for roles of social stress and drug use (with psychedelics and marijuana making things worse, and nicotine if anything making things better). Bipolar and schizophrenia are so closely linked on a pathophysiological level that it seems like they might be the same disease; on the clinical level, they do seem to follow two separate courses/prognoses but can sometimes surprise clinicians by eg someone who seemed condemned to permanent schizophrenia unexpectedly recovering. The brain’s native reinforcement learning algorithms cash out in the phenomena of classical and operant conditioning; different reinforcement schedules reinforce at different levels, which can be exploited by bad actors to make especially compelling products (eg slot machines). None of these are immune to doubt. A few are speculative; one or two are genuinely unorthodox. But they’re all a step above one p = 0.049 paper that might not replicate. If they get overturned, they’ll be overturned by the same sort of genuine scientific shift that replaced Ptolemy with Copernicus or Lamarck with Darwin, not some sort of sordid discovery about someone entering random numbers into a spreadsheet. In 2008, Eliezer Yudkowsky wrote about The Beauty Of Settled Science: If you only care about scientific issues that are controversial, you will end up with a head stuffed full of garbage. The media thinks that only the cutting edge of science is worth reporting on. How often do you see headlines like “General Relativity still governing planetary orbits” or “Phlogiston theory remains false”? So, by the time anything is solid science, it is no longer a breaking headline. “Newsworthy” science is often based on the thinnest of evidence and wrong half the time—if it were not on the uttermost fringes of the scientific frontier, it would not be breaking news. Scientific controversies are problems so difficult that even people who’ve spent years mastering the field can still fool themselves. That’s what makes for the heated arguments that attract all the media attention. Worse, if you aren’t in the field and part of the game, controversies aren’t even fun. Don’t be embarrassed to read elementary science textbooks, either. If you want to pretend to be sophisticated, go find a play to sneer at. If you just want to have fun, remember that simplicity is at the core of scientific beauty [...] A steady diet of science news is bad for you: You are what you eat, and if you eat only science reporting on fluid situations, without a solid textbook now and then, your brain will turn to liquid. Psychology isn’t centrally the new paper in Annals Of Recently-Breaking News saying that an n=5 study showing that large portion sizes increase support for terrorism. Psychology is centrally ideas like brain waves, GABA receptors, reinforcement learning, twin studies, and connectionism. People who think differently are worrying too much about BREAKING NEWS!!!, and should stop. The key distinction isn’t “recent research in social psychology” vs. “recent research in cognitive/bio/behavioral psychology”, as my essay last week unfortunately implied. It’s recent research in general vs. the deposit of settled science, battle-tested hypotheses, and carefully-earmarked speculation. And part of the reason social priming in particular was such a disaster was that it defied that heuristic, building itself up over hundreds of studies until it approached or even entered the pantheon of settled science - before collapsing (5-HTTLPR was also sort of like this). This isn’t a claim that settled science is always right - Freud was “settled science” for a long time - just that there’s an intuitive distinction between the level of credence you should have in the existence of power posing vs. the existence of Broca’s Area. (I compounded this error by calling the last post “Psychology Research Is Mostly Fine”, which suggested I was talking about the new research rather than the settled science. I actually meant to distinguish between research-psychology-the-field vs. clinical-psychology-aka-therapy, but this framing unintentionally pushed against the point I wanted to make) Some commenters on the previous article understood this point, but challenged its weight. It’s very cool, they said, to know how many neurons are in which brain lobe, but the main way that Psychology affected their lives was through bad clickbait studies used to buttress dumb policies; some p = 0.049 study says that exposure to microaggressions makes minorities’ brains shrivel into raisins, and now their company has to implement punitive DEI or be sued for hazardous work conditions. I won’t dispute this as an accurate description of how the sausage gets made, but it’s worth examining the trains of thought that connect this observation to a campaign against psychology in general: The dumb woke psychology was wrong and bad, and I don’t have good heuristics for distinguishing dumb woke psychology from other psychology, so I’m going to assume it’s all wrong and bad. The dumb woke psychology was wrong and bad; it seems useful to punish/discredit psychology as a whole to assist my campaign against it, so for propaganda purposes I will try to discredit psychology as a whole. To people in the first camp, my response is: come on, your heuristics aren’t that bad. “Microaggressions makes minorities’ brains shrivel up like raisins” is in the woke category; “the hippocampal theta rhythm seems to be associated with both memory and pathfinding, suggesting an unexpected yet surprisingly compelling relationship between the two” is in the not-woke category, don’t play dumb here. To people in the second camp, my response is: deliberately exaggerated propaganda campaigns are bad and this is a confession, not an excuse. Inability to view science through anything but a political lens was what got us into this problem; doubling down on it is a temporary solution but a recipe for permanent disaster. If you pronounce entire fields of science to be acceptable collateral damage in your campaign against bad political actors, you will alienate not just scientists but patients, clinicians, and general lovers of truth. Instead, you can say the thing you mean - that woke partisans shouldn’t try to occupy and exploit the otherwise-noble science of psychology for their own political gain - and wage the same campaign with a halo rather than a comic-book-villain goatee. From the font of oracular wisdom that is Weird Sun Twitter: [ Vessel Of Spirit@VesselOfSpirit i love you all conditionally but i love the good part of you unconditionally. even if it becomes a very small part 2:11 AM · Jan 22, 2015 1 Reply · 5 Reposts · 29 Likes ](https://x.com/VesselOfSpirit/status/558084551772471298) This is how I feel about psychology1, too, but with a touch more optimism. The good part of psychology is enormous, a hoard of endless treasures. You just have to look past the headlines! 1 To play devil’s advocate - should one always be so magnamious? Astrology? Phrenology? These seem different in that their entire orientation was flawed from the start; they didn’t simply fail to find the correct constellation-personality connections, the whole project of searching for connections between constellations and personality was doomed from the start. More difficult might be asking the same question about education research, which is notoriously bad. I don’t know enough about education research to know if there’s some settled science of correct and enlightening results lurking in the background beyond the day-to-day froth of poorly-conducted and politically-motivated studies. I think if it’s 90% bad, 10% good, it’s correct to call it bad as a compact way of communicating a useful truth; I think psychology is better than that, but admit it’s a tough judgment call.

Aug 4 • 5:33 PM EDT • Science • astralcodexten.com

Scientists Say They Can Now Pinpoint Where Big Quakes Might Hit

A GPS-based algorithm is used to identify where stress has been building up.

Sep 9 • 8:20 AM EDT • Science • gizmodo.com

New Quantum Algorithm Targets Bottleneck in Fluid-Flow Modeling

Oak Ridge National Laboratory researchers developed LuGo, a quantum algorithm that reduced quantum gate requirements by more than 95%.

Sep 30 • 4:23 AM EDT • Science • thequantuminsider.com

NVIDIA Unveils "Preset C "DLSS Frame Generation with up to 35% Faster Generation

NVIDIA has updated its DLSS Frame Generation algorithm to be faster and more efficient. In the latest NVIDIA App beta update, rolling out today, the company introduced "Preset C," the fastest Performance Mode for DLSS Frame Generation. NVIDIA demonstrates that this mode can make frame generation...

Oct 13 • 9:06 AM EDT • Technology • techpowerup.com

The New Roku Update Is Terrible And Everyone Hates It

Stop fucking things up just to shove more ads and algorithm nonsense into my face

Jul 10 • 4:48 PM EDT • Technology • kotaku.com

Mental Health Workers Say Algorithmic Triage Is Hurting Patients

Workers and policymakers are pushing for safeguards around AI’s use in health care through collective bargaining and legislation.

Aug 27 • 5:15 AM EDT • Health • prospect.org

Epic's mortality model, and Omada's future products

In this edition of STAT Health Tech: What Epic's mortality prediction algorithm tells us about AI in geriatrics, Omada's expansion plans, and more.

Sep 22 • 9:15 AM EDT • Health • statnews.com