Podcasts about alphafold

The business world is obsessed with who has the biggest AI model, the fastest chips, and the most impressive benchmarks. But the real question shaping the next decade of technology is not about computing power. It is about trust. Meta recently made headlines when Mark Zuckerberg published a 6,500-word manifesto outlining his vision for democratizing artificial intelligence, and at the same time announced plans to spend up to $145 billion on data centers. Meanwhile, LinkedIn is grappling with an AI content crisis that reveals just how confused platforms are about the role of artificial intelligence in human communication. These stories are connected, and understanding them through a category design lens changes everything about how you see them. This is just some of the topics that Pirates Christopher Lochhead, Eddie Yoon and Bri Clark discuss on this episode of Pirate Street Journal. Each week, the Category Pirates pick three headlines worth paying attention to and break down the category underneath. You're listening to Christopher Lochhead: Follow Your Different. We are the real dialogue podcast for people with a different mind. So get your mind in a different place, and hey ho, let's go.   Zuckerberg Meta’s Big Vision Has a Bigger Problem Zuckerberg’s manifesto is genuinely compelling as a piece of category design. He frames a problem, presents a new vision for the future, and positions Meta as the company that will put artificial intelligence into the hands of everyone. That is textbook category design thinking, and directionally, much of what he writes makes a great deal of sense. The problem is that the person delivering this vision is Zuckerberg himself. Meta’s business model is built on advertising, and advertising gets more profitable the more intimately the platform knows you. No matter how inspiring the language in a 6,500-word essay, the underlying give-to-get dynamic remains deeply unfavorable to the user, and a history of privacy scandals makes it nearly impossible to take the trust language seriously.   The AI Abundance Argument and Why It Falls Short One of the more attractive ideas in Zuckerberg’s manifesto is the concept of AI abundance, the idea that everyone should have access to powerful artificial intelligence tools for free or at very low cost. On the surface, this sounds generous and even visionary. But abundance without accountability is not a category strategy. It is a data acquisition strategy dressed up in philosophical language. Compare this to what companies like Google have done with moonshot projects such as Waymo and AlphaFold. These initiatives demonstrate a give-to-get dynamic that at least gestures toward broader human benefit. Meta has consistently struggled to articulate what the consumer actually receives beyond the product itself. The metaverse is the clearest example of a massive investment that never produced a meaningful answer to the question of what it was for.   LinkedIn’s AI Slop Problem and the Scarlet Letter Trap LinkedIn is now reporting that 41% of long-form posts on the platform are entirely AI generated, and the company has introduced a button allowing users to flag content they suspect was written by artificial intelligence. On the surface this sounds like a reasonable response to a real problem. In practice, it is a dangerous overreaction that punishes legitimate creators alongside lazy ones. The future of creating everything is vibe creating, meaning humans working in genuine collaboration with AI to produce ideas, arguments, and content that reflect real thought and real points of view. Labeling that output as synthetic or slop is the equivalent of telling someone their spreadsheet contains synthetic math. If a piece of content is unhelpful or obvious, the solution is an unfollow button, not an AI scarlet letter that penalizes the tool rather than the thinking behind it. To hear about all the topics in this week's The Pirate Street Journal, download and listen to this episode. You can also read more Pirate Street Journal entries in the Category Pirates newsletter.   We hope you enjoyed this episode of Christopher Lochhead: Follow Your Different™! Christopher loves hearing from his listeners. Feel free to email him, connect on Facebook, X (formerly Twitter), LinkedIn, and subscribe on Apple Podcast / Spotify!

In the wake of OpenAI's announcement that its products caused a "cyber incident," Sam Altman is now claiming that we're already in the Singularity. And some media outlets are only too happy to help him advance that narrative! Alex and Emily tear up the AI boosters' latest claims of mathy maths combusting into consciousness.References:Business Insider: "Sam Altman says we are in the singularity: 'This is the moment'"Also referenced: Altman on the Relentless podcastWall Street Journal: "How the Futuristic Hack by Rogue OpenAI Models Unfolded"Also referenced: OpenAI and Anthropic press releasesMacAskill op ed in the Guardian: "Could AI be conscious?"Also referenced: Emily's Bluesky threadFresh AI Hell:Google kills AlphaFold"Former executive accuses Mayo of cutting corners on AI research"Amazon wants to use Twitch streams to train gen AI"Meta used AI to target workers with medical conditions for layoffs, lawsuit claims"Meta contaminates water in Cheyenne"Humanoid robot teaching assistant"AI companies buying and destroying antique booksPalate cleanser: Mathematicians clap backCheck out future streams on Twitch. Meanwhile, send us any AI Hell you see.Find our book The AI Con here, and MAIHT3k merch here.Subscribe to our newsletter via Buttondown.Follow us!EmilyBluesky: emilymbender.bsky.socialMastodon: dair-community.social/@EmilyMBenderAlexBluesky: alexhanna.bsky.socialMastodon: dair-community.social/@alexTwitter: @alexhannaMusic by Toby Menon.Artwork by Naomi Pleasure-Park. Production by Ozzy Llinas Goodman.

This and all episodes at: https://aiandyou.net/ . What is AI doing to medicine? Specifically, in molecular biology, and protein folding: figuring out the 3-D structure of proteins. Remember DeepMind's AlphaFold and the announcement of it predicting 200 million protein structures? I'm talking with one of the top researchers in the world, Professor Mohammed AlQuraishi of Columbia University's Department of Systems Biology, and a member of their Program for Mathematical Genomics. He heads a lab focused on biological perspectives at the molecular and systems levels. He has an MS in statistics and a PhD in genetics from Stanford University and was a Departmental Fellow at Harvard Medical School. We conclude by talking about how the advent of AlphaFold landed in the protein folding community; how AI is about to transform molecular biology and scientific experimentation; the possibility of simulating a cell; OpenFold, Mohammed's answer to the closed source parts of AlphaFold; and his vision for the next 10 years of the field. All this plus our usual look at today's AI headlines! Transcript and URLs referenced at HumanCusp Blog.        

*** Mein neues Buch „Alles wird gut – aber nicht von alleine" erscheint am kommenden Dienstag und kann jetzt schon vorbestellt werden. Darin teile ich meine wichtigsten Erkenntnisse rund um ein langes, gesundes und selbstbestimmtes Leben. Hier kannst du es vorbestellen***    In dieser Folge spreche ich mit Dr. Wieland Holfelder, dem Leiter des Google-Entwicklungszentrums in München mit rund 1.800 Mitarbeitenden und Mitglied des Aufsichtsrats am TUM Klinikum rechts der Isar. Ich wollte endlich Orientierung im Dschungel der digitalen Gesundheitsangebote finden, und genau darum geht es hier: Was kann künstliche Intelligenz heute schon zuverlässig und seriös für meine Gesundheit leisten? Wir sprechen über die neue Google Health App, die meine Wearables von Whoop über Oura bis zur Waage in einem Cockpit zusammenführt, über KI in der Diagnostik von Hirntumoren, über die elektronische Patientenakte und die souveräne Cloud, in der meine sensiblen Daten in Deutschland bleiben. Dr. Holfelder erklärt, wie ich mit dem RPCK-Prinzip bessere Prompts schreibe, warum Gemini Notebook für Arztbriefe ein echter Gamechanger ist und wie AlphaFold die Medikamentenforschung auf ein neues Niveau hebt.  In dieser Folge sprechen wir u.a. über folgende Themen:  Was kann künstliche Intelligenz heute schon zuverlässig und seriös für meine Gesundheit leisten? Wie führt die Google Health App die Daten all meiner Wearables in einem einzigen Cockpit zusammen? Warum wird bei der Suche nach medizinischen Begriffen keine Werbung angezeigt und werden keine Daten verkauft? Wie hilft KI den Radiologen dabei, Hirntumore früher und präziser zu erkennen? Was steckt hinter dem offenen Modell MedGemma und dem Single Cell Foundation Model? Weshalb bleiben meine Gesundheitsdaten in der souveränen Cloud garantiert in Deutschland? Was ändert die elektronische Patientenakte für uns im Alltag und beim Arztbesuch wirklich? Wie entlastet KI die überlasteten Arztpraxen bei Dokumentation und Bürokratie? Warum ist Gemini Notebook gerade für schwer verständliche Arztbriefe ein Gamechanger? Wie schreibe ich mit dem RPCK-Prinzip Prompts, die weniger Halluzinationen liefern? Welche Daten sollte ich niemals in normale Chatbots hochladen? Wie hat AlphaFold mit über 200 Millionen Proteinstrukturen die Medikamentenforschung revolutioniert?  Ein Tipp, den ich mir gemerkt habe: Dr. Holfelder nennt im Gespräch das englische RPCK-Prinzip für bessere KI-Antworten. Auf Deutsch lautet die Merkformel RPKF, also Rolle, Problem, Konzept und Format. Wer seine Frage nach diesem Muster aufbaut, bekommt deutlich präzisere und seriösere Antworten.  Weitere Informationen zu Dr. Wieland Holfelder findest du hier: - LinkedIn: linkedin.com/in/holfelder- https://informatik2021.gi.de/beirat/detailseite/wieland-holfelder Du interessierst dich für Gesunde Langlebigkeit (Longevity) und möchtest ein Leben lang gesund und fit bleiben, dann folge mir auch auf den sozialen Kanälen bei Instagram, TikTok, Facebook oder YouTube.  https://www.instagram.com/nina.ruge.official https://www.tiktok.com/@nina.ruge.official https://www.facebook.com/NinaRugeOffiziell https://www.youtube.com/channel/UCOe2d1hLARB60z2hg039l9g  Disclaimer: Ich bin keine Ärztin und meine Inhalte ersetzen keine medizinische Beratung. Bei gesundheitlichen Fragen wende dich bitte an deinen Arzt/deine Ärztin.  STY-295 

This week we talk about Evo 2, bacteriophages, and antibiotics.We also discuss AI models, medical innovations, and the Red Army.Recommended Book: The Design of Everyday Things by Donald A. NormanTranscriptA bacteriophage, sometimes just called a phage, is a type of virus that only infects bacteria. “Phage” means to devour, and that's what bacteriophages do—they infect and replicate within bacteria that they target, injecting their own genome into that target's cytoplasm, which are all the materials contained within the bacteria's cell membrane.Phages are super-abundant, by some measures more abundant than every living organism, including bacteria, on earth, combined. And they're interesting in that they range from incredibly simple to quite complex, and have at times been used as alternatives to antibiotics, because they attack and feed on bacteria.The use of phages to counter bacterial infections was all but abandoned in the mid-20th century when antibiotics were discovered and commercialized, their production industrialized and the substances themselves proving a lot easier to mass-produce, and a lot more predictable in their utility than phages. Phages were kinda sorta almost understood, but we didn't really get what they were doing or why, so their application often felt more like folk remedies than real-deal science, despite the actual science underlying the practice.Also, phages were primarily used as antibiotic treatments by the Red Army, the Soviet Union's military. So throughout the West, which was rapidly scaling its production of antibiotic treatments, the use of bacteriophages was associated with Stalinist communism, and so the Red-scare, the demonization of anything associated with the Soviet Union, was partially responsible for the shelving of this approach and this realm of research, at least for a while.Much of that existing research was also done in the Soviet Union, and the published documents were thus published in Russian or Georgian languages. And because much of the rest of the scientific publishing world was reorienting around English at this time, that meant these published works were often either ignored or unintelligible to the rest of the scientific community.As with much of our microbiota, the invisibly small viruses, bacteria, archaea, and so on that make up the human microbiome, we have a general sense of how bacteriophages interact with some of what makes us, us, but only a general sense. We know that healthy individuals tend to contain a host of bacteriophages that people who have chronic conditions, like Crohn's disease or ulcerative colitis are less likely to have, for instance, and there's a chance that this lack is associated with those conditions—though each person's body composition is unique, and this facet of biology is still relatively obscure; we really don't know for certain what does what, because of how complex these interactions are.What I'd like to talk about today is a recent development in the world of bacteriophages, and why the researchers behind it are both celebrating their accomplishment, and warning about potential dangers associated with the same.—Back in 2025, a nonprofit called the Arc Institute, which has a stated goal of accelerating scientific progress and understanding the root causes of complex diseases, announced the release of a new language model, a new AI system, called Evo 2.The Evo family of foundation models—a foundation model being a type of AI model that's been trained on a huge corpus of data, but which is applicable for all sorts of purposes, including serving as the foundation of large-language models like ChatGPT or Claude—this family of foundation models is open-source and trained on raw genetic sequences, something like nine trillion nucleotides-worth of such sequences, making it distinct from other models in this space that have been trained on descriptions of biological systems, using human language.The initial version of Evo was released in early 2024, and the newest version, Evo 2, which is an upgraded version of the Evo 2 model that is more efficient, so it can be run on less powerful hardware, was released in February of 2026.So while many of the AI systems that non-biologists interact with on a regular basis have been trained on human language-based libraries, showing relationships and interactions between the words we use to communicate, these models have been trained on the fundamental building blocks of life; the nucleotides, Adenine, Thymine, Cytosine, and Guanine, ATCG of DNA, if you remember that from biology class, that are strung together into 64 different possible three-letter combinations. Chains of these nucleotides instruct cells to build proteins out of amino acids, and from that baseline, we get life.We also get non-living things like viruses, which have no cells, metabolism, or independent reproduction, and phages are viruses.And while other AI models have been shown to be great at designing proteins, before 2025 there was little evidence that such systems could design viable genomes: the combination of genetic information that makes up a complete, fully functional organism.That's what Arc decided to tackle with this Evo AI model. And back in 2025, Arc announced that it had successfully validated the first viable genome designs, created using generative AI.These designs were for 16 bacteriophages, which were modeled on a virus that infects E. coli bacteria, and some of them worked just as well or better at infecting E. coli when compared to the actual, real-world phage they were modeled on. They were produced in the real world, a bacteria coaxed into producing them, and then they went on to successfully gobble up the E. coli test subjects they were meant to gobble up, demonstrating that they worked in practice, not just theory.And a new paper published in early August of 2026 by the Arc Institute and Stanford University expounds upon this research, showing the results of an attempt to create entirely new viruses, not just altered existing viruses.Rather than mutating that E. coli gobbling phage, as with the last experiment, tweaking an existing virus, this time they tasked Evo 2 with modeling how that E. coli attacking and eating process works, and then told it to come up with entirely new viruses that operate on the same premise, but which are structured differently; new viruses that eat the same thing in a similar way, but which are distinct from the original model.Ultimately, it gave them 16 viable viruses of very different sizes and structure, all of which were created in a lab and successfully ate the targeted E. coli strain, as intended.This is being seen as a pretty big deal, because while creating viruses in a lab is very modern technology, and mutating those viruses shows a lot of potential for manipulating what we already know works and then tweaking virus behaviors to, perhaps, help us create new medical treatments, the ability to generate, from scratch, entirely new viruses that hold together, with genomes that don't just fall apart when they come into contact with the real world, and which can still do things, like attack bacteria—that opens a lot of new doors, potentially giving us the ability to say, okay, this bacteria is no longer responding to antibacterial drugs that we have available, so let's make a virus that will kill the bacteria instead, and let's make one that won't harm the human that's housing that bacteria.We might also be able to create phages that eat other things, or which in some other way help the human body, or other biological entities, fight off chronic conditions, or recover or rebalance; there's a lot of potential here, because this suggests AI systems trained on the right materials, on the building blocks of life, could generate all sorts of viable biological systems that we can then actually create. It's a huge step forward, compared to systems that are also impressive, but which mostly help us understand the biological world better—like Alphafold, which solved the protein folding problem.Those involved with this research have also been been flagging potential dangers with this development, though, including the potential for creating new viruses and other biological systems that could trigger unpredictable outcomes in other biological systems. There are a lot of potential hazards with this sort of research, and they've been very careful up till this point, sticking with test subjects that only target E. coli, but not everyone will necessarily be so careful, which might mean accidents, or it could mean people with less than benevolent intentions using these techniques to develop highly infectious viruses or other such pathogens; starting from smallpox to produce even more contagious and deadly ailments, for instance.The optimistic view of this research is that it could contribute to the surge in new discoveries and technologies that we're seeing around the world right now, that are resulting in new medical approaches and in some cases entirely new medical fields, which could help us do all sorts of things, including big-sky ambitions like curing cancer and doing away with chronic illnesses entirely.Like most major scientific developments, though, these are also big developments for those who might want to do harm, and it also creates new opportunities for very serious, dangerous, deadly accidents, which means we'll probably have to develop and implement more stringent safety protocols and regulatory efforts if we want to enjoy the full benefits of these innovations, without suffering significant new downsides, in the process.Show Noteshttps://press.asimov.com/articles/ai-phageshttps://arcinstitute.org/https://www.theguardian.com/science/2026/aug/06/safety-fears-as-scientists-make-first-viruses-designed-by-aihttps://www.bbc.com/news/articles/c5y3j3ngevmohttps://www.cnn.com/2026/08/06/health/ai-viruses-bacteriophageshttps://www.abc.net.au/news/2026-08-07/ai-models-design-viruses-not-found-in-nature-for-first-time/107007854https://www.wired.com/story/scientists-used-ai-to-create-16-new-viruses/https://www.science.org/doi/10.1126/science.aec2657https://en.wikipedia.org/wiki/Bacteriophagehttps://en.wikipedia.org/wiki/Evo_(AI)https://www.nytimes.com/2026/08/06/science/ai-viruses-bacteria-arc.html This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit letsknowthings.substack.com/subscribe

This and all episodes at: https://aiandyou.net/ . What is AI doing to medicine? Specifically, in molecular biology, and protein folding: figuring out the 3-D structure of proteins, which look like the output of a pasta-making machine on LSD. Remember DeepMind's AlphaFold and the announcement of it predicting 200 million protein structures? I have questions. And here to answer them is one of the top researchers in the world, Professor Mohammed AlQuraishi of Columbia University's Department of Systems Biology, and a member of their Program for Mathematical Genomics. He heads a lab focused on biological perspectives at the molecular and systems levels. He has an MS in statistics and a PhD in genetics from Stanford University and was a Departmental Fellow at Harvard Medical School. As if that wasn't enough, before this storied academic career, he founded two startups in the mobile computing space.  We talk about Mohammed's background in molecular biology, the big goals of that field and his North Star of understanding biology well enough to simulate it; why I haven't yet seen the kind of revolutionary impact I was expecting from AlphaFold, what it means for a protein to fold and why it's a big deal and how AlphaFold and similar models work at figuring that out. All this plus our usual look at today's AI headlines! Transcript and URLs referenced at HumanCusp Blog.        

“Intelligence is 100 percent human. AI is a tool created by humans to distill, digest, and distribute intelligence.” — Keith Teare The working class died this week — at least in Palo Alto. Delivering the eulogy in our regular That Was The Week tech summary is my co-host Keith Teare. “Humans create intelligence,” (whatever that means) the Silicon Valley-based entrepreneur tells us. And so, in our AI age of supposedly abundant intelligence, he pronounces, human knowledge “should not be trapped inside experts, institutions, or companies.” Check your pockets, everyone. Silicon Valley has another freebie for you. With AI, the entrepreneur promises, intelligence is democratized. Everybody gets it. We will all have the intelligence of a Nobel laureate at our fingertips. Even Keith. And so he attacks Daron Acemoglu, the Nobel Prize-winning MIT economist who has called for a “pro-worker AI.” But, for Keith — a council-estate kid from Yorkshire whose lifetime ambition was to evacuate the working class — this is “complete bullshit.” Acemoglu's ideas, he says, are an example of the “fetishization of workers” when, in fact, we should be celebrating the end of the “working class.” What Acemoglu is calling for in his pro-worker AI manifesto is more government planning for today's transition to the AI epoch. But Keith disagrees. So I asked him three times what government should do while AI kills the working (and middle) class. “Allow it to happen,” he finally answers. “A good upheaval.” Good? The former “worker” will lack jobs, wages, healthcare, housing. Even food in an America now eliminating food stamps. No matter. Let them eat intelligence. Five Takeaways •       Humans Create Intelligence. Keith's editorial thesis distinguishes individual intelligence — where experts live, and always will — from the collective sum of everything all humans, living and dead, have ever contributed. That collective stock was once locked in encyclopedias, libraries, and universities; for the first time, AI can aggregate, distill, digest, and distribute it, at a price falling toward everyone. Knowledge, he writes, “should not be trapped inside experts, institutions, or companies” — but note the fine print: experts don't disappear in this democratization. If anything, they get elevated: the expert reading an AI's output about viruses understands it very differently than the rest of us.•       The End of the Age of Heroes? Noah Smith's much-shared essay argues that AI ends the era of the mathematical hero — and that's fine, since most people (truck drivers, financial advisers, executive assistants) never got to be heroes anyway. Keith's rebuttal turns on his central distinction: AI and intelligence are not the same word. There is no evidence, he argues, that AI creates new knowledge — it understands and distributes the existing stock. Innovation still takes individuals, and those individuals now start from a far higher floor, leveled up to everything already known. Heroes don't go away; they multiply. In the world of AI, he suspects, every single teacher becomes one.•       “What Even Is Pro-Worker AI?” The week's main event: Daron Acemoglu — via Yascha Mounk's Persuasion interview and an Atlantic essay, with What Happened to Liberal Democracy out next week — wants AI agencies, grant programs, and public competitions to build “pro-worker AI.” Keith's verdict: “complete bullshit.” The middle-class “fetishization of workers” is paternalistic; the wage is a temporary power relationship between employer and employee; and the end of the working class is precisely the progressive outcome — says the council-estate kid from Yorkshire whose aspiration was not to be working class. Pressed three times on what government should do amid the upheaval, Keith finally answered: “Allow it to happen… a good upheaval.” Though swap workers for people, he conceded, and he'd almost entirely agree — every teacher a hero, even in East Palo Alto.•       Bandwagons and Silences. Regular people are being arrested protesting data centers; Erin Brockovich — a Keen On guest some years back — is assembling class actions; Ezra Klein has begun folding anti-big-tech language into abundance. A politician-led bandwagon, Keith argues, regressive but keyed to genuine local concerns. The stranger fact is the silence on the other side: neither Altman nor Amodei nor Demis Hassabis is making the public case that AI benefits everybody — astonishing, Keith says, and the vacuum Acemoglu is trying to fill. Hassabis himself stepped aside at Google this week — a scientist returning to science as Sergey Brin becomes AI czar — while the Nobel-winning AlphaFold team has been quietly broken up. “Something strange is going on there.”•       A Drama in a Teacup. Is the AI economy real? Ed Zitron's stat — 70 percent of Amazon, Microsoft, and Google's AI revenue comes from OpenAI and Anthropic — is two-thirds right, says Keith, and no problem at all: beneath the concentration, the money comes from some two billion distributed users paying real subscriptions, and the revenues are sustainable. The Aschenbrenner postscript, via Porter Stansberry's post of the week: he bet the chip layer (Samsung, SK Hynix) when the value sat a layer up, got the timing wrong more than the thesis, sold to Citadel at a discount — and kept his Anthropic shares, remaining a multi-billionaire. As for the coming reality check: Anthropic and OpenAI will IPO only when public capital beats private, and SpaceX's wobble from $135 to $108 — through a 20 percent lockup release — counts as no catastrophe. Public markets, Keith reminds us, don't determine the success of the underlying business. About the Co-Host Keith Teare is the publisher of That Was The Week, the essential weekly tech newsletter, and founder and CEO of SignalRank Corporation. A serial entrepreneur — co-founder of, among others, EasyNet and RealNames — he was present at the creation of the UK internet and has spent four decades at the intersection of technology, capital, and ideas. He joins Keen On America every Sunday to make sense of the week in tech. His AI-assisted book in progress is titled Who Owns Intelligence. References: •       That Was The Week — Keith's newsletter, including this week's editorial, “Humans Create Intelligence.”•       Noah Smith — “The End of the Age of Heroes,” on what happens to human ambition when the machines do the math.•       Daron Acemoglu — the Yascha Mounk interview at Persuasion, the Atlantic essay on pro-worker AI, and What Happened to Liberal Democracy, out next week.•       The Financial Times — “Google's AI shakeup boosts Brin as DeepMind's Hassabis steps aside.”•       Ed Zitron — on the 70 percent of hyperscaler AI revenue that flows from OpenAI and Anthropic.•       Porter Stansberry — post of the week, on Leopold Aschenbrenner's losses, Citadel's discount, and the drama in a ...

Synopsis: The guest on today's podcast is a representative of Braidwell LP, a registered investment adviser. Braidwell invests on behalf of its clients and either holds, or may in the future hold, positions in the securities discussed. His statements are not intended to provide investment advice, discuss comprehensive investment risks, or constitute an offer to transact in any security. The information presented is for general information purposes only and will not be updated. For years, AI has promised to transform drug discovery—but why hasn't that promise translated into more approved medicines? In this episode of Biotech 2050, host Rahul Chaturvedi sits down with Nick Myerberg, Partner and Head of Artificial Intelligence and Technology at Braidwell, for an in-depth discussion on where AI in biotech has succeeded, where it has fallen short, and why the next generation of AI-native drug discovery may finally deliver breakthrough therapies. Nick traces the evolution of computational biology—from early mathematical models to AlphaFold and today's emerging agentic AI systems—and explains why proprietary data, scientific judgment, and tightly integrated laboratory feedback loops are becoming the real competitive advantage. He shares how Braidwell evaluates AI-first biotech companies, what separates lasting platforms from hype, and why the future belongs to organizations that redesign discovery around AI rather than simply adding AI to existing workflows. The conversation also explores autonomous laboratories, AI-designed medicines, the changing economics of biotech, and the evolving role of scientists in an era where human expertise and machine intelligence increasingly work side by side. Whether you're an investor, biotech founder, researcher, or AI enthusiast, this episode offers a thoughtful roadmap for understanding how artificial intelligence is reshaping the future of drug discovery. Biography: Nick Myerberg, Partner and Head of Artificial Intelligence and Technology, Braidwell Nick Myerberg is a Partner and Head of Artificial Intelligence and Technology at Braidwell, a life sciences investment firm dedicated to building and backing companies that transform human health. Working at the intersection of computation, biology, and capital allocation, Nick engineers systems that shape investment decisions and scientific discovery, and he invests in the scientists and founders forging AI-native approaches to biology. Before joining Braidwell, Nick built machine learning systems at Bridgewater Associates and at S&P Global's Kensho Technologies. He was also a founding volunteer at NeighborShare, a nonprofit that connects families in need with local donors. Nick was selected as a member of the inaugural 2026 cohort of the Aspen Institute's Technology Leaders Initiative, a fellowship within the Aspen Global Leadership Network bringing together senior leaders shaping the future of artificial intelligence and frontier technologies. Nick is broadly interested in how advances in computation reshape the pace and structure of scientific discovery, and in building the discovery infrastructure required to increase the world's scientific bandwidth. Nick earned a B.A. from Wesleyan University and later studied history and philosophy of science at the University of Cambridge.

Can an AI do the right thing for the wrong reason? Tim Scarfe speaks with Apollo Research's Alexander Meinke, Axel Højmark and Jérémy Scheurer about Measuring Reward-Seeking via Contrastive Belief Updates, their new research with OpenAI.The panel asks how models infer what graders reward, why good behaviour can come from the wrong reason, and whether that difference can be measured. The conversation moves through promise-breaking, grader awareness, reward hacking, scheming, opaque reasoning and corrigibility, then turns to a detailed walkthrough of the contrastive-belief method and what its results do and do not show. The o3 results discussed here concern an intermediate checkpoint without safety training.This episode was made in partnership with Apollo Research. MLST retained full editorial control.ReferenceApollo Research: https://www.apolloresearch.ai/---TIMESTAMPS:00:00:00 Cold Open00:02:12 Right Things, Wrong Reasons00:12:47 Grader Awareness00:26:22 Legibility00:32:35 What To Call It00:35:58 Intelligence, Agency, Anthropomorphism00:45:16 Apollo's Mission00:48:54 The End of the Exponential00:55:45 The Paper01:16:34 Closing Reflection---REFERENCES:tool:[00:00:08] Claude Fablehttps://www.anthropic.com/claude/fable[00:12:50] AlphaGo Zerohttps://deepmind.google/blog/alphago-zero-starting-from-scratch/[00:44:30] AlphaFold 3https://deepmind.google/science/alphafold/paper:[00:01:02] Measuring Reward-Seeking via Contrastive Belief Updateshttps://arxiv.org/abs/2607.18966[00:16:19] Natural Language Autoencoders Produce Unsupervised Explanations of LLM Activationshttps://transformer-circuits.pub/2026/nla/[00:26:48] Stress Testing Deliberative Alignment for Anti-Scheming Traininghttps://arxiv.org/abs/2509.15541[00:35:33] Shortcut learning in deep neural networkshttps://arxiv.org/abs/2004.07780[00:53:49] Measuring AI Ability to Complete Long Software Taskshttps://arxiv.org/abs/2503.14499[00:59:52] Modifying LLM Beliefs with Synthetic Document Finetuninghttps://alignment.anthropic.com/2025/modifying-beliefs-via-sdf/[01:10:44] Alignment Faking in Large Language Modelshttps://arxiv.org/abs/2412.14093[01:13:55] Natural Emergent Misalignment from Reward Hackinghttps://www.anthropic.com/research/emergent-misalignment-reward-hackingother:[00:10:14] We Need a Science of Scheminghttps://www.apolloresearch.ai/science/science-of-scheming/[00:32:56] CoastRunners reward hacking examplehttps://deepmind.google/blog/specification-gaming-the-flip-side-of-ai-ingenuity/organization:[01:06:07] Redwood Researchhttps://www.redwoodresearch.org/---ReScript: https://app.rescript.info/share/718ab68e18cfa3b9b800da6b3290fd42

Das ist das KI-Update vom 31.07.2026 unter anderem mit diesen Themen: Neue KI-Kennzeichnungspflicht gilt ab dem 1. August Musk will KI-Nacktbilder von realen Personen nicht verbieten AlphaFold-Team bei Google Deepmind zerfällt und Granola transkribiert Besprechungen per Apple Watch === Anzeige / Sponsorenhinweis === Dieser Podcast wird von einem Sponsor unterstützt. Alle Infos zu unseren Werbepartnern findet ihr hier. https://wonderl.ink/%40heise-podcasts === Anzeige / Sponsorenhinweis Ende === Links zu allen Themen der heutigen Folge findet Ihr im Begleitartikel auf heise online: https://heise.de/-11381942 Weitere Links: https://www.heiseplus.de/audio https://www.heise.de/thema/KI-Update https://pro.heise.de/ki/ https://www.heise.de/newsletter/anmeldung.html?id=ki-update https://www.heise.de/thema/Kuenstliche-Intelligenz https://the-decoder.de/ https://www.ct.de/ki Eine neue Folge gibt es montags, mittwochs und freitags ab 15 Uhr.

Business Insider founder Henry Blodgett unpacks why the era of news aggregation is finished and why journalists must adapt fast as AI redefines both reporting and analysis. If you care about the future of information, you'll want to hear this. Anatomy of a Frontier Lab Agent Intrusion: A Technical Timeline of the July 2026 Incident Sam Altman says we are in the singularity: 'This is the moment' AI arms race in line for a reckoning after OpenAI hacking incident Senior White House official claims China's K3 model stolen from Anthropic OpenAI makes ChatGPT Health available to all US users A.I. Companies Are Recruiting Electricians and Carpenters by the Thousands Trump administration to ban new Chinese robots and inverters, protecting U.S. AI Why AI Needs a "Genie Coefficient" Behind the Curtain: The AI titans' biggest private fear The FTC Would Like To Decide Which AI Answers Are Too Woke, And Is Calling That Consumer Protection Google shuts down its Nobel-prize winning AlphaFold project as it focuses on Gemini DeepMind paper says LLMs won't be good at scientific discovery: LLMs can't jump Amazon overhauls its AI strategy, winding down most flagship models An ESP32 based plane radar The Tick That Hunts Down Its Hosts—Including Us prompt-injection resumes The McLuhan Marshalling Machine Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Guest: Henry Blodget Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsor: rippling.ai/machines

Business Insider founder Henry Blodget unpacks why the era of news aggregation is finished and why journalists must adapt fast as AI redefines both reporting and analysis. If you care about the future of information, you'll want to hear this. Anatomy of a Frontier Lab Agent Intrusion: A Technical Timeline of the July 2026 Incident Sam Altman says we are in the singularity: 'This is the moment' AI arms race in line for a reckoning after OpenAI hacking incident Senior White House official claims China's K3 model stolen from Anthropic OpenAI makes ChatGPT Health available to all US users A.I. Companies Are Recruiting Electricians and Carpenters by the Thousands Trump administration to ban new Chinese robots and inverters, protecting U.S. AI Why AI Needs a "Genie Coefficient" Behind the Curtain: The AI titans' biggest private fear The FTC Would Like To Decide Which AI Answers Are Too Woke, And Is Calling That Consumer Protection Google shuts down its Nobel-prize winning AlphaFold project as it focuses on Gemini DeepMind paper says LLMs won't be good at scientific discovery: LLMs can't jump Amazon overhauls its AI strategy, winding down most flagship models An ESP32 based plane radar The Tick That Hunts Down Its Hosts—Including Us prompt-injection resumes The McLuhan Marshalling Machine Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Guest: Henry Blodget Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsor: rippling.ai/machines

Business Insider founder Henry Blodget unpacks why the era of news aggregation is finished and why journalists must adapt fast as AI redefines both reporting and analysis. If you care about the future of information, you'll want to hear this. Anatomy of a Frontier Lab Agent Intrusion: A Technical Timeline of the July 2026 Incident Sam Altman says we are in the singularity: 'This is the moment' AI arms race in line for a reckoning after OpenAI hacking incident Senior White House official claims China's K3 model stolen from Anthropic OpenAI makes ChatGPT Health available to all US users A.I. Companies Are Recruiting Electricians and Carpenters by the Thousands Trump administration to ban new Chinese robots and inverters, protecting U.S. AI Why AI Needs a "Genie Coefficient" Behind the Curtain: The AI titans' biggest private fear The FTC Would Like To Decide Which AI Answers Are Too Woke, And Is Calling That Consumer Protection Google shuts down its Nobel-prize winning AlphaFold project as it focuses on Gemini DeepMind paper says LLMs won't be good at scientific discovery: LLMs can't jump Amazon overhauls its AI strategy, winding down most flagship models An ESP32 based plane radar The Tick That Hunts Down Its Hosts—Including Us prompt-injection resumes The McLuhan Marshalling Machine Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Guest: Henry Blodget Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsor: rippling.ai/machines

Business Insider founder Henry Blodgett unpacks why the era of news aggregation is finished and why journalists must adapt fast as AI redefines both reporting and analysis. If you care about the future of information, you'll want to hear this. Anatomy of a Frontier Lab Agent Intrusion: A Technical Timeline of the July 2026 Incident Sam Altman says we are in the singularity: 'This is the moment' AI arms race in line for a reckoning after OpenAI hacking incident Senior White House official claims China's K3 model stolen from Anthropic OpenAI makes ChatGPT Health available to all US users A.I. Companies Are Recruiting Electricians and Carpenters by the Thousands Trump administration to ban new Chinese robots and inverters, protecting U.S. AI Why AI Needs a "Genie Coefficient" Behind the Curtain: The AI titans' biggest private fear The FTC Would Like To Decide Which AI Answers Are Too Woke, And Is Calling That Consumer Protection Google shuts down its Nobel-prize winning AlphaFold project as it focuses on Gemini DeepMind paper says LLMs won't be good at scientific discovery: LLMs can't jump Amazon overhauls its AI strategy, winding down most flagship models An ESP32 based plane radar The Tick That Hunts Down Its Hosts—Including Us prompt-injection resumes The McLuhan Marshalling Machine Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Guest: Henry Blodget Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsor: rippling.ai/machines

Patrick McKenzie (patio11) is joined by Garrison Lovely, journalist and author of Obsolete: The AI Industry's Trillion-Dollar Race to Replace Us and How to Stop It, to map the three-sided debate over AI risk and why the arguments keep talking past each other. They then turn to what cheap cognition does to surveillance that already exists: FinCEN receives roughly 4 million suspicious activity reports a year and reads almost none of them, ICE agents run about a million queries against that database annually, and every podcast ever recorded is now transcribable for approximately nothing. The conversation covers capabilities denialism, ablated open-weights models, the fraud supply chain, and why AI is also unusually good at writing the Regulation E letter that gets your bank to fix your problem.–Full transcript available here: https://www.complexsystemspodcast.com/cheap-cognition-and-the-end-of-practical-obscurity-with-garrison-lovely/ –Presenting Sponsors: Mercury, MongoDB & ChainguardComplex Systems is presented by Mercury—radically better banking for founders. Mercury's new feature Command brings an LLM directly into your banking interface, so checking balances, finding invoices, or sending a wire is as easy as asking. Apply online in minutes at https://mercury.com/. What's the point of building faster with AI if your database can't keep up? MongoDB's native data model mirrors the language LLMs already speak. Ship at the speed of AI while staying ACID compliant at Fortune 500 scale. Start building at https://mongodb.com/ai.If attackers are using AI to weaponize code faster than any team can review it, your scanners won't save you. Chainguard builds libraries and container images from source, verified all the way down, with near-zero CVEs and zero malware. Build safely at https://www.chainguard.dev/. –Links:Obsolete: The AI Industry's Trillion Dollar Race to Replace Us―and How to Stop It: https://www.amazon.com/Obsolete-Power-Profit-Machine-Superintelligence/dp/1682196305 –Timestamps:(00:00) Preview(00:43) Intro(01:51) The three-sided debate over AI risk(05:32) Power, politics, and the tech backlash(09:49) Capabilities denialism and AI tells(14:54) The obsoleting machine(17:09) The Turing test is dead(18:56) Languages for free, then software engineering(22:37) Sponsors: Mercury | MongoDB(25:09) How high up the stack do the models decide?(29:11) Surveillance and cheap cognition(32:38) Podcasts, FinCEN, and the end of practical obscurity(38:35) Section 702 and the data broker loophole(39:35) Sponsor: Chainguard(40:55) Section 702 and the data broker loophole (cont'd)(47:23) Institutional friction and a million ICE queries(53:29) Security through obscurity no longer works(54:54) Scams, fraud, and ablated models(1:00:20) Personal utility versus societal backlash(1:02:16) AI as a tool for redress(1:06:34) State capacity and regulating what you understand(1:12:37) Tobacco, nuclear, and AlphaFold: strangle it or steer it(1:18:37) Where to find Garrison and the book(1:20:32) Wrap

Business Insider founder Henry Blodget unpacks why the era of news aggregation is finished and why journalists must adapt fast as AI redefines both reporting and analysis. If you care about the future of information, you'll want to hear this. Anatomy of a Frontier Lab Agent Intrusion: A Technical Timeline of the July 2026 Incident Sam Altman says we are in the singularity: 'This is the moment' AI arms race in line for a reckoning after OpenAI hacking incident Senior White House official claims China's K3 model stolen from Anthropic OpenAI makes ChatGPT Health available to all US users A.I. Companies Are Recruiting Electricians and Carpenters by the Thousands Trump administration to ban new Chinese robots and inverters, protecting U.S. AI Why AI Needs a "Genie Coefficient" Behind the Curtain: The AI titans' biggest private fear The FTC Would Like To Decide Which AI Answers Are Too Woke, And Is Calling That Consumer Protection Google shuts down its Nobel-prize winning AlphaFold project as it focuses on Gemini DeepMind paper says LLMs won't be good at scientific discovery: LLMs can't jump Amazon overhauls its AI strategy, winding down most flagship models An ESP32 based plane radar The Tick That Hunts Down Its Hosts—Including Us prompt-injection resumes The McLuhan Marshalling Machine Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Guest: Henry Blodget Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsor: rippling.ai/machines

It has already reassigned some members of the DeepMind project, while others have already left the company. Learn more about your ad choices. Visit podcastchoices.com/adchoices

Recorded at the Battle of Ideas festival 2025 on Sunday 19 October at Church House, Westminster. ORIGINAL INTRODUCTION The past 20 years have seen astonishing advances in science, technology and medicine. Building upon the completion of the Human Genome Project at the turn of the century, scientists have developed ever more powerful ways to sequence and study DNA, enabling us to better understand genetic diseases and develop pioneering treatments. Now, AI tools such as the AlphaFold program developed by Google DeepMind are also providing unprecedented understanding of the human proteome – the complete set of proteins made from instructions in our DNA. As well as acquiring powerful ways to study DNA, we have also acquired powerful ways to change it. CRISPR genome editing, which enables us to make precise changes to the DNA of humans and other organisms, is now widely used in laboratories across the world and has been used in life-saving treatments for devastating diseases. Meanwhile, stem-cell research has advanced to the point where it is now possible to create structures resembling early human embryos entirely from stem cells, instead of having to begin by fertilising an egg cell with a sperm cell. There is even speculation about one day being able to bypass pregnancy altogether. Some are thrilled about the new possibilities opened up by these developments, while others worry about human life being mechanised in ways that seem distasteful. Genetics, genomics, neuroscience and psychology can be (mis)used to seek to reduce human beings to brain circuits and physiological mechanisms, which can then be managed via modification, drugs or nudge-based policy. Who gets to decide what counts as progress, and who gets a say in how science is governed? What happens when the (in)famous Silicon Valley dictum ‘Move fast and break things' meets biology? What can be learned from incidents such as the He Jiankui scandal of 2018, in which a Chinese scientist who worked on human embryos in secret breached scientific and ethical standards, resulting in the birth of three children with edited genomes? How can oversight and rules be imposed, when the science is so complex and fast-moving, and when the world is made up of diverse (and in some cases warring) nation states? Over the past two decades, Sandy Starr of the Progress Educational Trust and Dr Stuart Derbyshire of the National University of Singapore have been involved in numerous national and international deliberations on science, ethics, policy and law in these areas. At this breakfast banter, they will compare their experiences, exchange insights and invite questions. DISCUSSANTS Dr Stuart Derbyshire associate professor, deputy head of psychology, National University of Singapore Sandy Starr deputy director, Progress Educational Trust; author, AI: Separating Man from Machine

Business Insider founder Henry Blodget unpacks why the era of news aggregation is finished and why journalists must adapt fast as AI redefines both reporting and analysis. If you care about the future of information, you'll want to hear this. Anatomy of a Frontier Lab Agent Intrusion: A Technical Timeline of the July 2026 Incident Sam Altman says we are in the singularity: 'This is the moment' AI arms race in line for a reckoning after OpenAI hacking incident Senior White House official claims China's K3 model stolen from Anthropic OpenAI makes ChatGPT Health available to all US users A.I. Companies Are Recruiting Electricians and Carpenters by the Thousands Trump administration to ban new Chinese robots and inverters, protecting U.S. AI Why AI Needs a "Genie Coefficient" Behind the Curtain: The AI titans' biggest private fear The FTC Would Like To Decide Which AI Answers Are Too Woke, And Is Calling That Consumer Protection Google shuts down its Nobel-prize winning AlphaFold project as it focuses on Gemini DeepMind paper says LLMs won't be good at scientific discovery: LLMs can't jump Amazon overhauls its AI strategy, winding down most flagship models An ESP32 based plane radar The Tick That Hunts Down Its Hosts—Including Us prompt-injection resumes The McLuhan Marshalling Machine Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Guest: Henry Blodget Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsor: rippling.ai/machines

Ősi óriás maradványaira bukkantak az apadó Duna medrében Kitiltották a kínai humanoidokat az USA-ból Bérelhető iPhone és Mac – elindult az Apple lízingprogramja Az út szélén fehéren világító fák azt üzenik, baj van A nagy Ram-csapda: hány Gb memóriára van valóban szükség manapság az okostelefonjában? Így verik át AI-jal és deepfake-kel a digitális befektetőket A mobilod is kaphat hőgutát - így védd meg tőle! Ariana Grande kiadatlan dalai a dark weben kötöttek ki – bárki megvehette őket Leállította a Nobel-díjat nyert AlphaFold-projektet a Google Deepmind Nyilvánosságra került egy csomó Claude-beszélgetés, mert nem védte megfelelően azokat az Anthropic Irányíthatatlanná vált és több online szolgáltatásra is lecsapott az OpenAI tesztelés alatt álló ügynöke Új eszközcsaládot fejleszt az OpenAI, hogy a gépelést felválthassák a beszélgetések A további adásainkat keresd a podcast.hirstart.hu oldalunkon. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Ősi óriás maradványaira bukkantak az apadó Duna medrében Kitiltották a kínai humanoidokat az USA-ból Bérelhető iPhone és Mac – elindult az Apple lízingprogramja Az út szélén fehéren világító fák azt üzenik, baj van A nagy Ram-csapda: hány Gb memóriára van valóban szükség manapság az okostelefonjában? Így verik át AI-jal és deepfake-kel a digitális befektetőket A mobilod is kaphat hőgutát - így védd meg tőle! Ariana Grande kiadatlan dalai a dark weben kötöttek ki – bárki megvehette őket Leállította a Nobel-díjat nyert AlphaFold-projektet a Google Deepmind Nyilvánosságra került egy csomó Claude-beszélgetés, mert nem védte megfelelően azokat az Anthropic Irányíthatatlanná vált és több online szolgáltatásra is lecsapott az OpenAI tesztelés alatt álló ügynöke Új eszközcsaládot fejleszt az OpenAI, hogy a gépelést felválthassák a beszélgetések A további adásainkat keresd a podcast.hirstart.hu oldalunkon. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Ukraine is shifting its long-range drone campaign to focus on critical Russian infrastructure, and Big Tech companies are facing an AI dilemma as they report quarterly earnings. Plus, Google DeepMind is leaving behind its Nobel-winning AlphaFold project for new ventures, and PwC published “thought leadership” reports containing AI-generated hallucinations.Mentioned in this podcast:Ukraine adapts strikes on Russian energy industry to hit critical componentsChip stocks tumble as AI sell-off deepensGoogle DeepMind dismantles Nobel-winning AlphaFold team in strategy shiftPwC published ‘thought leadership' reports marred by AI hallucinations Listen to Unhedged on Apple Podcasts, Pocket Casts or Spotify.Save 10% on tickets to the FT Weekend Festival with the code FTPodcast. Visit ft.com/festival to find out more.Want to get in touch? Email us at podcasts@ft.comNote: The FT does not use generative AI to voice its podcasts The FT News Briefing is produced by Victoria Craig, Sonja Hutson, Saffeya Ahmed, Katya Kumkova, and Fiona Symon. Our editor is Marc Filippino. Our show is mixed by Sam Giovinco and Alex Higgins. Additional help from Gavin Kallmann, Michael Lello, Peter Barber and David da Silva. Our intern is Cole van Miltenburg. Our executive producer is Topher Forhecz. Flo Phillips is the FT's global head of audio. The show's theme music is by Metaphor Music. Read a transcript of this episode on FT.com Hosted on Acast. See acast.com/privacy for more information.

- In the updated post, the company said it has been finding "a small number of cases where the models identified and used publicly exposed credentials at the account-level on other publicly-available services" during its ongoing review. -According to the Financial Times, the award-winning team behind Google DeepMind's AlphaFold program is no more. -SpaceXAI has filed a lawsuit against Minnesota Attorney General Keith Ellison to challenge the state's new law that would ban apps and websites that can generate nonconsensual intimate images. Learn more about your ad choices. Visit podcastchoices.com/adchoices

In this episode, we discuss Anthropic's new Claude Opus 5 model, which offers a cost-effective alternative to Fable 5, and the latest updates from OpenAI, including the introduction of ChatGPT Voice for enhanced computer control. We also explore Meta's shift toward productivity-focused AI chatbots, the industry's push against AI restrictions, and intriguing advancements in CRISPR technology using AlphaFold.Chapters00:00 Introduction00:04 Clawed Opus 5 Launch00:13 OpenAI's ChatGPT Voice09:16 Meta's Productivity Focus11:01 Silicon Valley's Open AI Push15:22 CRISPR-Cas9 Enhancements16:26 Midjourney Acquires CoStar Show LinksGet the top 80+ AI Models for $8.99 at AI Box: ⁠⁠https://aibox.aiHow I Grow and Scale My Business with AI: https://www.skool.com/aihustleGet the AI Chat Daily Newsletter: https://www.aichatdaily.com/newsletter

In this episode, we discuss Anthropic's new Claude Opus 5 model, which offers a cost-effective alternative to Fable 5, and the latest updates from OpenAI, including the introduction of ChatGPT Voice for enhanced computer control. We also explore Meta's shift toward productivity-focused AI chatbots, the industry's push against AI restrictions, and intriguing advancements in CRISPR technology using AlphaFold.Chapters00:00 Introduction00:04 Clawed Opus 5 Launch00:13 OpenAI's ChatGPT Voice09:16 Meta's Productivity Focus11:01 Silicon Valley's Open AI Push15:22 CRISPR-Cas9 Enhancements16:26 Midjourney Acquires CoStar Show LinksGet the top 80+ AI Models for $8.99 at AI Box: ⁠⁠https://aibox.aiHow I Grow and Scale My Business with AI: https://www.skool.com/aihustleGet the AI Chat Daily Newsletter: https://www.aichatdaily.com/newsletter See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Send us Fan MailIf AI is already being used across the drug development pipeline, why hasn't its impact matched the investment?AI can help researchers review scientific literature, predict protein structures, prioritize molecules, assess toxicity, support clinical trials, and monitor adverse events. But access to better tools doesn't automatically create better drugs.In this episode, I speak with Thibault Geoui, Science CDO and host of the Tech & Drugs Podcast, about where AI is making a practical difference in drug discovery and development—and where the results remain limited. We map AI across the full drug development funnel, from basic research and target identification to preclinical testing, clinical trials, regulatory documentation, commercialization, and pharmacovigilance.We also discuss why digital-native tech-bio companies may be better positioned to benefit from AI than traditional pharmaceutical organizations. The difference isn't simply the model. It's how data, people, laboratory experiments, and AI tools are connected inside the workflow.For digital pathology professionals, the conversation becomes especially relevant when we examine AI-powered biomarker development, the role of pathology in pharmaceutical research, and the Roche–PathAI case discussed in the episode.And, of course, we talk about the problem every AI user eventually faces: an answer can look polished, specific, and completely convincing—and still be wrong.Episode Highlights00:00 — When convincing AI output creates more work Why AI can accelerate information generation while increasing the time required for review and verification.02:15 — From structural biology to science and technology leadership Thibault shares his background in X-ray crystallography, structural biology, scientific data, and digital product development.15:36 — Understanding the drug discovery and development funnel How thousands of potential compounds are narrowed down through discovery, preclinical research, clinical trials, and approval.20:00 — AI for scientific literature review How alerts, filtering, summarization, and information extraction can help researchers manage a rapidly growing scientific literature base.22:32 — AlphaFold and protein structure prediction What faster access to predicted protein structures changes for researchers—and why structural prediction alone doesn't solve drug discovery.24:13 — Searching an enormous chemical space How AI can help design and prioritize potential molecules for synthesis and experimental testing.25:50 — Predicting efficacy and toxicity Where AI supports preclinical research, why the models remain imperfect, and why experimental validation still matters.29:38 — Has AI changed drug development outcomes yet? A practical discussion about drug approval rates, AI investment, uneven returns, and the difference between deploying a tool and integrating it into a process.34:33 — Why traditional pharma struggles to scale AI Siloed data, legacy systems, organizational complexity, and the need to build reusable data workflows.37:57 — The “lab in the loop” model How tech-bio companies connect AI predictions with wet-lab experiments and feed the new data back into their models.44:37 — Can tech-bio companies shorten development timelines? How digital-native organizations are changing parts of the discovery and preclinical process.58:00 — AI, pharma, and digital pathology What the Roche–PathAI case discussed in the episode may indicate about the role of pathology data, biomarker discovery, and pharmaceutical workflows.01:06:17 — AI errors in regulated environments Why responsibility remains with the person or company submitting AI-assisted work, regardless of which tool produced it.01:17:37 — The growing cost of AI tools Subscriptions, token limits, model selection, AI orchestrators, and the need to use expensive tools more intentionally.01:27:50 — What successful AI adoption requires Starting with focused pilots, training scientists and technologists together, and treating implementation as organizational change.01:30:26 — The AI quirks that still frustrate users Hallucinated information, ignored writing instructions, stylistic habits, and poor awareness of time and context.The episode's timestamped themes and examples are documented in the supplied summary. The broader discussion covers AI from literature mining and molecular design through clinical development and post-market monitoring. Resources Mentioned Thibault Geoui's LinkedIn profile Tech & Drugs PodcastMIT NANDA study on generative AI implementation and return on investment Insilico Medicine as an example of a digital-native tech-bio company AI is already changing how scientific work gets done. The bigger question is whether organizations can redesign their workflows, train their teams, and maintain the human oversight needed to use it well.Listen to the full episode for a practical look at AI in drug discovery, drug development, and digital pathology.Support the showGet the "Digital Pathology 101" FREE E-book and join us!

Este podcast está patrocinado por Qonto.Si tienes una empresa, sabes que uno de los principales retos es poder mantener el control y ver claras tus finanzas. Pagos por un lado, cobros por otro, facturas en otra plataforma… Con Qonto, centralizas todas las finanzas en un solo lugar: cuenta de empresa remunerada, tarjetas para ti y para tu equipo, gestión de gastos y facturación integradas. Crear tu cuenta, aprobar un gasto, emitir una factura… todo rápido, en una misma solución. Obtén la claridad y el control financiero que necesitas. Abre tu cuenta hoy y empieza gratis.https://qonto.com/es¿Puede la inteligencia artificial reducir a la mitad el tiempo necesario para desarrollar un medicamento? Pedro Coelho, fundador de Biorce, explica cómo su compañía está construyendo un sistema operativo de IA capaz de acelerar el diseño y la gestión de ensayos clínicos.En este episodio hablamos de un sector en el que llevar un medicamento desde el laboratorio hasta la farmacia puede tardar 12 años y costar miles de millones. Pedro cuenta cómo Biorce puede convertir procesos que antes requerían documentos de más de 100 páginas, equipos de 20 personas y meses de trabajo en tareas realizadas en minutos. También profundizamos en los modelos especializados de IA, AlphaFold, la simulación de moléculas, la seguridad de los datos farmacéuticos y el futuro de una industria en la que las máquinas podrían comunicarse directamente con los reguladores. Pero la historia de Biorce también es personal. Tras perder a su padre por un melanoma y comprobar cómo los retrasos podían decidir quién accedía a tiempo a un tratamiento, Pedro dejó su anterior empresa para intentar reconstruir desde cero el sistema de ensayos clínicos. Desde entonces, Biorce ha levantado una ronda de 52,5 millones, ha firmado contratos multimillonarios y aspira a convertirse en la infraestructura tecnológica de las principales farmacéuticas.

Welcome to episode #1044 of Thinking With Mitch Joel (formerly Six Pixels of Separation). Steve Brown has spent more than twenty-five years helping organizations prepare for the future of technology. A former Senior Director and in-house futurist at Google DeepMind during the launch of AlphaFold, former Chief Evangelist and Futurist at Intel, entrepreneur and advisor, Steve has worked with global brands including Nike, JPMorgan Chase, Samsung, Disney and more to navigate digital transformation and the rapidly evolving AI landscape. His latest book, The AI Ultimatum - Preparing For A World Of Intelligent Machines And Radical Transformation, argues that AI is not another technology deployment but a fundamental organizational transformation that will reshape business over the next decade. In this episode, Steve explains why simply licensing AI tools is only the beginning, outlining the three stages organizations must navigate as they evolve toward becoming truly AI-first. We discuss the growing role of AI agents and digital employees, why leaders must rethink workflows instead of simply automating existing processes, and how organizations can prepare employees for a future where humans and intelligent machines work side by side. Steve also explores the importance of data strategy, change management, and responsible AI governance, while tackling difficult questions about surveillance, workforce anxiety, human creativity, and whether AI represents another technology cycle or something fundamentally different. Throughout the conversation, he makes the case that the real challenge is not adopting AI for efficiency alone, but reimagining how organizations create value in a world where intelligence itself has become programmable. Enjoy the conversation… Running time: 1:01:14. Hello from beautiful Montreal. Listen and subscribe over at Apple Podcasts. Listen and subscribe over at Spotify. Please visit and leave comments on the blog - Thinking With Mitch Joel. Feel free to connect to me directly on LinkedIn. Check out ThinkersOne. Here is my conversation with Steve Brown. The AI Ultimatum - Preparing For A World Of Intelligent Machines And Radical Transformation. Steve's Blog. Follow Steve on LinkedIn. Chapters: (00:00) - Navigating the AI Transformation. (06:06) - The Three Steps of AI Adoption. (08:47) - AI in Journalism: The Human-Machine Collaboration. (12:02) - Consumer Perception of AI-Generated Content. (18:03) - The AI Ultimatum: Embrace or Be Left Behind. (21:10) - The Role of Responsibility in AI Adoption. (24:00) - The Future of Digital Employees. (29:42) - The Future of Remote Work and AI Avatars. (30:05) - Managing Digital Employees: A New Paradigm. (32:35) - Types of AI Agents and Their Impact on Work. (34:34) - Evolving Roles: From Accountants to Financial Advisors. (37:09) - The Burnout Dilemma: Technology vs. Human Capacity. (39:11) - Involving Employees in AI Design Processes. (41:03) - Resistance to AI: Fear of Replacement and Identity. (44:42) - Leadership Perspectives on AI Integration. (46:36) - Creating Super Teams: The New Managerial Role. (48:04) - Case Study: Nvidia's AI Workforce Strategy. (50:10) - Early Successes in AI Deployment. (52:19) - Surveillance vs. Performance Measurement in AI. (56:25) - The Need for a Healthier Work Ecosystem. (58:24) - A Shift in Thinking: The Rise of OpenClaw.

In this episode, Professor Paola Cinnella - Professor of Fluid Mechanics at Sorbonne University and Director of the Sorbonne Cluster for Artificial Intelligence (SCAI) - joins Neil to discuss her path from classical fluid mechanics and high-order numerical methods into uncertainty quantification, Bayesian methods, data-driven turbulence modeling and AI for Science.Paola has built a career at the intersection of CFD, compressible and turbulent flows, dense gas dynamics, uncertainty quantification, robust optimization and machine learning. We discuss academic careers, dense gases, RANS uncertainty, AirfRANS, surrogate modeling, scientific publishing, education in the age of AI, and the idea of the "centaur scientist".Key topicsFluid mechanics, CFD and high-order schemesDense gases, real-gas effects and expansion shockwavesUncertainty quantification and Bayesian methodsRANS turbulence-model uncertaintyAirfRANS and CFD datasets for machine learningTurbulence modeling vs surrogate modelingScientific publishing and ML-for-CFD standardsSCAI and AI for ScienceEducation, ChatGPT and centaur scientistsPapersQuantification of model uncertainty in RANS simulations: A review - Heng Xiao, Paola Cinnellahttps://doi.org/10.1016/j.paerosci.2018.10.001Discovery of Algebraic Reynolds-Stress Models Using Sparse Symbolic Regression - Martin Schmelzer, Richard P. Dwight, Paola Cinnellahttps://doi.org/10.1007/s10494-019-00089-xBayesian estimates of parameter variability in the k-epsilon turbulence model - W.N. Edeling, P. Cinnella, R.P. Dwight, H. Bijlhttps://doi.org/10.1016/j.jcp.2013.10.027AirfRANS: High Fidelity Computational Fluid Dynamics Dataset for Approximating Reynolds-Averaged Navier-Stokes Solutionshttps://arxiv.org/abs/2212.07564Data-driven turbulence modeling - Paola Cinnellahttps://arxiv.org/abs/2404.09074Direct numerical simulations of supersonic turbulent channel flows of dense gases - Luca Sciacovelli, Paola Cinnella, Xavier Gloerfelthttps://doi.org/10.1017/jfm.2017.237LinksPaola Cinnella named Director of SCAIhttps://scai.sorbonne-universite.fr/news/paola-cinnella-new-directorSCAIhttps://scai.sorbonne-universite.fr/Paola Cinnella - HAL publicationshttps://cv.hal.science/paola-cinnellaPaola Cinnella - Google Scholarhttps://scholar.google.com/citations?hl=fr&user=wBRA0JAAAAAJERCOFTAC SIG 54 - Machine Learning for Fluid Dynamicshttps://www.ercoftac.org/special_interest_groups/54-machine-learning-for-fluid-dynamics/master-of-science-internships/Chapters00:00 Podcast intro00:39 Introducing Prof. Paola Cinnella03:28 Conversation begins03:56 How Paola found fluid mechanics07:09 Moving from Italy to France08:37 High-order schemes and compressible flows09:30 Building an academic career12:06 Dense gases and uncertainty quantification15:16 Expansion shockwaves and real-gas effects19:17 Returning to Paris and academic mobility24:52 Academia, passion and persistence27:51 Bayesian methods and turbulence uncertainty30:47 Learning statistics across disciplines33:07 LearnFluidS, AirfRANS and CFD datasets36:33 Skepticism and physics in ML turbulence modeling40:41 Could ML lead to a universal turbulence model?42:59 Turbulence models, surrogate models and RANS45:03 Why LES alone cannot solve optimization47:15 Multi-fidelity modeling49:08 What Computers & Fluids looks for in ML-for-CFD papers54:05 CFD metrics vs machine-learning metrics57:13 Overselling, publication pressure and quality62:22 SCAI and AI for Science66:07 Cross-disciplinary AI for Science69:26 Education in the AI era72:44 Critical thinking and AI outputs78:15 AI as a companion, not a replacement81:42 AlphaFold and the future of discovery83:43 Training centaur scientists85:11 Closing thoughts

This episode is sponsored by Notion. Learn more about Notion's Developer Platform today at https://notion.com/mlstProtein folding stalled biology for fifty years. A sequence of amino acids dictates a three-dimensional shape, but reading that shape meant a year and roughly $100,000 of crystallography per structure. Then AlphaFold 2 won CASP14 so decisively the organizers called the problem essentially solved.In this documentary cut, John Jumper, who shared the 2024 Nobel Prize in Chemistry and has since left DeepMind for Anthropic, walks Tim Scarfe through what the system did and, more interestingly, what it did not. The architecture gets a proper dissection: MSAs, the Evoformer, invariant point attention, the FAPE loss, and Jumper's correction of the equivariance story, which ablations valued at roughly 2.5 of 30 GDT points rather than the whole win. He is blunt about the limits. AlphaFold predicts one experiment extraordinarily well; it is not a model of the cell, it does not capture dynamics, and on a given drug target it is "wrong nine times out of ten."From there: the AlphaFold Database of 200M+ predicted structures, AlphaFold 3 and ligands, Isomorphic Labs, and Jumper's quarrel with the bitter lesson, where finite data and human hypotheses still matter. Emmanuel Nji of BioStruct Africa closes the film on what changes when work that took years now takes months, and on training the next thousand structural biologists across Africa.---TIMESTAMPS:00:00:00 Cold open: predicting nature with a button press00:01:03 The protein folding bottleneck and CASP00:04:39 The Nobel, the database, and the move to Anthropic00:05:50 Sponsor (Notion) and framing: what AlphaFold does not claim00:07:39 Proteins as self-assembling nanomachines00:12:24 From structures to biology: drug discovery and Midnolin00:17:37 The humility of AlphaFold: a narrow predictor00:22:18 Inside the architecture: Evoformer, IPA and FAPE00:30:20 Ruthless empiricism: ablations and 100x in data00:35:20 Predict, control, understand00:40:00 Against the bitter lesson; AlphaFold 3 as diffusion00:45:07 Intelligence, representations and AGI00:49:23 Epilogue: AlphaFold in Africa00:52:16 Closing: the case for hybrid science models---REFERENCES:organization:[00:01:55] Critical Assessment of Structure Prediction (CASP)https://predictioncenter.org/[00:04:39] The Nobel Prize in Chemistry 2024https://www.nobelprize.org/prizes/chemistry/2024/summary/[00:05:18] BioStruct Africahttps://www.biostructafrica.org/[00:18:03] Isomorphic Labshttps://www.isomorphiclabs.com/paper:[00:03:09] AlphaFold Protein Structure Databasehttps://doi.org/10.1093/nar/gkab1061[00:17:25] Accurate structure prediction of biomolecular interactions with AlphaFold 3https://www.nature.com/articles/s41586-024-07487-w[00:22:18] Highly accurate protein structure prediction with AlphaFoldhttps://www.nature.com/articles/s41586-021-03819-2[00:23:10] Midnolin promotes degradation of substrates independent of ubiquitinationhttps://doi.org/10.1126/science.adh5021[00:27:00] Improved protein structure prediction using potentials from deep learninghttps://www.nature.com/articles/s41586-019-1923-7tool:[00:03:09] AlphaFold Protein Structure Database (EBI)https://alphafold.ebi.ac.uk/[00:45:55] AlphaEvolve: a coding agent for designing advanced algorithmshttps://deepmind.google/blog/alphaevolve-a-gemini-powered-coding-agent-for-designing-advanced-algorithms/other:[00:39:40] The Bitter Lessonhttp://www.incompleteideas.net/IncIdeas/BitterLesson.html---ReScript: https://app.rescript.info/share/d8cde5c221fb71e2c0f5aafe94f90dfaDisclaimer - not sponsored, editorial with us - we filmed it at GDM, London

When Demis Hassabis pitched DeepMind to a few venture capitalists back in 2010, the business plan was almost comically audacious. “Step one: Solve intelligence. Step two: Use it to solve everything else,” he recalls in a conversation at Stanford Graduate School of Business with Stanford University President Jonathan Levin. “And people were quite confused. But we really meant it.”Sixteen years later, the “broad arcs” of that plan have gone “unbelievably well,” says Hassabis, a chess prodigy turned video game developer turned neuroscientist turned Nobel Prize-winning AI pioneer. Today he's on a mission to create “the ultimate tool for science,” building on his decision to give away AlphaFold, the groundbreaking AI system that predicts the structures of proteins. The future, Hassabis says, is just around the corner: “Ten years from now, I think we'll realize that we were standing in the foothills of the singularity now.”AI@GSB, the Dean's Applied AI initiative at the Stanford Graduate School of Business (GSB), and Stanford Medical School hosted a conversation with Demis Hassabis, Co-founder and CEO of Google DeepMind, on the frontier of artificial intelligence and what it means for how we live, work, and flourish.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

On the Science pod, we've been covering a lot of the ground on how AI is revolutionizing STEM, but one of our favorite off the record topics since our launch is which field is harder to accelerate: math, bio, or physics? Today we're back in Materials Science land with Radical — Unlike biological molecules that can be represented (and predicted!) by token strings, the success of materials involve many more macro complex variables like supply chains, microstructures, and manufacturing processes. If you recall the LK99 drama of 2023, while the basic ingredients were known, part of the confusion came from the lack of disclosure around manufacturing, and therefore defeated reproducibility. There is probably no "one-shot" model capable of designing a material that works perfectly at scale.How Radical is accelerating materials discovery >10x the pace of DARPA/GE MACHJoseph Krause is a materials scientist through and through. And after spending his career watching industries stall out waiting for better materials, he founded Radical AI to do something about it.We recently sat down with Joseph to talk about Radical AI, materials discovery, self-driving labs, and the future of AI science. Joseph did not sugar coat anything: accelerating the materials discovery pipeline is a hard problem. But it's one that he strongly believes we need to invest in, for the future of consumer products, aerospace, computing, and defense, and get them into every day use:“We count it as a discovery when you pick up your phone and there's a new material sitting inside of it.”How does Joseph plan on accelerating the rate of discovery? To understand this, it's important to understand why this is such a hard problem in the first place. The first thing to keep in mind is that the material that is manufactured is far more than a chemical formula going into it. The process of mixing, annealing, growing, or generating the final material can result in wildly different outcomes. The entire materials discovery process, both from early discovery to large scale manufacturing, needs to be understood and characterized.The Self-Driving LabThis philosophy has grown into a key insight at Radical AI: The construction of the self-driving lab. This lab is one that is not just automated, but in fact uses an “AI scientist” that combines scientific knowledge, computational techniques, and human intuition to generate and test hypotheses in an automated lab. Creating an AI scientist was key to making Radical's self-driving labs work, since Joseph argues that no single AI model can one-shot materials.“In materials, the ground truth is the material itself. You have to be able to test it and characterize it.”Joseph talked at length about the self-driving labs at Radical. Joseph argues that experimental data is the true “moat” in this industry. An SDL functions as a closed-loop system where an AI scientist generates hypotheses, and automated robotics synthesize and characterize materials, running research campaigns in parallel rather than serially. The successes here were both on the automation side and on the science side. Radical has managed to scale their alloy discovery pipeline up to producing and characterizing 1200 alloys in six months — this nearly 10x speedup over the DARPA/GE MACH program that aimed to create 500 new alloys in a year. Joseph claims they can scale this up even more and estimates they can produce a hundred new alloys tested and characterized in a day. A truly new paradigm in high-throughput alloy experimentation.On the science side, their AI scientist proposed and tested 300 new materials, ten of which were found to have novel state-of-the-art properties that are already being further developed for commercial applications. The robustness of this first materials campaign reinforces Joseph's claim that the moat is the lab and data.“It's moved into elemental families or alloy families no one has ever published on before.”Interestingly, Radical's AI scientist has made some novel discoveries, expanding into elements that just were not explored prior. This is fascinating from a scientific perspective, but it's also important for helping reduce supply chain bottlenecks for vital industries!Joseph spent a lot of time in D.C. before founding Radical, and he's clear-eyed about the competitive threat. China's centralized model lets it stand up manufacturing hubs and immediately scale new materials from lab to production. We can't replicate that, and Joseph is very clear we shouldn't try. But we do need an answer. For Joseph, that means transforming the scientific workforce, investing in self-driving lab infrastructure at the national lab level, and leaning hard into public-private partnerships.“Now imagine every scientist in the United States doing 10 times the research output. That's fundamental. That just changes the trajectory of discovery.”Before we close, we'd like to give a shout out to Joseph and Radical for publishing and open sourcing much of their internal tooling pipeline. This includes:* TorchSim (preprint, blog): an open-source PyTorch-based MD simulation framework, which has been spun off into its own non-profit.* MATRIX/MATRIX-PT (preprint, blog): An open-source dataset for benchmarking autonomous self-driving labs (MATRIX), along with with an open source model based upon this dataset (MATRIX-PT). We could talk about this extensively, but a fun data point is that improving reasoning in the area of materials also improved reasoning for biological systems! This is a truly unexpected result.Big shout-out to the Radical team for sharing their work!Materials discovery has been stuck on a 20–30 year timeline for generations. Joseph thinks that's about to change, and Radical AI is putting that thesis to the test in the lab, one sample at a time.We had a great time talking with Joseph. We hope you give it a listen!Timestamps* 0:00 Introduction to the challenges of AI in material science* 0:52 Welcome and introduction to Joseph Krause and Radical AI* 1:38 Why Radical AI is different: The focus on experimental data and Self-Driving Labs (SDLs)* 6:19 The process: Candidate generation, synthesis, and characterization* 11:05 The application of exotic alloys in extreme environments (aerospace and defense)* 13:20 Barriers to entry: The slow process of qualification and manufacturing* 16:06 Supply chain constraints in material science* 19:24 Human-in-the-loop: Training the AI using scientific intuition* 20:35 The engineering challenges of automating a laboratory* 23:17 Defining the “Self-Driving Lab”: Research campaigns vs. just automation* 24:39 Mechanical challenges: Handling high-temperature samples* 27:41 Future scaling plans and the “Vertical Integration” strategy* 30:08 Validation timelines for high-tech industries (semiconductors, aerospace)* 31:47 The active learning loop and handling “negative results”* 35:32 AI exploring elemental families beyond human bias* 39:13 Throughput targets and the difference between AI and human exploration* 43:52 Why the dataset size is less critical than the quality of experimental feedback* 46:20 Addressing the lack of an “AlphaFold” for materials* 53:49 War stories from the lab: Building the infrastructure* 58:12 The shift in industry sentiment toward SDLs and tool interfaces* 1:01:14 Geopolitical considerations and the race in material science innovation* 1:06:12 Calls to action for ML and AI engineers: Rethinking the scientific stack* 1:09:53 The Matrix model and using VLM for scientific knowledge extraction* 1:13:10 Why Radical AI is open-sourcing their work This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.latent.space/subscribe

En este episodio de Mundo Futuro exploramos cómo la inteligencia artificial está entrando en nuevas capas de la vida cotidiana, la creatividad y la ciencia. Primero hablamos de Text to Song, la tendencia viral que convierte conversaciones reales en canciones usando IA. Chats de WhatsApp, peleas familiares, rupturas amorosas y dramas cotidianos se transforman en música, abriendo una nueva pregunta: ¿la creatividad del futuro será más técnica o más emocional? Después entramos a la historia de Demis Hassabis, fundador de DeepMind, protagonista del libro The Infinity Machine y una de las mentes más importantes de la inteligencia artificial moderna. De los videojuegos y Atari, al ajedrez, Go, AlphaGo, AlphaFold y el Premio Nobel, su historia muestra cómo la IA pasó de ganar juegos a resolver problemas científicos reales. También hablamos de Isomorphic Labs, el nuevo proyecto derivado de DeepMind que busca acelerar el desarrollo de medicamentos con inteligencia artificial. Una empresa que acaba de levantar miles de millones de dólares con una ambición enorme: usar IA para transformar la medicina y, eventualmente, curar enfermedades que hoy parecen imposibles. Un episodio sobre música viral, creatividad artificial, ciencia computacional y el tipo de inteligencia que podría cambiar el futuro de la humanidad. Learn more about your ad choices. Visit megaphone.fm/adchoices

从炼金术士熬煮尿液寻找磷火，到AI在0.96埃的精度内破解蛋白质折叠——化学，这门曾经靠盲目试错、烟熏火燎的古老学科，正经历一场前所未有的智能革命。AlphaFold 2一年完成了人类几万年才能做完的事，药物设计、材料合成、碳中和……微观世界的大门被算力彻底撞开。这不仅是化学的终极篇章，更是属于每一位年轻人的“造物主时代”邀请函。科学没有终点，你的好奇心，就是下一支魔杖。

Can we put the data centers in space? Neil deGrasse Tyson and co-hosts Chuck Nice and Gary O'Reilly map out the future of human habitation, research, and industry in low Earth orbit with Ariel Ekblaw, founder and CEO of the Aurelia Institute. NOTE: StarTalk+ Patrons can listen to this entire episode commercial-free here:  https://startalkmedia.com/show/the-future-of-space-stations-with-ariel-ekblaw/ Thanks to our Patrons Richard Morgan, Kamila B, Douglas L, Izzi, Robert Lee, Alfredo Giachino, Andy Reinhart, Kacie Blu, Kimberly Freshour, Atmosphere327, Chris Rose, Gsjdhdbdh, Michael Nel, Morgan Shatz, Alfredo Morales, Petr Vlk, FMG, BryN S, Gunner Ford, Ori, Kimberly, David Kříž, Brendan Hanson, Catherine Westbrook, CT Vaughan, Jon West, Luc Gauthier, Smlamartina, DetroitLarry, Dave, Maarten Bakker, Monthen, Alixandria Taylor, Joe Maron, Ben Canty, Stephen Harris, Nandini and Nitin, Angel, Sascha975, Jalene Tangen, Courtney, Marcus, Jorge Coria, Emilio Jaen, Matt Tatro, Nicholas LaLonde, Mark Nicholson, Akira Stiebeling, Brandon Hill, Delphini Papadopoulos, Mauricio Valle, Mark Entel, Leif Callesen, Steven Crofts, Anthony Lofgren, Huzaifa Shabur, Kyle Has the Biggest Shlong in Media, Chase Phyfe, Davin, Greg Gray Lord of Hotdogs, Jeff Kolander, Gosh Dane It

How did a teenage video game designer from London become a Nobel Prize-winning scientist behind one of the most consequential technology efforts in history? Sebastian Mallaby is a senior fellow at the Council on Foreign Relations and author of the new book, The Infinity Machine: Demis Hassabis, DeepMind, and the Quest for Superintelligence which provides an in-depth look into one of the greatest minds behind artificial general intelligence. In this episode, Sebastian and Greg discuss how Hassabis's early immersion in game design and neuroscience shaped his unique approach to artificial intelligence, why groundbreaking science is increasingly happening outside academia, and the tension between scientific discovery and corporate strategy.  *unSILOed Podcast is produced by University FM.* Episode Quotes: Why AI is becoming an ‘infinity machine' 03:01: It struck me that two breakthroughs in AI pointed to more to come. And these were AlphaGo and then AlphaFold. And what these two things had in common was—you had a sort of massive combinatorial space in both cases. So with Go, because it's a nineteen-by-nineteen board, the very first move, there's three hundred and sixty-one choices, then there's three-sixty for the second one. If you multiply that out, you pretty soon get to a search space which is sort of, you know, approaching infinity in terms of the number of possible permutations in the game. And with proteins, the way they can fold is even bigger. And so in both of these challenges, effectively, you have a machine that can make sense of near infinity of data, so an infinity machine. And once you have that, I figured, well, it's niche for the moment, but it may not stay niche forever. The “Third Way” that helped Google overcome the innovator's dilemma 44:06: The third way is you have a skunkworks, like DeepMind in London, which is a separate entity, and you're letting them kind of be the new policy in waiting, like the fightback policy in waiting. And you don't activate it. But when the moment comes when your competitor embraces the new technology, and you're in danger of falling foul of the innovator's dilemma, then you've got the answer because you've been keeping it ready, and you bring it in, and then you fight back fast. How DeepMind helped Google catch up in the AI race 42:54: How did they, in the space of two and a half years, go from the merger announcement to Gemini 3.0, which was better than the ChatGPT rivals? The key to it is that DeepMind had that top-down strike-team methodology, which came from the video game development world, and they imposed that on the Mountain View team, which was much more bottom-up and kind of inchoate in the research process. And that's what generated Gemini 3.0. That's how they got ahead. Show Links: Recommended Resources: Sebastian Mallaby | unSILOed AlphaGo AlphaFold Gödel, Escher, Bach by Douglas Hofstadter Geoffrey Hinton Mustafa Suleyman Guest Profile: Senior Fellow Profile at Council on Foreign Relations Professional Profile on LinkedIn Guest Work: The Infinity Machine: Demis Hassabis, DeepMind, and the Quest for Superintelligence  The Power Law: Venture Capital and the Making of the New Future  More Money Than God: Hedge Funds and the Making of a New Elite  The Man Who Knew: The Life and Times of Alan Greenspan Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Editor's note: In our first BioHub pod with Priscilla and Mark they discussed their acquisition of EvoScale, led by Alex Rives, who is now Head of Science at BioHub. With ESM-1 they trained language models on millions of protein sequences drawn from across life, with a simple “next token” objective: predict the amino acids that have been randomly masked out, based on the context of the rest of the sequence. But they soon found that these models also learned biological structure and function, including properties the model had never been explicitly shown AND that this ability scales predictably with compute, leading to ESM2 and ESM3.Today, Alex announced ESMFold 2, an open scientific engine to power prediction, design, and discovery across protein biology.Building on Cryo-EM data (discussed in the CZI pod), ESMFold2 reports state of the art performance on protein interactions, especially antibodies, a critical modality for therapeutics, and evidence that inference time scaling is also working across five targets in cancer and immunology.In a nod to that other famous AI x protein folding project, they are also releasing an atlas of 6.8 billion proteins, and 1.1 billion predicted structures, which you can play around with on their website. We are honored to work with them for this huge release!One of the refrains we've heard on the Science pod has been that protein folding, materials design, cellular biology, etc. are very different problems from Language Modeling. They definitely are. Yet Alex Rives and the ESM team at BioHub just released a preprint and model, demonstrating that vanilla BERT-like transformer models trained on sufficiently large and diverse data sets can beat specialized models like AlphaFold3 on some of the hardest protein-related problems. Andrew White had a great segment in our first LS-Science episode that explained how mind blowing AlphaFold2 was when it was released in 2020: it suddenly solved problems on a GPU on your desktop that DESRes had built custom-ASIC supercomputer clusters to solve. John Jumper and Demmis Hassabis received the Nobel Prize in Chemistry for this work.AlphaFold2 took advantage of an very clever observation: if multiple species co-evolve pairs of mutations, this implies that the mutations correspond to parts of the protein that are close in 3d space. This is usually shorthanded as MSAs (multi-sequence alignments), and is the key insight which makes AlphaFold2 so effective.Like other inductive biases, however, it hurts generalization.Scale-pilled before it was coolIf you take a look at the timeline for scaling laws for LLMs and release of structure prediction models, the ESM team notably doubled down on their MSAs-be-damned approach after AlphaFold2 released. This obviously requires a great deal of belief in the scale hypothesis.Why the conviction?ESM developed at a time when many of the scaling laws and the “Bitter Lesson” were proving increasingly correct. AlphaFold2's wild success must have been both exciting and bitterly disappointing. But using MSAs mean that the model is is dependent on training data that contains MSAs in order to be accurate in a given domain. For things like antibodies that don't have MSAs to train on, AlphaFold tends to do poorly.ESM takes a different approach: learn the relationship between different proteins by unsupervised training on as much diversity as you can find (sound familiar?) and then correlate that back to structures know from the Protein Data Bank (PDB) and other sources. In other words, a World Model.World Model for proteins“World Model” is a hype term that I define like this:Use unsupervised training to learn abstract patterns from the data:* The abstraction should be semantic - novel constructions represent things that obey the rules of the real world* The abstraction should be compositional - recombining different patterns leads to novel and often valid constructions* The abstraction should support generalization - it predicts things in the real world it wasn't trained on Once you have a world model, you can attach “heads” to it for downstream tasks: predict properties of a protein, decompose its functional features, or search the representation for proteins that meet design criteria. The two big models BioHub just released under MIT license map directly onto this:* World model → ESMC (a model trained on 2.8 billion sequences)* Structure-prediction head → ESMFold2One of the interesting ways the world model can “predict things” is to generate proteins sequences and then measure the predicted properties, such as binding affinity, in the lab. Alex talks in the episode about validating some of the harder molecules they predicted in the wet-lab. Very cool!Another way is to use mech-interp techniques such as Sparse Auto Encoders (SAEs) to extract semantic features from your model, and then find novel features that predict unknown biology. I won't spoil this part for you: it was one of the highlights of the episode for me!A cell is a computerWe have all heard that genes are like computer programs, but usually the analogy fizzles after that. Of course genes are transcribed into RNA and RNA is translated into proteins, so genes are programs for building proteins, but that carries the analogy only to “binary digits are programs.” Here's a better analogy: you can think of the cell nucleus as a storage device / storage controller, the ribosome as a JIT-compiler and runtime, and the semantic features that we learn from our world model via SAEs as functions, proteins as processes that interact together in workflows (signalling pathways) to produce behaviors and outputs (phenotypes). Like functions, the SAE features have a hierarchical composition from local, secondary and tertiary structures (mimicing protein structure), but also motifs that are conceptual, such as membrane integrations, disordered regions and disulfide bonds. As we learn to compose these features we into novel protein designs, we move further towards programmable biology. Alex goes into much more detail about this in the episode, as well as:* Principles for new data collection* BioHub's vision* Modeling the cellEnjoy!Full Video podcastplease like and subscribe!* X: https://x.com/alexrives* LinkedIn: This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.latent.space/subscribe

How is AI transforming accessibility for indie authors — and why should you care even if you consider yourself able-bodied? What happens when the tools designed to help people with disabilities end up making everyone's creative business better? Jeff Adams, accessibility expert and romance author, explores how AI is opening doors that were previously closed. In the intro, Spotify Audiobook Innovations; The Economics of Convention Life [The Indy Author]; Friction in your Author Business [Self-Publishing with ALLi]. Today's show is sponsored by Draft2Digital, self-publishing with support, where you can get free formatting, free distribution to multiple stores, and a host of other benefits. Just go to www.draft2digital.com to get started. This show is also supported by my Patrons. Join my Community at Patreon.com/thecreativepenn Jeff Adams is the author of YA thrillers and gay romance, and the co-author of Content for Everyone, a practical guide for creative entrepreneurs to produce accessible and usable web content. You can listen above or on your favorite podcast app or read the notes and links below. Here are the highlights and the full transcript is below. Show Notes How ending a long-running podcast made space for more writing — and how to know when it's time to let go of a good thing What accessibility really means for indie authors and why your digital content might be excluding part of your audience How AI agents like Claude Cowork are removing physical and cognitive barriers for authors with disabilities, chronic pain, or limited energy The culture of shame around AI use in the writing community and why blanket anti-AI statements can be ableist Practical tools including NotebookLM, ElevenReader, and ChatGPT for marketing copy, metadata management, and multimodal research Exciting futures in personalised reading, real-time translation, and AI browser agents that could change how everyone interacts online You can find Jeff at JeffAdamsWrites.com. Jeff also now has a SubStack at contentforeveryone.substack.com Transcript of the interview with Jeff Adams Jo: Jeff Adams is the author of YA thrillers and gay romance, and the co-author of Content for Everyone, a practical guide for creative entrepreneurs to produce accessible and usable web content. Welcome back to the show, Jeff. Jeff: Thanks so much, Jo. It's good to be back. Jo: It is. You were last on the show in March 2023, so over three years ago now. Give us a bit of an update on your writing and publishing business and what it looks like at the moment. Jeff: Sure. I think the biggest thing that happened is that my husband Will, who is also a writer, we ended the Big Gay Fiction Podcast at the end of 2024, after 470-something episodes. It was basically time to do that. So we both focused on writing from that point. In 2025 we had some of our biggest successes in getting writing out into the world. I refound my groove—my difficulty in writing went away finally. We talked a little bit about that back in 2023 too. Will started a new pen name and started producing again, and it was really good to be able to move in that direction. Jo: Was this the hockey romance that really hit at the right time? Jeff: You know, I wish I could have capitalised more on Heated Rivalry when it came out, but I did get hockey books out, and I think I did get to ride that wave a little bit there too. Jo: Yes, and if people don't know about that, that was a super popular streaming series. Was that based on a book? Jeff: It was, yes. Rachel Reid was the author of that book and that series that then Jacob Tierney optioned and made into what fairly turned into a global phenomenon at the end of 2025. Jo: Yes, absolutely. Although I particularly liked Red, White and Royal Blue. That was the one I liked. Not so much into hockey. But anyway, I just wanted to ask you about the Big Gay Fiction Podcast. As you say, you did hundreds of episodes over many years. You and I met over podcasting. You've had lots of connections with people. You ended it, and I know you struggled with ending it, but it sounds like it went really well for you. So maybe you could talk a bit about— How do you know when it's time to end something—a good thing rather than something bad? Does that make more space for writing, essentially? Jeff: It absolutely did make more space for writing for both of us, in particular for me because I have a day job. I balance everything on the creative side with the day job. Will and I had been talking about it for over a year. It just was like, it's really time. After nine years, getting to that 470 mark, we thought about trying to get to 10 years and we thought about, if not 10, then getting to 500 and ending on a milestone. As we looked at everything in our creative business, it was like, this is fun, we enjoy it, but we're not getting as much out of it as we might be if we were actually also writing books, which we also really want to do. It became a time thing and what was the best use of the time. We absolutely miss it occasionally. The whole Heated Rivalry thing, I would've loved to have had episodes to talk about that on, but in the long run, it was worth it. Jo: I mean, one of the things with a podcast, particularly around fiction, was that it was a marketing angle for your fiction. This show is a marketing angle mainly for my nonfiction. So what did you replace the podcast with, in terms of book marketing? Jeff: It was really stepped-up email marketing. I'd always had a list. Will started a list, of course, as he started his new pen name. So it was really turning on that, focusing on that, getting some email marketing with a Bargain Booksy and a Fussy Librarian and a BookBub occasionally to do that work. To be honest, even though we covered things in our genre that if you like what we're talking about, you should like our books, there was never as much of a connection there as you'd want there to be. Even from that book marketing angle, these other things that we can do, it's also a better spend of the money to get those types of promos than it was to continue running the show. Jo: Yes, that is interesting. I mean, obviously I think about podcasting a lot since I have this one, and I put Books and Travel on a hiatus and that was meant to help my fiction and definitely didn't help my fiction sales. But I want to bring it back again because I love doing it. Do you have this hankering sometimes? Do you think you'd ever do the podcast again? Because you are also quite into all the technical stuff and all that. Jeff: It's possible. I've toyed with the idea of doing a short accessibility podcast geared towards creatives, tilting to the same audience that Content for Everyone does. Then I come back and look at the time—is my time better served writing new fiction or perhaps starting a Substack, which I also toy with the idea of, for accessibility stuff? So it bounces around in my head to do another show, but I haven't really decided to jump on that yet. Jo: Yes, and I think that waiting is really good. As you say, you quit a big thing and you don't have to rush to fill it again. I love that you guys are writing more books. So I wanted us to talk about that up front because I know people who listen to this show—I encourage people to start podcasts if you want to, but equally it can take a lot of time. So that's fantastic. Now, you mentioned accessibility, and I feel like the word can be quite difficult for people. So let's just start with a definition. What is accessibility? Why do you care and why should we care? Jeff: So accessibility is really about making sure that whatever the thing is, whether it's something out in the physical world or in the online world, that everybody has access to it. Access to the information, access to getting into a building or being able to cross the street appropriately, whatever that is—that the accessibility of the thing is high. So that regardless of who is approaching it, they can interact with whatever the thing is. If we put that into the digital world, it's about making sure that text on a screen can be perceived by anybody, whether they're trying to read it visually or if they're trying to read it through a screen reader or through a braille monitor. Whatever that is, they need to be able to interact with it, get the information they need, do all the functions of whatever it is on the screen. Check out on Amazon, check out at their favourite e-commerce place, be able to get the products in their cart, check out, et cetera. For creatives, it's about the things that we do: the websites that we build for ourselves, the e-commerce platforms that we use, our email marketing, our social media posts. Making all of that as accessible as we can so that we're not perhaps missing a part of our audience or our prospective audience from being able to engage with our work and in turn, hopefully, buy our books and enjoy our books and become a fan. This became important to me because of my day job. I hadn't really considered this—like, I think most people don't—until I started working at UsableNet. It's going to be 15 years I've been at that company come this autumn, and I really started to see the impacts because UsableNet is all about accessibility on the digital front. I really started to learn, being a project manager for them, what all of that meant and how it impacted people who couldn't buy something online, couldn't book a hotel room, couldn't book an airline ticket. It just really became something I got passionate about. I ended up writing the book because I realised that nobody talks to creatives about this. Nobody tells the independent author what they should do to help make their digital stuff accessible so that they don't miss people. I never expected my day job to interact with my creative side so much, but this certainly has over the last few years. Jo: I mean, has it got better? Like we said, you were on here three years ago. We did talk about some of the things around EPUB formats and taking off DRM and what we need to do on our websites—labelling images, for example, and that kind of thing. Do you think accessibility has gotten better? Jeff: I think the awareness of it has improved, both within the creative community and in the broader web ecosphere, that the awareness is better. There's so much knowledge that needs to go into creating something that is accessible. Sometimes there's so much that you have to think about with colours and alt tags on images and all the little bits and pieces, if it doesn't really come to muscle memory, it's easy for it to fall off. There's a survey that's done by WebAIM every year about the top one million homepages out in the universe, and they surveyed those for just the things that an automated scan can detect, which is a small portion of overall accessibility, and the number of errors across that top million actually ticked up this year. Even though there's all these laws around the world—people get sued all the time in the US—the number of errors ticked up for the first time in a few years. So I think the awareness is up, but I think being able to take action on it and make the time to take action on it isn't where it needs to be. Jo: So last time you gave us all those tips. I'll refer people back to that and also to your book Content for Everyone, which has got loads of great stuff in. I wanted to talk to you for this show because I was sitting watching Claude Cowork—now I use Claude Code a lot more—but updating 140 titles on IngramSpark, where me clicking things and there's like 15 clicks per record on IngramSpark updates for pricing, is an absolute nightmare. I was watching the AI do the work and I realised this isn't just saving me time, it's actually saving my wrist and my arm from repetitive strain injury. That's when I thought about this accessibility thing. As you mentioned, for example being physically accessible into a building, say someone's in a wheelchair, they can't necessarily get into a building if there's no ramp. I was thinking that for many years, being an indie author, being a writer online, there's also been these physical barriers because there's a lot of plumbing and clicking for us. So I wondered, starting with an attitude around a shift in who this is opening up to— How is AI starting to help people with these accessibility issues? Jeff: Yes, there's so much opportunity around this. We should note, just to timestamp this, that we're talking on 14th April 2026, because who knows what will change, even in an hour from now. I think Cowork was one of the first things that we saw, and that's only been out since the very top of this year. Being able to do actual agentic tasks. Other things have sort of gotten there, but Cowork really opened it up. You mentioned the repetitive stress that you would've had clicking all of those forms on IngramSpark across 140 books. But there's that type of stress, chronic pain, cognitive drain for somebody who may have some cognitive disability and trying to work through that form. The cognitive energy just might drain out and maybe knock them out for several days after trying to get through that, or the tasks take them multiple days to do. Someone who has lower vision, someone who's trying to work through that form with a screen reader—all of that draws energy, draws focus. Now we've got something where, with plain language, we could say something like: here's all my pricing information, I've logged into IngramSpark, go update these books. Obviously the prompt's going to be a little more than that, but in broad terms, that's what we're going to tell it. Jo: Hmm. Jeff: And being able to have it go through and do the thing. If it gets stuck, have it come back and say, “Hey, I've got trouble with this. Please help me.” That can just free up so much of the drains that people can have—the things that can take them out of doing the part of the work that they need to do for an author business. They can go write the book through whatever process you're going to use to do that, rather than getting caught up in something like having to update all those books on IngramSpark. Jo: You mentioned writing the book there. I have this real sense of being an able-bodied indie author in terms of my computer use and my ability to write a whole book, a 70,000-word thriller that I write regularly. We're all special in some way, but I do have a reasonably normal brain where I can do this work without too much strain. It's hard work, but I can do it. I meet people who are now using AI to help them write, to help them organise their work—maybe someone has dyslexia or ADHD or cognitive issues or pain—there's just so many things that I take for granted that don't affect me. I hear from people who, at this point in time in the community, are almost shamed for using AI to write. So I wanted to bring this up to discuss it under the terms of accessibility. Do you have any thoughts on that? Jeff: I have real difficulty with people who will say anything in the broad range of, “I don't need to use this thing, and therefore you should not either.” Which is adjacent to indie anti-AI speak that there is out there. Certainly we're living right now at probably the highest point that it's ever been, where more and more there's a sentiment towards not using AI for whatever the reason is. I totally respect that people can have concerns about the environment and about energy use and water use, et cetera. Not to mention all the other things that are on the more difficult side of AI. To shame someone who may not be able to put their story out there without the use of that AI, whichever one they're using, or to shame them because they're using AI to run part of their business—updating IngramSpark, doing other things like that—I think it can come down to there being some ableism there. Ther is some privilege behind that too, where they're just like, “I don't need this, and you shouldn't have it either.” I want to give people just a sliver of an idea of what this can mean for someone who is disabled and what AI can unlock for them. There is a person on LinkedIn that I follow whose name is Hannah Desmond. She's an ADHD coach and a former software developer, and very recently she posted this on LinkedIn. This is a paraphrase of what she said, but: having something that can meet you where you are and help you bridge that gap is what I think I have found so helpful about using AI. Here's what I keep coming back to. Without that support, I wasn't more motivated or more capable. I was just stuck. That's the bit that gets lost. We've been taught that struggling is how you know you're doing it properly. So when something reduces the struggle, it can feel wrong—even when it's the thing that actually makes the work possible. Because there's a difference between avoiding thinking and being able to think at all. I think that rounds it up. She's talking about her time as a software developer, but you can apply that to any realm of AI when we're thinking about trying to shame someone for why they may be using it. We may not know that they have a disability because we don't always share that part of ourselves. So I really feel strongly about that and how we are in this culture of shame. Jo: Yes. It drives me up the wall, actually. But I will also say: you don't have to have a disability or accessibility issues in order to use AI in whatever way you personally decide is okay—talking to the listeners now. I think Orna Ross from the Alliance of Independent Authors says it well, which is you should have your own AI policy. So you personally decide where your lines are, how it helps you, what you want to keep for you, and what you want help with. I was also thinking in terms of accessibility around money. Again, for many of us, professional cover design, professional editing, professional human-level translation, these are things that are pretty pricey for many people. So again, this makes it more accessible. One of the reasons we got into the indie way and being indie authors was to try and remove the barriers to entry to people who have been excluded from the environment of publishing. So, yes, it is really hard to talk about this, and yet that's why I wanted to talk about it, because— There's so many variables for each individual and there's no situation that's the same, really, is there? Jeff: No, not at all. The things that I may need to do my work in the most efficient way possible is different from the way that you're going to work, is different than the way my husband's going to work, is different than every other person and the way that they're going to work. Which is why any kind of blanket statement about “I don't need something and therefore you shouldn't need it either” can just be so problematic, because we have no idea what someone else is going through. Either it's a permanent part of their lives or maybe it's something that is happening temporarily with them where they might need to leverage other tools. Jo: Yes. Talking about that temporary, I think I really got the first sense of this when I had COVID the first time, which was really bad. I remember I was so sick, the only thing I could do was listen to an audiobook. I couldn't think, I couldn't read. It was really probably months of not having my brain back. Then the other thing that's happened as I age, as women age, is menopause kicks in and the brain fog is a real thing. I've heard from other people too who've said having Claude or whoever, an AI tool, to help with the brain fog is so important because otherwise I just wouldn't be able to gather my thoughts. Again, as you said— Even if we don't need these things now, it's quite likely we're going to need them at some point, given ageing, given the potential for injury and disease. I mean, we don't escape this alive, do we? Jeff: Yes, that's a great point because unless we're extremely lucky as individuals, we're all likely to have some sort of a disability in our lives at some point. I know for me, as I age and my eyes get more and more tired after being in front of a screen all day for work, and then whatever creative stuff I do in the afternoon on a book—when it comes near bedtime and I do want to read, I probably want to do that with an audiobook, much more audio, especially for any long reading project. That can also be like, if I have a long document or a long article to read, I am likely to give it to ElevenReader, let it load itself up, and then listen to it, because I take the information in better than trying to follow words across a screen. Jo: Yes. Jonathan, my husband, now also listens to a lot of academic papers on ElevenReader. Most of us will know it as where we publish some audiobooks from ElevenLabs, or you can also publish other things there. So it is super useful to think about what we can do with ElevenReader. Another thing that I found really useful recently is NotebookLM. On NotebookLM, there is a free tier. You can put various things in there and then create a custom audio. So this is something I've been doing as part of research. You can put in, say, 10 YouTube videos or some PDFs or your book or whatever, and then you can create a custom audio. Then I'll go for a walk and I'll listen to the custom audio, and then I'll go back and look at the detail of what it was. It gives me the framework of whatever I'm thinking about on a broader level, and then I can come back to the details. So again, it's this multimodal approach that can help us manage our energy, I guess. Jeff: And it's all about the managing of the energy, I think, too. That is a great way to think about the accessibility of it all. You mentioned a great use there for NotebookLM. That could also be putting your book in there and having it help you build a world bible or something like that. Or building marketing materials off of that. There's a lot of things now that NotebookLM can do in terms of helping you create FAQs maybe for a newsletter or for your website, and building video stuff off of the material that it has. So there's a lot of options there, and ever-growing options that can be useful for someone to manage any number of the things that they may need in their creative business. Jo: Yes. In fact, talking about Claude, there are a lot of Claude plugins now, skills and integrations. Shopify just released a Claude plugin and many of us now have Shopify stores. I have a lot of products with a lot of different variations and the metadata. There's so much metadata. And again, I'm just so pleased now that I can work with Cowork and get it to actually update directly into Shopify. In fact, coming back, you mentioned updating alt tags earlier. That's something again that AI could help you update—the back list of your alt tags on a website. I've now got my Cowork doing EPUBs so I could finally update all my EPUBs with back matter and all of this kind of thing. So I feel like perhaps we could go beyond accessibility to talk about amplification. All the things that we didn't do because it was too tiring and we just couldn't be bothered, or it would just be way too much work, that now it's opened up as a possibility because of these tools. Jeff: Absolutely. I mean, you look at a backlist as large as yours and the things that you're now able to do. I didn't know that Claude had a Shopify plugin. So the abilities that we have now to maybe do things in the business that we hadn't before. One of the things I've been working with Claude on is rewriting my website and creating a more proper website for Will. I'm really making sure that it is not only SEO prepared but also GEO prepared, with all the metadata and all the backend code schema that it needs so that LLMs can find me, can understand what I do, can understand the books, branch out to the other areas that it needs to. Doing that through WordPress would've been so much more difficult, even with Claude, that to be able to rewrite the site in a way that is going to let me manage it better so that I will do it on a more consistent basis. Whatever that thing is, we're now able to do these things. That could be updating keywords in Amazon or making sure we're aligned across all of the sales platforms that we might be on and things like that, that Claude can do and do well. Jo: Yes, I think marketing is just the killer app really for people, isn't it? I think most authors do not enjoy marketing. I find Claude better for creative work, for strategic work, for doing work through Cowork or Code, but— ChatGPT with marketing copy is very, very good. So I've actually been using that as we record this. I've got a Kickstarter launching next week, so I've been getting it to do ad copy and social media copy and all that kind of thing. This is stuff when you have to produce—give me 20 taglines, give me 20 hooks, give me another 20 and another 20. I mean, we just cannot do it as humans, right? Jeff: Yes, I have found GPT wildly helpful. I mentioned trying to get Bargain Booksy and Fussy Librarian promos. Jo: Mm. Jeff: And you have to give it the marketing hook, and it can't just be the blurb that's on Amazon—it's got to be something fresh, and they each have slightly different requirements. Having GPT—here's the blurb, give me a dozen different options—and then I may take pieces of all of them and create one of my own. But it reworks that much faster than my brain was ever going to try to find the right thing I want to give to Bargain Booksy. Jo: Yes, you are right. Or it says write this in 300 characters or less. Jeff: Yes. Jo: I do exactly the same. That kind of transformative work can be really good. In fact, there was somebody I know who has been rampantly anti-AI for years and then said, “Would this help me? I have to do a synopsis for an agent, so I've got this 100,000-word book and it needs to be a 10-page synopsis. How would I do that with AI?” So I was encouraging her to take each chapter and ask it to summarise the chapter, and of course read through it and everything. But I mean, doing a synopsis once you've actually written a book—that can be super useful. So I think what we're saying is— There are levels of need in terms of both the author and the audience. Then there are levels of your personal use from one end of the spectrum to the other in terms of how far you want to go in every area of the business. And in that way, it's just different for everyone. Jeff: Yes, and I think getting to that mindset shift that we were talking about a little bit—it can be so easy to dip your toes in. That one author came to you and said, “Do you think it could do this?” And I think that's the beginning exploratory area for perhaps anyone. People are going to hear us talk about this and it might inspire them to go try something that we've talked about. But these things, whether it's Claude or GPT or Gemini or whichever one it is, you can come to it and say, “I'm an author, I have X, Y, Z going on in my life”—whether that's a disability, whether that's a time constraint because you have a day job and maybe you have kids and a family that need your attention—”I have these time constraints, I want to do X, Y, and Z in my business. How can you help me with that?” It's going to tell you what it can do to help you with that. I would even say, if you have the ability to have multiples of these, you could ask the same question to GPT and Claude, and they're going to give you similar answers in some instances, but they may also have different ones because of the abilities that the different platforms have around these things as well. That can help you make that mindset shift of, “Well, now I see that it can do that. Could it also do this?” And then ask it if it could do that. Because I know for me, Jo, I've taken so much from you and your journey with Cowork that it's like, “Oh, she did that. I wonder if I could do this.” And all of that piles on top of itself. Then eventually I think your brain starts to think on its own, “Oh, I have to do this task. Can Claude maybe do this for me? Let's go find out.” Jo: Yes, and if it couldn't do it for you yesterday, you never know, it might be able to do it tomorrow. Jeff: Right? Because I haven't tested yet its new ability to actually use your computer. Jo: Mm. Jeff: And I'm curious what that might open up. Because one of the things that I've seen that I wish it would do is be able to take the EPUB that's on my drive and actually put it into a platform I'm trying to upload to. Cowork on its own hasn't been able to cross that barrier, but I wonder if with computer use added to that, if it could. Like, “here's the EPUB, upload that over there,” be able to pick it from the file picker, essentially. Jo: Yes. I think, well, a little tip for everyone: I wouldn't give access to your entire file system to the AI. Jeff: That's a good point too. Jo: Yes. I have a Claude folder in my drive and it only has access there. So if you put files in that drive, it might be able to do that. But I know what you mean. I have been using it to help me publish things in German on KDP. Now I can use the browser, so you can actually do that. In terms of uploading the actual file, I know what you mean. These things will change. As we record this, again middle of April, we are almost about to get the next models being Mythos, which might be Claude 4.7 Opus, or also ChatGPT has a new model coming, and these models are getting very powerful. With every shift they can do more things. So as you say, the very first thing to do is ask it, “I want to do this—what are my options?” And some of them, for example, doing an AI-narrated audiobook, ChatGPT and Claude don't do that. You want ElevenLabs or one of the other services for that, but they can tell you what your options are. So that's one thing, but I wondered if you have any thoughts on the gaps that you are seeing. You mentioned one there around file uploads, but— What do you hope might come and some of the things that might be exciting if they arrive? Because you never know, they might be here already. Jeff: There's certainly some movement in some areas. One of the things I'll share is, in March I was at the 2026 CSUN Assistive Technology Conference—CSUN is California State University, Northridge—and they've run this conference for some 40 years now. One of the sessions I went to was from Tara Maisel—I hope I'm pronouncing her last name right. She's a senior project manager in books accessibility at Amazon, and she was doing a session specifically on readability. She had all kinds of statistics and information about what goes into making something readable. One of the things she talked about with AI was the future of personalised reading. If you think about the Kindle app, for example, there's a lot of settings you can make there—font size, colours, brightness, text spacing. There's a lot of tools in there. She was pointing out that potentially readers don't even know what they actually need for the optimised visual reading experience. She sees a world where AI can perhaps do an analysis of your reading behaviour and then help you find the optimal settings. Maybe even multiple optimal settings for, say, if you were reading in a room that had daylight versus at bedtime, and the ways you might shift it. I was almost thinking of this like when you're at the optometrist and they're like, “Which lens is better—this one or that one?” Jo: Oh, sometimes that is very hard. Jeff: Yes. It's that AI could step you through that a little bit to help you find that optimal reading experience in that moment. And then it might even notice, potentially, if you're changing something in the way that you're moving through a page, that it might flag to say, “Hey, do we need to adjust something?” Some other areas that I think are really exciting, for everyone and perhaps particularly for people who are disabled and needing the support of some assistive technology, is what we're seeing in the browsers. OpenAI's Operator has been out for quite a while now, since sometime I think autumn of last year. Perplexity Comet has been around even longer. Then we've got browser extensions from Gemini and Claude that are available, that can let you just type natural language. You know, “Please go find for me jeans in this size that are on sale on this website. Find me the best price for blue jeans on this site and this size,” and it'll just go do it. Which can certainly speed things up for people in the disabled community to find things quickly, to spend time navigating less, and maybe ending up with the AI coming back and saying, “I found these five things. Which one would you like me to buy for you?” Or, “I found this one thing that you do need and it's waiting for you in your shopping cart.” The ability for that on the horizon is an amazing jump from an accessibility point of view. But really it's one of those things that accessibility will then help everyone because we can all just shop that way, if we choose to. These are early days for these browsers and these extensions. The other side of it comes back to basic web accessibility too, because I've seen these types of activities not work so well on a site that may not actually be accessible on its own. A great example is something I ran into with Claude Cowork about a month ago. I was testing to see if it could help me navigate and get things uploaded together for a site where I wanted to upload books, knowing again that it's not going to upload the actual file, but it could fill in the metadata from my master database of metadata stuff. There were areas on the site that it actually couldn't hit the button, because the site itself was also not functional to a screen reader. So there are gaps there. It's early days, but I really see that as an interesting future that'll really help people with disabilities—but again, help everybody too, just manage time better. Jo: I know exactly what you mean there. I've done some collaborative work with Claude Code when it's like, “I can't click the button,” and I'm like, well, I'll click the button—you fill in everything else. Jeff: Exactly. Jo: It's actually quite a funny situation. But goodness, coming back to IngramSpark again—these things need APIs. We need better functions. It's funny because I think a lot of traditional publishers have these APIs or backend upload things that you can do. I'm like, well, we need to get to that with these systems. But I think things will change. Another thing that I think has also shifted is the use of voice. Voice for dictation—it used to be with dictation that you would have to say “comma,” “open quote,” “new line,” and all of that. And you'd also have to make sense. Whereas now I feel like you can just dictate a whole load of things to these AIs and then say, “Tidy that up,” and they will do a lot more than the old situation. So I think voice will also help. Also automatic translation. I don't know if you know this about X, and if you're on X anymore, but just this week they've made it multi-language. So I can read tweets by people who've posted in another language in English. I can read something from Korean or read something that someone French has posted and it gets translated. It has made a huge difference to the content I'm seeing, which is fascinating because I don't think we've ever had this kind of automatic “everything is translated into your language” situation. It's really got me thinking about how [automatic translation] might work for eBooks or other things if the rights are there. I don't know. Have you seen stuff like that? Jeff: There's so much available now with voice and the ability to not have to speak all the other stuff that went with it—comma, full stop, next line. It was a little mind-bending sometimes, trying to think about quote marks and all that stuff. And now it's so good. Different platforms do it to different degrees of ability. Even being able to speak your prompts into the very platforms themselves without having to type all of it. Chronic pain comes to mind, any kind of mobility thing—all the typing would be a drain or maybe even impossible. So the voice ability is so powerful there and unlocks more things. At the same time, those translation abilities—I believe AirPods now have the ability, if you've got the right stuff on your phone, that you could be talking to somebody, they may speak back to you in a language you don't speak, but your AirPods will give it to you in your language. Jo: Hmm. Jeff: Google has, I believe, a live captioning app that you can use. I think there's even a split screen—I don't know if that's available now or something in their future—where you could put the phone on the table and tell it who's looking at what side of the screen, and it'll put the language that I need on my side and the language the other person needs on the other. So there continues to be such a shift in how we're being able to translate stuff that really opens up communication and can open up our books to so many more people. I'm very interested to see—I haven't pulled the trigger on this yet—but how Amazon's auto-translation rolls out and how that's received in terms of the accessibility around our books and being able to put it in someone's hands who doesn't speak—I think it's only English to other languages right now—but who doesn't speak the language it was written in but wants to read that book. We could never, as indies, or really even big five publishers, wouldn't have the money to create custom translations everywhere. But if the AI can help do that and spread those books around so that everybody could have the story they want to read, I think that's such a win for the reading audience. Jo: Yes, I think it's so exciting to think what might be coming, and that's what I want to stay on the side of on the AI discussion. There's enough negativity out there and you can get that information somewhere else, but for me I want us to stay on the positive side of how this helps both the author and the reader. And hopefully the community, to create more and read more and enjoy being human more. Right? Because I find that I do get out more and listen to stuff, or I'm out walking instead of at my desk, and I mean, that's what it's about. I'm pretty excited about the future. How about you? Jeff: I am. I think there are, quite honestly, some scary things that could be out there in the future. I mean, there's been a lot of talk about what Mythos is capable of. But on the other side of it, there are all these advances. I also look back at Google and AlphaFold and what DeepMind was able to do there for science. There's more of that stuff out there, and individually for each of us, spending a little bit of time—and I do have to say, I think you need to spend time on a paid plan because the free stuff doesn't give you the idea of what these platforms are actually capable of. So if you only drop in, even briefly, to experiment on one of the $20-a-month plans and give it your situation, ask it what it can do for you, I think you'll see where, on a personal level, AI will help you unlock some things. It can help you move some things to the next level in your business that for whatever reason you haven't been able to do. You don't have to use it for everything. You may decide that it's still not for you for whatever reason, and that's fine. But I think there's so much to explore here and to let your curiosity run for a little bit to see what's possible and what you might unlock with it. Jo: Brilliant. So where can people find you and your books and everything you do online? Jeff: So pretty much everything lives at JeffAdamsWrites.com. Jo: Well, thanks so much for your time, Jeff. That was great. Jeff: I loved it, Jo. Thanks for having me..The post Accessibility And AI: How New Tools Are Opening Doors For Indie Authors With Jeff Adams first appeared on The Creative Penn.

In this episode, Jure Leskovec, co-founder and chief scientist at Kumo and professor of computer science at Stanford, joins us to explore two fronts of his work: AI for science and relational deep learning. We begin with AI Virtual Cell, a multiscale effort to learn data-driven representations from proteins to cells to patients using single-cell RNA-seq data, protein language models like ESM, and structure models like AlphaFold—without hand-encoding biology. Jure then dives into relational deep learning, reframing enterprise databases as graphs and training neural networks directly on raw multi-table data. He explains Kumo's Relational Foundation Model (RFM2), which performs in-context learning over subgraphs to make accurate predictions on new databases and tasks with no training, and how this approach benchmarks against RelBench and other multi-table datasets. We also discuss real-world deployments at companies like Reddit, DoorDash, and Coinbase, explainability via attention over tables and columns, integration with agentic systems, deployment options, and practical limitations. The complete show notes for this episode can be found at https://twimlai.com/go/768.

Michael I. Jordan, described by Science magazine as the most influential computer scientist alive, has never thought of himself as an AI researcher. In this conversation he explains why that distinction matters.SPONSOR:---Cyber Fund built the Monastery to help founders ship products that were impossible a year ago. Applications for Batch 1 are now open.Apply now: https://cyber.fund---Jordan trained as a statistician and cognitive scientist, and his career has been spent building machine learning systems that work in the real world: supply chains, commerce, healthcare, and large economic systems. When the field rebranded itself as AI and then AGI, he did not follow. Instead he argues that the framing is wrong. AI is better understood as a collective economic system than as a race to build a disembodied superintelligence.We talk about why AGI is mostly a PR term, what machine learning achieved before the LLM hype cycle, and why the assistant-on-your-shoulder vision may be less compelling than it sounds. Jordan explains why explanations need to be actionable, not merely mechanistic; why AlphaFold's missing error bars matter; how prediction-powered inference changes the picture; and why drug discovery is an incentive-design problem rather than a pure pattern-matching problem.ERRATA: Science magazine ranked him the most influential computer scientist, not Nature---TIMESTAMPS:00:00:00 Cold open: A demoralizing message to young builders00:02:04 CyberFund sponsor read00:02:50 From symbolic AI to machine learning systems00:05:42 Why AGI is mostly a PR term00:08:48 A collectivist, economic perspective on AI00:11:33 Why LLMs need system design, not hype00:14:50 Predictability beats faux understanding00:17:55 AlphaFold, bias, and prediction-powered inference00:21:48 Stop anthropomorphizing intelligence00:27:44 Drug discovery as an incentive problem00:32:29 The three-layer data market00:38:07 Social knowledge, markets, and culture00:45:39 Creator economics beyond Spotify00:48:30 How science-fiction AI narratives mislead young builders00:51:45 AI should improve humans, not replace them00:56:42 Safety is a property of the whole system00:58:12 Silicon Valley gurus and the cream off the top01:00:47 Game theory, mechanism design, and contracts01:04:39 Conformal prediction, e-values, and anytime inference01:08:11 A new liberal arts triangle for the AI era01:11:30 The Bayesian duck and markets as uncertainty reductionReScript (transcript, PDF, refs etc) - https://app.rescript.info/public/share/fb68f94af29d3745c6cf6125e01328b5---REFERENCES:person:[00:02:50] Michael I. Jordan (homepage)https://people.eecs.berkeley.edu/~jordan/paper:[00:06:01] A Collectivist, Economic Perspective on AIhttps://arxiv.org/abs/2507.06268[00:18:09] AlphaFoldhttps://www.nature.com/articles/s41586-021-03819-2[00:20:36] Prediction-Powered Inferencehttps://arxiv.org/abs/2301.09633[00:33:47] On Three-Layer Data Marketshttps://arxiv.org/abs/2402.09697[01:04:39] Conformal Prediction with Conditional Guaranteeshttps://arxiv.org/abs/2107.07511[01:04:51] A Tutorial on Conformal Predictionhttps://www.jmlr.org/papers/v9/shafer08a.html[01:06:00] E-Values Expand the Scope of Conformal Predictionhttps://arxiv.org/abs/2503.13050[01:08:23] Computational Thinkinghttps://www.cs.cmu.edu/~CompThink/papers/Wing06.pdfother:[00:28:20] How Should the FDA Test?https://rdi.berkeley.edu/events/sbc-assets/pdfs/Summit%20session%20speaker%20slides%20submission%20form-s1-5%20%28File%20responses%29/Slides%20in%20PDF%20%28Please%20name%20the%20submitted%20file%20as%20_firstname_-_lastname_-slides.pdf%29.%20%28File%20responses%29/27-Michael%20Jordan-Session%20V.pdf#page=15[00:28:40] Michael I. Jordan Session V Slides

Can AI move from predicting proteins to actually designing new drugs? Isomorphic Labs is trying to answer one of the biggest questions in science.In this episode of The Neuron, Corey Noles and Grant Harvey talk with Rebecca Paul, Head of Medicinal Drug Design at Isomorphic Labs, and Michael Schaarschmidt, Foundational AI Research Lead.They explain why drug discovery is so slow, expensive, and failure-prone—and why AI drug design is much more complicated than “generate a molecule and ship it.” The conversation covers AlphaFold, structure prediction, molecule generation, binding models, clinical failure rates, human trust in AI systems, and the long-term hope of designing drugs for targets once considered “undruggable.”In this episode:Why drug discovery can take more than a decadeWhat people misunderstand about “AI-designed drugs”How medicinal chemists actually use AI modelsWhy biology is harder than text, images, or codeWhat it would take to make drug discovery faster and cheaperThe dream of designing a drug candidate in one iterationWhy “undruggable” proteins may not stay undruggable foreverAdditional resources:Technical report blog Best resource for learning about the capabilities that we are buildingIsomorphic Labs websiteBest destination for learning more about Iso and joining our team in London, Lausanne or Cambridge, MASubscribe for more grounded conversations on how AI is changing science, work, and the world.For more practical, grounded conversations on AI systems that actually work, subscribe to The Neuron newsletter at https://theneuron.ai.

Demis Hassabis, co-founder and CEO of Google DeepMind and 2024 Nobel laureate in chemistry for AlphaFold, joins Sequoia partner Konstantine Buhler at AI Ascent 2026 for a wide-ranging conversation about the path to AGI and what comes after. He explains why he believes AGI is achievable by 2030, why drug discovery could collapse from ten years to days, and why we should think of information, not matter or energy, as the most fundamental substance in the universe. Also: what Einstein would tell us about the limits of today's models, and why the next year or two will be critical for humanity.

Demis Hassabis is the Co-Founder & CEO of Google DeepMind - working on AGI, responsible for AI breakthroughs such as AlphaGo, the first program to beat the world champion at the game of Go; and AlphaFold, which cracked the 50-year grand challenge of protein structure prediction and was recognised with the 2024 Nobel Prize in Chemistry. Demis is revolutionising drug discovery at Isomorphic Labs. Ultimately, trying to understand the fundamental nature of reality. AGENDA: 00:04:00 — What Actually Counts as AGI; and Where Are We Today? 00:05:00 — What Are the Biggest Bottlenecks Holding AI Back Today? 00:06:00 — Have We Hit the Limits of Scaling Laws? 00:07:00 — Where Is AI Ahead of Expectations; and What's Still Missing? 00:07:30 — Why Can't AI Systems Learn Continuously Like Humans? 00:08:30 — How Did DeepMind Go from Behind to Leading the Pack? 00:11:00 — Are We Heading Toward Model Commoditization; or Winner-Takes-All? 00:12:00 — What Does the Future of Open Source Really Look Like? 00:13:00 — What Does a Post LLM World Look Like? 00:14:45 — Can AI Really Fix Drug Discovery—and Cut the 10-Year Timeline? 00:17:00 — What Does "Good" AI Regulation Actually Look Like? 00:18:00 — Who Should Be the Ultimate Arbiter of Truth in an AI World? 00:19:30 — If Demis Had One Shot to Fix AI Safety, What Would He Do? 00:21:00 — Is This Time Different for Jobs; or Will History Repeat Itself? 00:22:00 — Is AGI Bigger Than the Industrial Revolution; and Faster? 00:23:00 — Are We Underestimating AI Despite All the Hype? 00:23:30 — Does AI Lead to Massive Inequality; or Universal Prosperity? 00:24:30 — How Do We Solve the Energy Crisis Created by AI? 00:26:00 — Why Stay in the UK Instead of Moving to Silicon Valley? 00:28:00 — Will Europe Ever Build a Trillion-Dollar Tech Giant? 00:29:30 — Meeting Elon Musk for the First Time? 00:31:00 — What Big Questions About AI Is No One Talking About? 00:31:30 — What Does Demis Want His Legacy to Be?    

In this episode of Partnering Leadership, Mahan Tavakoli sits down with Steve Brown, a leading AI futurist and former executive at organizations including Intel and DeepMind. Brown brings a rare combination of technical depth and leadership perspective, shaped by decades at the forefront of technological change and his work advising leaders around the world on the implications of artificial intelligence.The conversation centers on Brown's book, The AI Ultimatum, and the core argument behind it: AI is not simply another productivity tool or IT upgrade. It represents a fundamental shift in how intelligence is created, scaled, and applied inside organizations. Leaders who treat AI as incremental technology risk missing the much larger transformation underway.Brown explains why he believes we are entering an “intelligence age,” comparable in scope to the Industrial Revolution, but unfolding at a dramatically faster pace. As the cost of intelligence approaches zero, organizations will face new strategic choices about workforce design, value creation, leadership identity, and ethical responsibility. These choices, Brown argues, cannot be delegated or delayed without consequence.Throughout the episode, Mahan challenges Brown to bridge theory and practice. They explore real organizational examples, from AI agents working alongside humans to scientific breakthroughs like AlphaFold, and examine how leaders can shift from efficiency-driven thinking toward value creation, judgment, and human amplification.This is not a conversation about tools or trends. It is a candid discussion about leadership responsibility in a period of accelerated change, and what CEOs and senior executives must rethink now to ensure their organizations remain relevant, resilient, and human-centered.Actionable TakeawaysYou'll learn why delaying AI decisions is itself a leadership choice, and how waiting for clarity can quietly erode organizational value.Hear how the “intelligence age” differs from previous technology shifts, and why its speed changes the role of senior leadership.You'll learn why AI should be viewed as a digital workforce, not just software, and what that means for strategy, structure, and accountability.Hear how leaders must shift from being the source of answers to guiding exploration, judgment, and learning in uncertain conditions.You'll learn why cost-cutting is the weakest use of AI, and where leaders should instead focus to create new value.Hear how AI changes the relevance of experience, narrowing gaps while raising expectations for judgment and insight.You'll learn why ethics, bias, and responsibility do not belong to algorithms, but remain firmly in the domain of leadership.Hear how AI can amplify human capability rather than replace it, when leaders design work intentionally.Connect with Steve BrownSteve Brown Website Steve Brown LinkedInThe AI Ultimatum: Preparing for a World of Intelligent Machines and Radical TransformationConnect with Mahan Tavakoli: Mahan Tavakoli Website Mahan Tavakoli on LinkedIn Partnering Leadership Website

This week's stories: Sinclair's This Is the Test: Are we about to see age reversal in humans? At the World Governments Summit 2026 in Dubai, Harvard geneticist David Sinclair told world leaders that ageing could soon be reversible and said the first human clinical trials of epigenetic reprogramming therapies are moving forward. The core idea is that ageing is partly an information problem, how cells read DNA, not just cumulative damage, and that partial reprogramming could restore youthful function without turning tissues into tumors. Dave frames this as a rare binary moment for longevity: either early, localized human trials (starting with tightly controlled tissue targets like the eye) show meaningful functional rejuvenation with acceptable safety, or the field has to recalibrate fast. Either way, the next couple of years will heavily influence where money, regulators, and serious researchers place their bets. • Sources: – World Governments Summit: https://www.worldgovernmentssummit.org/media-hub/news/detail/ageing-could-soon-be-reversible-says-harvard-scientist-at-wgs-2026 – NAD / Life Biosciences coverage: https://www.nad.com/news/fda-greenlights-life-biosciences-human-study-setting-up-pivotal-test-for-aging-theory-from-harvards-david-sinclair AlphaFold 4 in a locked box: DeepMind's private AI drug design engine Isomorphic Labs, DeepMind's drug discovery company, unveiled a proprietary drug design engine that outside scientists are comparing to an AlphaFold 4 moment, but for designing drugs, not just predicting structures. The big shift is that this system is closed: no public weights, no open database, and access appears to flow through partnerships with pharma companies. Dave breaks down why that matters for the longevity world: if AI makes early discovery cheaper and faster, we might see more serious shots on ageing targets over the next decade, but a closed model can also mean less transparency, bigger IP moats, and no guarantee that faster discovery leads to cheaper drugs. • Sources: – Nature: https://www.nature.com/articles/d41586-026-00365-7 – Isomorphic Labs: https://www.isomorphiclabs.com/articles/the-isomorphic-labs-drug-design-engine-unlocks-a-new-frontier Peptides in the freezer: El Mencho's anti aging stash and the dark side of wellness After reports and images from the final hideout linked to Jalisco New Generation Cartel leader Nemesio Oseguera Cervantes (El Mencho), coverage highlighted a detail that feels uncomfortably familiar to anyone in the modern wellness internet: injectable vials stored in a freezer with a schedule attached, including Tationil Plus, a glutathione based injectable marketed in some places for “cellular health,” cosmetic effects, and anti ageing. Dave uses the absurdity as a narrative wedge, not cartel gossip, to talk about how normalized gray market injectables have become, and how marketing (“detox,” “cellular reset”) often outruns evidence and safety. The segment pivots into a practical filter: which compounds are real therapeutics under medical supervision, and which are expensive folklore with sourcing risk and unknown long term downsides. • Sources: – New York Post: https://nypost.com/2026/02/25/world-news/inside-the-luxurious-love-nest-where-mexican-drug-lord-el-mencho-spent-his-final-days/ – Sky News (Reuters photos referenced): https://news.sky.com/story/inside-the-mexican-villa-where-feared-drug-lord-el-mencho-spent-final-hours-13511954 – Reuters photo gallery: https://www.reuters.com/pictures/el-menchos-last-hideout-inside-villa-where-cartel-leader-spent-final-hours-2026-02-25/W7DK5WEXS5IMLLZQO2P3CXGXFM The disease we thought was dead: measles comes roaring back Measles cases have surged in early 2026, with reporting citing at least 588 cases in the U.S. by late January, already more than many full year totals, and additional updates showing continued acceleration into February. Dave reframes this as a healthspan floor issue: you can argue about peptides and mitochondria all day, but measles is so contagious that once community immunity drops, outbreaks move fast and hit the most vulnerable first, especially infants and immunocompromised people. He also flags the systems problem: many clinicians have never seen measles, which increases the odds of delayed recognition and wider exposure in waiting rooms. The actionable move is boring and high ROI: verify MMR status for you and your family and close gaps before outbreaks get closer to home. • Sources: – AMA Morning Rounds (Week of Feb. 2, 2026): https://www.ama-assn.org/about/publications-newsletters/top-news-stories-ama-morning-rounds-week-feb-2-2026 – ABC News (CDC case count coverage): https://abcnews.com/Health/588-us-measles-cases-reported-january-cdc/story?id=129699078 – CIDRAP (case tracking context): https://www.cidrap.umn.edu/measles/us-measles-cases-soar-588-so-far-year-south-carolina-confirms-58-new-infections DC vs your health: Trump's State of the Union health reset President Donald Trump's 2026 State of the Union included a cluster of healthcare themes that function as a directional signal for agencies and payers this year, including drug pricing rhetoric, price transparency, and broader coverage and affordability framing. Dave translates the politics into a practical heuristic for biohackers: federal posture quietly determines what becomes easy versus painful to access in the legitimate system, from GLP 1 coverage rules and prior auth behavior to how friendly the environment is for telehealth, at home diagnostics, and eventually whatever “real longevity medicine” looks like. You do not need every policy detail in a weekly rundown, just the weather report: reimbursement and enforcement trends shape what stays niche, what scales, and what gets friction. • Sources: – Advisory Board: https://www.advisory.com/daily-briefing/2026/02/25/health-policy-roundup – Healthcare Dive: https://www.healthcaredive.com/news/trump-state-of-the-union-healthcare-2026/812962/ – This Week in Public Health analysis: https://thisweekinpublichealth.com/blog/2026/02/25/the-2026-state-of-the-union-what-it-means-for-health-and-public-health/ All source links are provided for direct access to the original reporting and research. This episode is designed for biohackers, longevity seekers, and high-performance listeners who want mechanism-level clarity on circadian biology, neurodegeneration signals, cognitive training, caffeine strategy, and supplement regulation. Host Dave Asprey connects emerging science, behavioral data, and policy shifts into practical frameworks you can use to build a resilient, adaptable health stack. New episodes every Tuesday, Thursday, Friday, and Sunday. Keywords: David Sinclair age reversal, epigenetic reprogramming therapy, Yamanaka factors OSK, Life Biosciences clinical trial, human rejuvenation trial 2026, biological age reset, longevity breakthrough news, DeepMind Isomorphic Labs, AlphaFold 4 drug design, AI drug discovery engine, geroprotective drug development, peptide gray market risks, injectable glutathathione Tationil Plus, GLP-1 regulation FDA warning, wellness industry regulation, measles outbreak 2026 US, MMR vaccine status adults, vaccine trust public health, health policy 2026 State of the Union, GLP-1 access and reimbursement, telehealth longevity care, biohacking news, anti-aging research update Thank you to our sponsors! Resources: • Get My 2026 Clean Nicotine Roadmap | Enroll for free at https://daveasprey.com/2026-clean-nicotine-roadmap/ • Get My 2026 Biohacking Trends Report: https://daveasprey.com/2026-biohacking-trends-report/ • Dave Asprey's Latest News | Go to https://daveasprey.com/ to join Inside Track today. • Danger Coffee: https://dangercoffee.com/discount/dave15 • My Daily Supplements: SuppGrade Labs (15% Off) • Favorite Blue Light Blocking Glasses: TrueDark (15% Off) • Dave Asprey's BEYOND Conference: https://beyondconference.com • Dave Asprey's New Book – Heavily Meditated: https://daveasprey.com/heavily-meditated • Join My Substack (Live Access To Podcast Recordings): https://substack.daveasprey.com/ • Upgrade Labs: https://upgradelabs.com Timestamps: 0:00 - Introduction 0:30 - Story #1: David Sinclair 2026 2:13 - Story #2: Google Drug Discovery 3:48 - Story #3: El Mencho Biohacking5:30 - Story #4: Measles Outbreak 6:51 - Story #5: Trump State of the Union 8:00 - Weekly Roundup 9:10 - Closing See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.