Podcasts about Cambrian

Host: Andrew Birmingham - Editor - CX | Martech | Ecom A year after Mi3 Australia began its agentic AI research deep dive – dubbed Inside the Tornado – that first wave of febrile experimentation has given way to what feels like the beginning of a Cambrian explosion as businesses embed AI agents into core operations, and realise measurable gains in areas ranging from pricing optimisation to creative production. But as adoption accelerates, executives say attention is shifting from the promise of autonomous systems to the practical realities of governing them, understanding and controlling costs and ensuring they do not drift off course – because they will absolutely drift of course. Speaking with Inside the Tornado author, and Mi3 Tech editor Andrew Birmingham, T2 Tea marketing director Peter Randeria and Omnicom Oceania chief product officer Alex Pacey argue that the organisations moving fastest are not those taking the greatest risks, but those building the strongest governance foundations. Their message is clear: agentic AI can create significant commercial value, but success depends on the discipline to supervise it, redesign workflows around it and manage its rapidly growing economic footprint, as much as it requires corralling a still immature and rapidly evolving technology that even its developers sometime still struggle to understand.See omnystudio.com/listener for privacy information.

Seventeen years into building the world's largest XR conference, Ori Inbar is not prone to hyperbole. He has watched hype cycles inflate and collapse, made predictions that turned out too optimistic, and learned to hold claims carefully. That is what makes his framing of AWE 2026 worth paying attention to: he calls it the most consequential year in the show's history. Not because everything is working — there have been heartbreaking layoffs in some corners of the industry — but because the convergence happening right now between AI and spatial computing is unlike anything the field has seen before.Before Ori joins, the hosts wade through a week of signal and noise. Three big IPOs — Cerebras, Quantium, and others — are absorbing investor attention, with Quantium carrying a $15 billion market cap on what Charlie calls "de minimis revenue," raising questions about whether the quantum AI bandwagon has lapped actual quantum utility. Rony poses the challenge directly: what is the real use case for quantum computing besides breaking encryption?When Ori arrives, the conversation opens on Snap. Evan Spiegel is expected to make a major consumer announcement at AWE — Ori says Snap has put all their eggs in this basket, and the audience at the show will be the first to see it. Ted frames the stakes plainly: if the price shocks people, it's a consumer breakthrough; if it's expensive and exotic, it stays in the science column. Snap recently acquired Illumix, a spatial universe understanding startup, a move that signals the company is building seriously in this space.The endgame vision comes from Rony: Oakley-weight wraparound glasses at 30–40 grams, human retina resolution, full indoor/outdoor capability, AR and VR combined, wireless, all variable focus, under $500. Ted adds that it also has to land under $650 fully costed at retail. Ori's honest answer: "I promised myself I'm not gonna predict when this happens. I've tried many times and was always way too optimistic." Ted teases Gixel, a German startup he and Rony are involved in using non-waveguide display technology already above 60 pixels per degree — when you put the prototype on, he says, it is crystal clear.Defense is the fastest-growing vertical at AWE. Healthcare, manufacturing, aerospace, and automotive are major enterprise sectors. Digital twins are the biggest thing in enterprise XR right now, with world models emerging as the intelligence layer sitting beneath them. Over 10 million AI glasses — display-free — sold last year. Ori's framing of why display glasses matter more: AI is shifting these devices from tools that help you learn about things to tools that actually do things.Key moments:[00:02:47] Quantum IPO bubble — Rony asks what the actual use case is[00:05:48] Quantum mechanics in plain language — qubits, superposition, neurons as quantum computers[00:09:51] Apple WWDC preview — Siri, folding phone, Rony's secret Apple wearable tease[00:11:38] Google Dream Beans — Ted: "It's an ad play"[00:12:51] Suno $400M raise — Rony: "Musical crack" and the TikTok-for-music thesis[00:14:42] Fox reformats "Farmer Wants a Wife" into 101 vertical episodes — the content inflection point[00:17:00] Ori joins — AWE 2026 as "most consequential year in our history"[00:17:40] Snap and Evan Spiegel's expected consumer announcement at AWE[00:19:38] Cambrian explosion of XR content — Meta talent diaspora, Supernatural spinoff[00:23:07] Vibe coding for XR — Ori's AR prototype built in two days with Gemini[00:25:48] Charlie inducted into the AWE Hall of Fame — joining Ted and Rony[00:28:36] iSpatial theme — Ori's three biggest XR trends: AI glasses, AI content, world models[00:39:31] Defense fastest-growing vertical. Digital twins biggest in enterprise.[00:47:18] Rony's endgame AR glasses vision. Ted teases Gixel's crystal-clear prototype.Brought to you by Zappar and Mattercraft. Build web-based AR experiences without writing code at mattercraft.io. Hosted on Acast. See acast.com/privacy for more information.

Jim Rutt was a working class kid from the suburbs of Washington DC who somehow ended up at MIT, spent years hitchhiking around the country, stumbled into the world's first consumer online service, and eventually became CEO of Network Solutions and chairman of the Santa Fe Institute. He was a relentless reader — 100 books a year since age 10 — and one of the most genuinely curious people Vance ever sat down with. He died recently after a period of illness, and this episode is a tribute. What you'll find here is a compilation of the conversations Vance and Jim had together over the years, including sessions recorded in virtual reality — because of course Jim was one of the first people to order a VR headset and try a podcast in it. They covered everything: the origins of the Internet, complexity science and the Cambrian explosion, Game B and how humans organized before hierarchy existed, the risks of artificial superintelligence, machine consciousness, and what it feels like to watch something you built learn on its own. Jim knew he was dying. Vance had the chance to sit with him for a Legacy Interview — a full recording of his life story — and to talk with him right up until the end. This episode is a small window into what made Jim so remarkable: the sheer breadth of his mind, his refusal to accept conventional wisdom, and his joy in being out on the edge of what's known. He was one of a kind.Articulate.Ventures/IBCLegacyInterviews.com

New fossil discoveries from China are being hailed as evidence that could reshape our understanding of the origin of complex animal life. Does the new find solve the mystery of the Cambrian explosion? Are the headlines about these fossils justified? Are these in fact the long-lost ancestors of the Cambrian animals we've been looking for? On this ID The Future, host Andrew McDiarmid welcomes Dr. Casey Luskin to the show to to examine the evidence, ambiguity, and ongoing controversy surrounding newly reported Ediacaran bilaterian fossils. Source

Tired vs. Wired: $4 Trillion in IPOs Coming, $100B in M&A, and Why the SaaSpocalypse is Over The public markets spent the last twelve months telling you B2B software was finished. Stocks down 60 to 70 percent. PE firms buying nobody. For the first time in history, software trading at a discount to the S&P 500. And at the exact same moment, Anthropic is projecting $50 billion in revenue, Cursor is getting acquired for $60 billion, and SpaceX, Anthropic, OpenAI, and Databricks are about to generate more market value than every other IPO since 2000 combined. Both things are true - and which one defines your next 18 months depends entirely on one question: are you tired or are you wired? In this episode, SaaStr CEO and Founder Jason Lemkin calls the market as he sees it, names who is winning and who is pretending, and makes the case that the Cambrian explosion in B2B is just getting started. You'll learn: Why the SaaSpocalypse was never about B2B dying - it was about pre-AI software dying - and what the Palantir, Twilio, and Atlassian re-acceleration stories actually tell you The four categories every B2B company falls into right now, and why category four founders need to stop pretending the recovery is coming on its own Why vibe coding your CRM is dead as a concept, and what "putting deals on your calendar" actually means as a product strategy Why your biggest near-term competitive edge might be two days of engineering work - making your API agent-friendly before your competitors do What SaaStr's own journey from 20 humans to 3 humans and 21 agents teaches you about consistency as the only real cheat code in agents This is for you if: Your growth has slowed and you are not sure whether it is a market problem or a you problem - this session will help you figure out which You are a founder or exec who has been in the "AI is coming" conversation for a year but has not yet seen it show up in your revenue You want the unfiltered version of where B2B is headed in the next 18 months, including the parts most people are too polite to say out loud

Vance sits down with St. Louis radio veteran Mark Reardon — 97.1 FM Talk — for a wide-ranging conversation that quickly reveals just how different two people's information worlds can be. Mark has been in talk radio since he was 15, has survived firings and format flips, and still believes in live local radio. But when Vance starts talking about the Bitcoin Clarity Act or Cynthia Lummis, Mark draws a complete blank — and neither of them finds that reassuring. The gap between boomer and younger media diets, they agree, is now so wide that the two groups are essentially living in different realities. From there the conversation gets into territory that makes Mark visibly uncomfortable in the best way: Vance's argument that young people aren't just disengaged from voting — they're losing faith in the entire system. Housing costs, inflation funneled into boomer-owned assets, Social Security nobody will touch, and now AI threatening whatever intellectual edge younger workers thought they had. Mark pushes back but doesn't fully disagree. He also opens up about his own AI intimidation — just getting started with help from a friend at ThrottleNet — and Vance walks him through the Cambrian explosion framing and Pope Leo's encyclical on building AI like Nehemiah's wall, not the Tower of Babel. The episode covers Iran, the Catholic Church abuse scandal, Vance's prediction of a Pentecostal revival, and whether lynch-mob justice is actually coming — before Mark rescues everyone with an extended, genuinely delightful tangent about Oreo, his litter-trained Dutch rabbit who has taken over his couch and his heart.Articulate.Ventures/IBCLegacyInterviews.com

In this episode of See See by Ceci, Toby Kiers, one of the world's most daring thinkers at the intersection of evolutionary biology, economics and ecology, takes us into the living web beneath our feet. University Research Chair and Professor of Evolutionary Biology at Vrije Universiteit Amsterdam, Tyler Prize laureate, MacArthur "genius" Fellow, Spinoza Prize winner and co-founder of SPUN, the Society for the Protection of Underground Networks, Kiers has spent more than two decades asking the questions most of us never think to pose: How does a brainless organism make decisions? and, What is it like to be a fungus? In this rich and revelatory conversation, Kiers reflects on symbiosis as the hidden driver of evolution, from the first algae crawling onto land 450 million years ago to a soybean root in a Dutch laboratory today; on cheating as a force of innovation rather than a moral failure; on the exquisite sensitivity of fungal networks that respond to vibration, breath and light; on sanctions that are swift, severe and ingenious; on what she calls "punk science", research that crosses disciplines and refuses to accept the world as given; and on the humbling moment in Ecuador when members of the Sarayaku community listened to her describe her findings and replied: Of course this is happening. We knew this! This is part of our belief! She tells us about her team, the "underground astronauts" mapping the world beneath our feet, and about fungi as a "library of solutions" for a planet in crisis: a circulatory system that processes some 13 billion tons of carbon each year, roughly a third of all fossil fuel emissions. Along the way, we hear former Harvard neurosurgeon Dr. Eben Alexander pose a question on the Cambrian explosion and evolutionary partnerships; we hear from Professor Katherine Hayles on the Umwelt, on actors and agents, and the uncoupling of consciousness from cognition; from ecologist Carl Safina on the cooperation between dolphins and fishermen and the worm's first aesthetic judgement; and from choreographer Alexander Whitley on the flow states that technology can both disrupt and reveal, each voice opening a new dimension of what it means to sense, to decide, and to belong on a living planet. This is an episode about the wonder beneath the soil: biological, strategic and ancient, namely the circulatory system that connects all life on earth. About the courageous shift in mindset to acknowledge the ground we walk on not as inert matter but as intelligent beings, capable of supporting universes above their own.

The fossil record shows us examples of God's great creativity in designing living things. It also shows that life appeared suddenly on earth, in finished form. The fossil record shows us that earth originally had a much greater variety of life. Finally, the fossil record doesn't show any evidence of creatures evolving from one type into another.Paleontologists have been looking for fossils of unusual creatures in some of the oldest rocks that have fossils in them. In other words, these rocks in British Columbia have evidences of some of the earliest forms of life. These layers show the rich variety of life that once existed on earth. Paleontologists have found a much greater variety of arrow worms and jellyfish than live today. But even in the earliest layers, the worms and the jellyfish are fully formed.In addition, paleontologists have found some startling creatures. One foot and a half long creature had a circular mouth with radiating teeth and claws. Another looks like a tiny, inch long dragon. Scientists describe it as looking like the cameo of a stegosaurus. Perhaps the most unusual creature was named "Santa Claws" by one paleontologist. It has five pairs of claws attached to its head, two flaps on the side, and a tail like a beaver.Paleontologists and Christians who believe the biblical record of creation don't dispute the facts about fossils. We object to interpretations of the fossils that needlessly contradict Scripture.Psalm 18:30" As for God, his way is perfect: the word of the Lord is tried: he is a buckler to all those that trust in him.”Prayer: Dear heavenly Father, even in death, brought about by man's sin, these creatures glorify You and bear witness to Your act of creation. Strengthen my faith so that I may not be intimidated by claims that contradict Your Word. In Jesus' Name. Amen.REF.: Weisburd, Stefi. "New creatures from the Cambrian." Science News, v. 128. Image: Burgess shale scale, Matt Martyniuk (Dinoguy2), CC BY-SA 4.0, Wikimedia Commons. To support this ministry financially, visit: https://www.oneplace.com/donate/1232/29?v=20251111

With 2023's El Niño – a recurring pattern of extreme weather across the pacific basin - still leaving a bad taste in people's mouth, 2026 sees an El Niño stirring in the Pacific Ocean and there are warnings that this will be one of the strongest yet.Roland Pease speaks with Amanda Maycock, a climatologist from Leeds University, to discuss what this climate phenomenon is and how it will impact the world from October to early next year. He also hears from Scott Evans from the American Museum of Natural History, who has been exploring the Mackenzie mountains of Canada's Northwest Territory to better understand the biology and ecology of life on earth before anything we might recognize - from the Ediacara era. This was before the explosion of different animal types with hard shells and bones in the later, Cambrian, time. In certain places around the world, much older rocks from the ancient ocean floor reveal an ecosystem abounding with soft, squidgy animal wierdness. In Canada Scott has found a new trove of these fossils, but from far deeper below the surface of those ancient seas. Did animal life begin deep in the darkest depths rather than paddling in pools nearer the land?Today, over 5 billion years later, bottom trawling, a common fishing method involving dragging heavy nets across the bottom of the seafloor, is an environmentally destructive process that rips up everything in its path to maximise catch. We talked to Amanda Vincent, a professor at the Institute for the Oceans and fisheries of the British Columbia university and founder of the international Project Seahorse conservation group, about what bottom-trawl bans can achieve, in the light of results published about a renaissance of biodiversity off the coast of Scotland in an area where trawling has been banned for several years.Plus, we talk to science journalist Gareth Mitchell, who explains how bottom trawling can also have negative consequences on technology, as well as other science news you may have missed, including updates on solar storms and robotic wolf shortages in Japan.Presenter: Roland Pease Producers: Alex Mansfield and Dan Welsh Editor: Martin Smith Production Co-ordinator: Jana Bennett-Holesworth

Terry Adair argues the Fermi Paradox has an energy answer. Advanced civilizations require photosynthesis, fossil fuels, and a brutal timeline. We might be alone because the requirements are impossibly rare.The thesis: We got here because of coal, oil, and gas. No civilization reaches our level without hundreds of millions of years of photosynthesis creating stored solar energy. Took 2 billion years to develop, then had to run long enough to create forests that became coal, phytoplankton that became oil.Without photosynthesis, planets never develop complex life. Not enough energy. Uranium can't fuel biology—destroys organic molecules. Only ongoing energy source: sunlight captured through photosynthesis.Cambrian explosion happened because genetics had surplus energy to experiment. Intelligence isn't evolution's goal—DNA only wants to reproduce. Human brain: 2% of body mass, 20% of resting energy. Super expensive. Without advantage, intelligence never evolves.Fossil fuels aren't optional. Can't reach our tech level without them. Renewables can't bootstrap industrial revolution. Nuclear requires already-advanced civilization. Energy ladder is fixed.Fermi answer: Most planets never develop photosynthesis. Those that do might not run it long enough. Those that do might not have accessible fossil fuels when intelligence emerges. Energy filter is brutal.

Special discounts up for AIE Melbourne (LS discount) and AIE World's Fair (group discounts up to 25% - CFPs still open for Autoresearch and Vertical AI) Cya there!Abridge did not start as an “GPT wrapper”. It was founded in 2018, years before the Cambrian explosion of AI application layer companies. OpenAI launched ChatGPT publicly on November 30, 2022 and by then, Abridge had already spent years doing the unglamorous work of building trust for one of the highest context, most important workflows in healthcare: the conversation between a patient and a clinician.Abridge's original wedge was clinical documentation. Listen to the visit, generate the note, reduce the clerical burden, and let clinicians spend more time with patients instead of the EHR. By focusing on how doctors actually document, how health systems actually buy, how EHR integration actually works, how clinicians verify outputs, and how missing context during a visit turns into downstream friction across billing, prior authorization, quality, and follow-up, the adoption of LLMs became a force multiplier on a workflow already optimized for sensitive context gathering.The company has scaled fast: Abridge says it is projected to support 80M+ patient-clinician conversations this year across 250 large and complex U.S. health systems, with support for 28+ languages and 50+ specialties. It raised $300M at a $5.3B valuation in June 2025, after a $250M round earlier that year.Today, Janie Lee and Chaitanya “Chai” Asawa of Abridge join us for another crossover pod with Redpoint's Jacob Effron (who is on the board of Abridge) to dive into how Abridge is building the clinical intelligence layer for healthcare starting with ambient documentation, then expanding into clinical decision support, prior authorization, payer/provider/pharma workflows, and eventually real-time agents that act before, during, and after the patient conversation. We go inside the product, data, infra, evals, workflow, privacy, and org design choices behind bringing AI into one of the highest-stakes enterprise environments from 100M+ medical conversations and specialty-specific evals to real-time alerts, EHR integration, de-identification, clinician-scientist teams, and why healthcare may solve some of the hardest AI problems first.We discuss:* Why Abridge started with clinical documentation, “pajama time,” and saving clinicians 10–20 hours a week* The transition from ambient scribe to clinical intelligence layer: save time, save money, and save lives* Why conversations between patients and clinicians may be the most important workflow in healthcare (patient visit summary feature)* Chai's “healthcare-coded Glean” framing: context is king, but healthcare raises the stakes on safety, evals, and rollout* Why Abridge wants AI to feel like “air conditioning”: always in the background, but only interrupting when it truly matters* The prior authorization example: turning a denied MRI weeks later into real-time guidance while the patient is still in the room* Why payer policies, EHR data, medical literature, and hospital-specific guidelines make the problem hard, and also create the moat* How Abridge thinks about ambient form factors: mobile, desktop, in-room devices, nursing workflows, multimodality, and future AR* The multi-sided healthcare customer: CMIOs, CFOs, CIOs, clinicians, patients, payers, and pharma* The hardest AI problem at Abridge: high-quality, low-latency, low-cost real-time support in a high-stakes clinical setting* When Abridge uses frontier models vs proprietary models, and why its unique data from medical conversations matters* Why “every agent is a coding agent underneath,” and how the EHR can be thought of as a filesystem for healthcare agents* How Abridge approaches personalization across individual doctors, specialties, and health systems* Why “AI slop” is AI without context, and how edits, memories, and clinician preferences create a data flywheel* Abridge's eval stack: LFDs, LLM judges, in-house clinicians, third-party evaluators, specialty-specific evals, and progressive rollout* HIPAA, PHI, de-identification, one-way anonymization, customer contracts, and learning from healthcare data safely* What changes when you operate at 100M+ conversations: reliability, cost, post-training, model routing, and infrastructure optimization* Why the same clinical conversation can serve doctors, patients, payers, pharma, and future clinical-trial workflows* How Abridge works with EHRs, and why deep interoperability is table stakes for clinician adoption* Why healthcare AI has regulatory tailwinds, why 80/20 does not work here, and why high-stakes domains may drive AI forward* Why Abridge embeds “clinician scientists” into product and eval teams* What Chai learned from Glean about search, quality, and durable AI infrastructure* Why the future of AI infra may look like context layers, event-driven systems, Kafka, Temporal, sockets, CRDTs, and tools built for humans* Why Janie changed her mind on “PRDs are dead,” and why crisp written clarity matters more in complex AI products* How Abridge uses Claude Code, Cursor, and coding agents internallyAbridge:* Website: https://www.abridge.com/* X: https://x.com/AbridgeHQJanie Lee:* LinkedIn: https://www.linkedin.com/in/janiejleeChaitanya “Chai” Asawa:* LinkedIn: https://www.linkedin.com/in/casawaTimestamps00:00:00 Introduction and what Abridge does00:02:05 From ambient documentation to clinical intelligence00:04:04 Clinical decision support and context as king00:06:57 Alert fatigue, proactive intelligence, and prior authorization00:12:36 Ambient AI form factors and healthcare customers00:16:59 The hardest AI problems in healthcare00:18:26 Frontier models, proprietary data, and model strategy00:21:07 The EHR as a filesystem for agents00:24:03 Personalization, memory, and clinician preferences00:30:40 Evals, LLM judges, and progressive rollout00:36:47 HIPAA, de-identification, and privacy00:39:21 100M conversations and operating at scale00:44:10 EHR integration and the clinical intelligence layer00:46:39 Healthcare regulation, latency, and high-stakes AI00:50:11 Clinician scientists and long-tail quality00:53:04 Lessons from Glean and durable AI infrastructure00:57:03 The future of agentic healthcare workflows00:57:34 PRDs, product clarity, and building serious AI products01:03:11 AI coding tools at Abridge01:04:06 OutroTranscriptIntroduction: Abridge, Clinical Intelligence, and the Latent Space x Unsupervised Learning CrossoverSwyx [00:00:00]: Okay. This is a special crossover Latent Space Unsupervised Learning pod.Jacob [00:00:07]: Very excited to do this.Jacob [00:00:08]: At this point, we get together once a year.Swyx [00:00:10]: Once a yearJacob [00:00:11]: And this is a fun occasion to get to do it on.Swyx [00:00:13]: I really wanted to talk to Abridge but I felt very underqualified because healthcare is not something we cover very intensely. It just so happens that Redpoint's our big investors and supporters of Abridge.Jacob [00:00:27]: Anytime you want to have a portfolio company on your podcastJacob [00:00:29]: Please, by all means.Swyx [00:00:31]: So we'll introduce our guests. Chai and Janie, welcome to the pod.Janie [00:00:34]: Thanks for having us.Chai [00:00:35]: Thank you.Janie [00:00:35]: We're excited to be here.Chai [00:00:36]: Thank you.Swyx [00:00:36]: So for listeners, what do you guys do, just to situate you guys in the company?Janie [00:00:42]: Abridge is a clinical intelligence layer for health systems. We really started with documentation and building for clinicians and as we think about reducing the burden that clinicians have, they're spending 10 to 20 hours a week on documentation. There's a massive doctor shortage in the country. We also think that conversations between patients and clinicians are probably the most important workflow in healthcare. It's where care is given and received but if you think about the 20% of our GDP that goes towards healthcare, almost everything is a derivative of that conversation, whether it's the claim, the payment, the actual diagnosis given, the treatment. And we've started with a conversation to reduce the burden for doctors on documentation but we're really excited about the path ahead as we become this broader clinical intelligence layer.Chai [00:01:34]: I'm Chai. I work on clinical decision support at Abridge.Swyx [00:01:37]: Yes.Chai [00:01:37]: And so as Janie said, we're uniquely situated where we started off with the clinical note. What I'm really excited about and where we're expanding towards is what are all the things you can do before the conversation, during the conversation and after the conversation if you did have access to all the context about patients, payer guidelines, medical literature and put that together and to serve, how healthcare could look fundamentally different.Swyx [00:02:01]: And that's the context engine that you guys have?Chai [00:02:04]: Yes.Swyx [00:02:04]: Is that what it's called? Okay.Swyx [00:02:05]: So historically, as I understand it, the company started in 2018. A lot of people would be familiar with the AI voice notes form factor that doctors would be “Well, do you consent to being recorded?” It replaces handwriting and what have you. But it sounds like more recently there's been a big transition in the company. Tell me about the broader transition.From Documentation to Clinical Intelligence: Save Time, Save Money, Save LivesJanie [00:02:26]: So from a transition perspective, we really think about our journey as The first act was: how do we help save time? And that's where a lot of that original product was.Swyx [00:02:37]: By the way, one of those interesting statsSwyx [00:02:39]: On your landing page was, doctors spend time after hours.Janie [00:02:43]: They call it pajama time.Swyx [00:02:44]: Why is that pajama time?Janie [00:02:46]: Doctors after work in their pajamasSwyx [00:02:48]: In their pajamas. OhJanie [00:02:49]: At home are just writing and catching up on their notes every day.Janie [00:02:53]: Some of our favorite customer love stories, we have a Slack channel called Love Stories. We have clinicians telling us, “Abridge has helped us, from retiring early or we're now finally able toJanie [00:03:06]: go home and eat dinner with our kids for the first time.”Chai [00:03:08]: Save the marriage in some cases.Swyx [00:03:10]: One of the quotes was “We're not divorcing anymore.”Swyx [00:03:12]: I'm asking, “Why?”Swyx [00:03:14]: Because they're working too much.Janie [00:03:16]: But, in terms of where we're going and where we're expanding, we really think about our second and third acts around how do we help health systems save and make more money. Health systems are operating with record-low operating margins. It's getting harder and harder to serve patients and they have regulatory, some tailwinds but also a lot of headwinds coming their way and AI is ripe for helping on the saving and make-more-money piece. And then ultimately, how do we help save lives? The fact that our software and our product is open millions of times a week before, during and after a patient walks in the room, gives us massive opportunity with products like clinical decision support, which Chai is building but so many others to improve patient outcomes and probably one of the most important workflows and problems to be going after right now.From Glean to Healthcare: Context Is KingJacob [00:04:04]: One thing that's interesting, Chai, is you came over to Abridge from Glean and clinical decision support, which for our listeners is, in the context of a visit, helping a doctor figure out the right type of care. It's really a search problem in many ways, going through lots of different data sources. Very analogous to your previous role as one of the earliest engineers over at Glean. I'm sure a lot of our listeners are curious what's similar about the problems that you're going after now and what feels different, now that you're in healthcare.Chai [00:04:33]: Very similar. Taking a step back, with every wave, there's a lot of very similar patterns that happen across different products. A lot of social networking products look the same. A lot of credit-based products look the same. And we're seeing that very similar in the agent era with many companies, of course, in Redpoint's portfolio and so forth. And the key insight between both companies is that you have amazing models but context is king. Context is what puts them to work. So I see it in a lot of ways, a lot of similarities in this is a healthcare-coded version of Glean but the differences are really interesting. A couple things that come to mind. First and foremost, the rigor of the setting we're in. The downside risk is extremely high here in healthcare. It can be fatal in some cases. You prescribe something that the patient is allergic to for example. Whereas at Glean, it's “Oh, you got the question wrong.” It wasn't the end of the world in most cases. And so what does that mean? That shapes our evaluation strategy, both offline evaluation, progressive rollout and there's a lot more we could go into there. Second thing that comes to mind is, vertical versus horizontal. In both cases, there's a large variance but when Glean is, it's a much more horizontal company, there's a variance of personas, companies that you're working with. We also have a variance of personas, different types of specialties, different hospital systems. But the variance is a little more narrow. So from a product perspective, you're able to focus far more, especially when you have a maturing technology and you're building new products that never existed before. It lets you go after them much more easily and especially in healthcare where so many problems were solved with labor and process, that it's extremely ripe for AI to keep helping augment and enable. And the final thing that's really interesting, Abridge specifically compared to many other companies in the AI area, is the modality we started with where we're ambient and we're always listening in the background. And many more AI products will go that way but it's how we started. And that's the greatest form of AI we can create, AI that's seamless. You're not looking at your screen. It's always there. It's always helping you out and being proactive. The Jarvis vision that, every hackathon I went to over the past decade, there was always a Jarvis competitor. But Abridge very much started from the opportunity and continues to go that way.Ambient AI and Alert Fatigue: When Should the Product Interrupt?Jacob [00:06:57]: One thing that is super interesting then from a product perspective is you have this always-on seamless in the background and then you have to decide when you break the wall almost and say, “Hey, clinician, you might not have thought about X,” or whatever it is that you want to do. And in healthcare traditionally there's been this idea of alert fatigue and a million pop-ups and then a doctor just ignores all of them. It's probably a pattern that a lot of builders are thinking through now. How do you think about the right way to intervene or to pop up in a doctor visit?Janie [00:07:26]: It's such a good question. Alerts are notorious in healthcare specifically. Over 90% of alerts are ignored. The first and most important thing is context is everything, as Chai alluded to and I also think about how do we go from being reactive alerting to really proactive intelligence at the point at which it matters most. One thing we like to say is we want our product to feel like air conditioning. It should be in the background just making things better and if there is something that has great clinical risk and we're acutely aware that intervening now and not later is incredibly important, we should decide to act. But if you think about proactive versus reactive, instead of alerting a clinician during a visit when they're with their patient having a pretty serious and sensitive conversation, how do we prep a clinician before they walk into the room with that patient? And so historically, clinicians might have to manually go through charts with a patient that they've had over the course of months or years and they'll try to suss out what are the things they should be doing. You can imagine a world with Abridge. We'll summarize all of the most recent context for you, tell you based on the reason for a visit the patient is coming in for the types of things you should be discussing. And so you're going into that conversation prepped rather than walking in cold to that patient visit and then having this product interrupt you five or 10 times throughout the visit. And there might be times where it's really important to interrupt. We have a product called Prior Authorization and so this is when you may go into a doctor's office with knee pain. They'll prescribe you an MRI and so many of us have had this experience before, where in four weeks you'll get a call saying, “Hey, Sean, that MRI that you were prescribed wasn't approved and why don't you come back in? We'll figure it out.” In a world with Abridge, we might choose to quietly but still alert a doctor in that visit. And alert is probably not even the word we would want to use. Before a patient leaves, we would want to tell the doctor, “Hey, Doctor, before Sean leaves, you should ask him, has he had physical therapy and has his pain lasted for more than six weeks? Because the Aetna plan that he's on in California requires six things. We've already confirmed four of them have been met ‘cause we have all the context. But these two last criteria, if you can address with Sean before he leaves the room, we could guarantee that your MRI is approved before you leave.” And so when you think about clinical usefulness, impact to the patient, there are instances in which if we can catch a doctor while the patient is still in the room, as we think about save time, save money, save lives, we get to check all of those boxes. But when doctors have 15 minutes between visits, we have to be really thoughtful about when it matters.Prior Authorization: Reducing Latency in CareChai [00:10:23]: There's this interesting product opportunity AI has is reducing latency in the world. For example, prior authorization is an example of where care gets delayed and so great AI can reduce that. And the problem with alerts before partially is a technical problem: the quality of your alerts really matters. They're going to get ignored if you get alerts that... Similarly in engineering, where they're noisy alerts that you can't act on. But if you can make really high-quality alerts with both the context, as Janie said, and really high-quality models, then you can create a whole other game.Janie [00:10:53]: And I really like that experience because it starts to tease apart, what makes this so hard and unique. One, to make that prior authorization example possible, think about all the data that you need to have. You need to integrate with the electronic health record to know all of the patient context. Do we have access to your previous labs, previous imaging? And then to match you and to know that you're on Aetna, we have to collect all of the different payer policies and they vary by state. Some of these payer policies live on websites. Some of them live in unstructured 50-page PDF files.Jacob [00:11:31]: I thought this episode wasJacob [00:11:31]: To make sure we didn't scare people from healthcare.Janie [00:11:34]: But when you think about the things that make it hard, it also gives you the moat.Janie [00:11:39]: And then the second is the AI and the model quality we need to be able to hang our hat on. And so the bar, similarly when I worked at Opendoor, I worked on pricing models. Every outlier wiped out the margins of 30 and so similarly here in healthcare, the bar for accuracy is so high. And then I'd say the last is workflow is everything. If insurance companies deploy AI, it typically happens too late and this is when you have the notorious comical examples of AI just fighting each other when it's too late. But if we can pull forward the use of both the AI but also the ability to solve problems when the patient's in the room, you can start to collapse what typically takes weeks or months after your visit, ideally down to minutes or real-time. And it's where healthcare is both very difficult but also extremely rewarding if you can crack it.Product Form Factors: Mobile, Desktop, In-Room Devices, and ARSwyx [00:12:36]: Just to get some baseline on the form factors, because I've seen some videos on your website and stuff. You guys talk a lot about ambient AI. Is it primarily on the phone? Is there any other form factor that people get Abridge in? Is there an Abridge room setup where it's always on? I don't know.Jacob [00:12:55]: An Abridge podcast studio.Janie [00:12:58]: Primary form factor is mobile and desktop. UsuallyJanie [00:13:00]: Clinicians are walking in and out of rooms with mobile but at the end of the day, when they're closing out their notes or wanting to prep for the day ahead, they might use desktop. We have been having a lot of really interesting partnership conversations with a lot of these in-room device companies as you think about the power of multimodality and even more data, as you think about all of what is not captured today. It is fascinating to think about, especially even as we go into building and scaling our nursing product. It's one where nurses constantly, as they're walking in to check in on a patient for two minutes or maybe even 30 seconds,Janie [00:13:43]: Starting an Abridge experience is probably going to take longer than the visit. And so what can we do with in-room devices that are always on starts to raise really interesting and fun product questions.Swyx [00:13:54]: I was thinking, the way in tech companies we have all these Google MeetSwyx [00:13:58]: And other things, we might as well set up entire rooms with just Abridge tech.Chai [00:14:02]: Very much. AR glasses and related form factors are also relevant: how do we bring the information to the clinician in real-time without a screen, while still letting them focus on the patient?Swyx [00:14:18]: Do you think they want that? I'm skeptical of AR, but I'm curious what you've tried.Chai [00:14:26]: Admittedly, it's not a near-term product roadmapChai [00:14:29]: By any means. I'm being far-fetched.Jacob [00:14:31]: There's some sick AR stuff for surgeries.Swyx [00:14:33]: Really?Jacob [00:14:33]: When people are trying to visualize, you're about to make an incision but you want to see, what the cut might look or what the body might look like inside and they can layer in imaging.Swyx [00:14:43]: That's cool.Chai [00:14:45]: At some point in the future.Janie [00:14:46]: But there are a lot of our largest customers and at the largest health systems integrating already and so even as we think about building into it, unlocks a lot of product capabilities.Swyx [00:14:57]: And just to establish the terminology. Sorry, and I know I'm asking basic questions somewhat for myself but also for the audience who might beHealth Systems, Buyers, Clinicians, Patients, and PayersSwyx [00:15:05]: Less integrated. When you say health systems, it's like the Johns Hopkins, the Kaiser Permanentes.Janie [00:15:09]: Mayos, the Kaisers of the world.Swyx [00:15:10]: These are your customers, right? And the outcome that you deliver for them is happier doctors, reduced cost of processing, reduced mistakes. It's weird in a sense that I feel like there's also, a secondary customer, the customer of the customer and I don't know if you — do you think about it that way?Janie [00:15:28]: The other interesting and complex part of building product is we have our buyers, who are the chief medical information officersJanie [00:15:39]: The chief financial officers, the CIOs of these large health systems. Our users today are clinicians but if you think about who downstream is impacted, it's patients. And so as we build, with every product in mind, we think about who we're building for, who the secondary user is and what does that mean either in terms of experience, security compliance, ROI that we have to make tangible. And so like you said, time savings is one of them. But for CFOs, they care a lot more than just time savings. We have to show for every dollar you put into Abridge, because you have more compliant documentation or because you have fewer queries coming from your billing team, we save or add real dollars to your bottom line or top line, are things that we're constantly thinking about because of the dynamic across all three sets of users.Chai [00:16:32]: There's a whole other axis too with the payers and pharmaChai [00:16:35]: as well. Connecting all these three big stakeholders in healthcare isSwyx [00:16:39]: Do the payers ever see your data? Sorry, the payers meaning the insurers, right?Chai [00:16:44]: Yes.Swyx [00:16:44]: They also see Abridge data?Chai [00:16:47]: NoSwyx [00:16:47]: Like the direct integration to you guysChai [00:16:48]: They wouldn't see the raw Abridge data but when you're working together on something like prior authorization, whatever information they need, we'd communicate to them.Jacob [00:16:59]: That's cool. I would love to dig into the AI side. You still have a lot of problems on the AI side. And so maybe to start at the highest level, what's one of the hardest problems you have to solve in AI at Abridge today?The Hardest AI Problems: Quality, Latency, and CostChai [00:17:11]: To make things simple, let's take, building off the prior auth example. So one thing Janie talked about is okay, this data is all over the place and there's this combinatorial explosion of procedures, payer policies and even sometimes different health systems. There can be some cross-product of all of these different considerations you have to take into account. But what's really hard about this problem is doing it real-time in the conversation. So, in any AI product, usually the three KPIs you care about are quality, latency and cost. Now, what we're saying is we want you to do this real-time in the conversation, guiding the clinician. How do we do it in a way that does not break the bank? But we're using — But we also need very intelligent models because you're working with this cross-product of data and this, all this context layer as well. So you need high intelligence and high-quality because you don't want the alert fatigue but you also need to be fast and cost-effective. And so that's where a lot of clever engineering goes. It's okay, without getting into all the details here, can you model these policies in some intermediate representation or other things that you can do that can make this problem tractable? And of course, the Pareto frontier is always changing but we are also trying to do this now.Model Strategy: Third-Party Models, Proprietary Data, and Medical ConversationsJacob [00:18:26]: What implications has that had for what you take off-the-shelf and say, “ what? We don't need to be world-class at X. We'll just take this from the model providers or from some infrastructure player,” and what you're “No, this is where we spend most of our time focused on”?Chai [00:18:38]: This is, the fun challenge in AI?Jacob [00:18:42]: It changes every three months? SoChai [00:18:42]: Of course, with the shifting landscape, we try to be extremely thoughtful on predicting the trends of where third-party models are going and where we can uniquely go. And, sometimes when you talk about AI models, we're the models are just going to get infinitely better. But I don't think... It may be in the grandness of time you could say that but, within every month, every quarter, there's specific ways they're getting better. They're training on a lot more, coding data to be better coding agents, for example. And soChai [00:19:14]: We have to think about where are the things that won't — unique data that we're uniquely training on or to step back a little, where is a proprietary model bringing advantage to us is if it can give higher quality or lower cost and latency for similar quality, very similar to many other companies. And when we can do that is when we have proprietary data. So, for example, we have on the order of eighty million or hundreds of millions now getting close to of medical conversations.Jacob [00:19:44]: It's insane.Chai [00:19:45]: This is a unique data set. And this data set, it's very interesting because this data set is effectively a large part of the trace between the patient and the provider. That's where the quote-unquote debugging happens in healthcare. We have these traces at scale, as in as, our CEOs even called it, an exhaust that comes out of our product. And so when you have these traces, that's how you can train better agents on certain use cases, whether it's your transcription diarization use cases or so on or like note generation models and we can do that much cheaper and faster. But we're always also working with these third-party model providers. We closely collaborate with them and that's how we predict where the trends are going. The thing that I think about a lot is that, I know that the model providers are going to train much more on agentic workflows and so forth, so that's great, so that you have a better agentic harness. But the other thing that's interesting is that the model providers, because a large class of the consumer model providers is healthcare queries, that they might, optimize to train a lot of healthcare data to encode the knowledge in its weights. And this is just a great thing for us as well, where the off-the-shelf models can keep bett-getting better at general healthcare information, such that what our strategy is, we have a constellation of models, we can use something for this, that and, we only care about, at the end of the day, the best product experience.EHR as File System: Agentic Workflows and Real-Time InterfacesJacob [00:21:07]: And, you have, overall capabilities improving. I'm curious, as these models get better, is there something you look at and you're “, three months ago, we really couldn't do that but God, the the latest models really allow us to do it”?Chai [00:21:19]: So here's something interesting that I've, been toying with. So all models are... This wasn't super obvious a year ago but now it's become clear and clear that almost every agent is a coding agent underneath the hood? So you give it whatever file system, it can write its own code and so forth. So when you think about within healthcare and the use case that we have, you can think of the EHR effectively like a file system. It's just — it's a storage of all this information. It's a lot of information there that cannot fit into the context window, at least of today's models and you want to use that context effectively for all these product use cases we're talking about. And so if you have better agents that can, manipulate data, read that data, treat it as a file system as we see they're going and we know model companies are investing this way, then that very directly benefits us.Swyx [00:22:09]: Yeah. Okay, cool. Again, just establishing basic things. But we're going back to the model stuff. I'm really interested in double-clicking more on the real-time, element, which is pretty important for both of you. Is it — Is real-time just batches of every one minute, every five minutes? Is that how we do it? Or is there some more native, genuinely real-time in the sense that OpenAI has a real-time API or Gemini has a real-time API?Chai [00:22:35]: Yeah. Yeah. So today it is more on the on the batch basis but there's interestingChai [00:22:41]: Prototypes that we have that we're still not fully, full time, voice in text out or in that sense. But, can you trigger your models, your agents or agentic workflows, depending on the right times in the conversation?Chai [00:22:58]: And so you can imagine, different techniques to bring this latency down and, you want to bring the feedback loop down as much as you can. And so a lot of clever engineering there without fully... Maybe one day we'll do full voice in and text out, train a model to do something like that.Swyx [00:23:15]: You do — People don't want voice in voice out?Chai [00:23:18]: Now we aren't creating experiences that are, during the conversation, inter — It's almost likeSwyx [00:23:25]: Might be too disruptiveChai [00:23:26]: Too disruptive until, who knows, maybe eventually you could have full voice agents once we — the quality and we improve the comfort of the technology. But right now gra — that change is much more gradual and it's more text focus, text out.Janie [00:23:42]: And so much of currently what our product is trying to do is allow a clinician to focus on their patient and maybe at some point but right now patients, clinicians don't want a third voice, at least in a literal voice in that room. And so how do we be there with all the contacts and information ready at hand when there's the right moment?Personalization: Individual Doctors, Specialties, and Health SystemsJacob [00:24:03]: Jenny, one thing I'm curious about is how you think about, personalization in the product. I imagine, every doctor is a special snowflake in their own way, has their own way they like to do things. There are probably a bunch of different approaches you could take to doing that, both within the model layer itself but then also just with clever prompting or engineering. How do youJacob [00:24:20]: Deliver on that?Janie [00:24:21]: It's such a good question. Personalization is massive for us. We think about personalization at three levels. The first is at the individual, the second is at the specialty level and then the third is at the health system or the organization level. To your point, there are a lot of individual preferences. You-When a note is produced, it almost is a reflection that is so deeply personal of a doctor's work and how they give care. And so do they have preferences on things like style? They might want bullets versus paragraphs, really concise versus comprehensive. They also might have phrases that they really like to use or the templates that they want every note to be structured. And, we see it in our feedback all the time. We want two spaces in between sentences or I refuse to use this tool. And so that's something that we've had to build in. And the tricky part is how do you make sure that stylistic preferences don't interrupt accuracy and quality and that's something that we've really had to refine and hone over time. Second is at the specialty level. A cardiologist note or workflow is going to look very different from a dermatologist workflow.Jacob [00:25:32]: I assume cardiology notes are the highest stakes for you guys, given your CEO is a cardiologist.Jacob [00:25:36]: It's “Oh my God, make sure we get this one.”Janie [00:25:37]: Shiv, our CEO, is still a practicing cardiologist. He rounds once a month. And so, first call when we want just quick and easy user feedback too.Janie [00:25:46]: But, specialties require a lot of personalization, both in terms of what does the product look and so we make sure that as new users onboard, we catch that and the product proportionally reflects that. But also on the back end, evals at the specialty level, they are hard-earned to calibrate and get. What does a really great dermatology note look like? What makes it complete? What makes it compliant and billable is very different than a primary care doctor. And so it's not just about what does the product experience look but on the back end tuning and really deepening our understanding for the specialists. What does great output look like? And that's, a problem that we need to calibrate internally, externally, online, offline but, takes lots of cycles but is necessary in a high-stakes environment. And then at the health system level, for products like clinical decision support, you have health systems who've spent years or decades refining their best practices and they want to know, “Hey, we love your clinical decision support product but how do we embed our own hospital guidelines into them to inform clinicians before, during or after a visit what brest — best practices should look like?” And as you think about, deepening moats as well, when health systems, trust us with that data, allow us to productize it and directly into the clinical workflow, makes us a really great partner to health systems who want to build something that truly meets their needs, their practicing guidelines.AI Slop, Memory, and Product Data FlywheelsChai [00:27:23]: And I want to add onto that. The for the clinical documentation problem, it's very similar to AI writing that doesn't feel like your own and then we call that slop. But the way I describe one framing of slop is like AI without context. But we have all that context and both the clinicians, can have it and can guide it. And so part of the other interesting exhaust for us is, memory is, one of these new systems recordsChai [00:27:49]: Almost.Janie [00:27:50]: And we also have all the edits people make on our product and when you think about a data flywheel and how we get better over time becomes really powerful as a mechanism to just going deeper in personalization.Jacob [00:28:04]: It's interesting. I love this idea of working with systems on the guidelines they built up over a long time. I feel like so many of the best AI app companies today are... The question is: How do you take the expertise that a law firm or a bank has built up over many years and then add that as context and also a special sauce over, a an AI tool? And so seems like y'all are really doing that very effectively.Janie [00:28:24]: We're now starting to have our customers ask, “What are other customers doing?”Janie [00:28:28]: “And how are they doing it?”Janie [00:28:30]: And as we think about having visibility across such a large set of care being delivered right now, a really interesting place we could also partner.Swyx [00:28:40]: I'm just curious. I — This may be a nothing question but, how different are health system guidelines from each other? Don't they all converge to the same thing? And if not, where do they differ?Chai [00:28:52]: At a really high level, they're going to talk about very similar things but the difference is probably in some more of the details. “Oh, you should refer to specialists only when XYZ conditions are met,” or so forth and maybe different organizations have different practices and guidelines around that. But high level, talking about similar things but the details are what, of course, that shapes the context and the decisions you make.Swyx [00:29:15]: And this all goes into the context engine and it might affect the notes but maybe not.Chai [00:29:21]: The — For these local pathways, we're definitely thinking about it a little more for our clinical decision support product.Chai [00:29:26]: So yeah.Swyx [00:29:27]: Which is your stuff, yeah.Swyx [00:29:28]: And then the memory which you raised, let's just tell us more about that. What have you tried in memory? What's the structure of the memory? What works? What doesn't work?Chai [00:29:38]: There's, of course, many different ways you could do memory, where it's okay, can you bake it into the model weights or can you do it in some external store? For us, what's interesting is, of course, when you think the models are rapidly changing, whether it's in-house or third-party, baking into the model weights, sometimes you worry that it could be a little throwaway. And so, how do you... You need to find a way that you decompose the problem, the preferences from the underlying models and so forth. The thing we're right now most both that's easiest to start with and we're excited about is having, a separate store for memory, where you have, for example, a memory sub-agent that's, working in the background, figuring out what are the important parts of the clinician's actions that we want to remember for the long term. And then you can also imagine, other things where in the — you have background jobs that are running that are collating these, memories similar to Sleep, of course and what other pattern, patterns products do as well. Learning over all these action, all the action data we have, again, note edits, the conversations they did and the actual transcripts.Evals: LFD, LLM Judges, and Clinical SafetyJacob [00:30:40]: What about evals? How in the world do you... It is such a complex product surface area. We would love to hear you riff on that and also how has that evolved? I'm sure you've gotten better at it, so any learnings along the way.Janie [00:30:50]: From an evals perspective, we, from day one when we build any new product or feature, we think about, what does good look like? And there are table stakes things like clinical safety but then you start to get deeper into what does good quality look like. And when you go into something like our core product, there's stuff like style and completeness and there's things like does this note become something that can be billable, which is very high stakes for a health system. We have a number of ways in which we get confidence for this. We have, internal in-house clinicians who do what we call an LFD process to give us our very first pass at is this or isn't this a good enough output, look at the effing data.Jacob [00:31:41]: LFD?Chai [00:31:42]: That's why I was smiling. I was “Is Janie going to mention what it stands for?”Jacob [00:31:46]: I was not... There's like a million acronyms.Jacob [00:31:48]: How am I supposed to know that I don't? So “Oh yeah, of course, an LFD.”Swyx [00:31:51]: I've never heard of LFDs.Chai [00:31:53]: It's a bridge for sure.Janie [00:31:55]: I got through three days and then I had to ask someone.Janie [00:31:58]: I thought it was just me that didn't knowJanie [00:32:01]: It's our internal process.Swyx [00:32:02]: But look at the data as a meme in ML, ‘cause you tend to not look at it. You just want to look at number go up.Chai [00:32:06]: Exactly.Swyx [00:32:07]: But yes.Janie [00:32:08]: But so, we make sure we look at the data and then as we think about all of the components of good output, we, one, create LLM judges across all of these and we make sure with annotated data and either internal or external evaluators, we feel like these judges are calibrated. And then depending on the stakes, we also work with in-house and third-party evaluators across all of these before we ship any big change. And the goal is, in terms of evolution, how do you go from this process taking months, down to weeks, down to days? Some of it is, a true science and ML problem. A lot of it's also just, hard operational work. Have you planned ahead in terms of what you need? Have you really optimized the capacity that you need across all of the different specialties you need? Have you gotten a really good sense of which third parties are great to work with for what use cases? This takes a lot of domain, expertise and, lots of mistakes and errors in figuring that out. And so as much of it is an ML problem, so much of it has also been operational gains that are hugely important, where domain-specific expertise is everything.Specialty-Level Evaluation and Progressive RolloutsJacob [00:33:23]: But it's funny, ‘cause I feel like people talk about healthcare like it's one giant market and the reality isJacob [00:33:26]: It's, dozens and dozens of sub-markets. And so it feels like in your evals you have to build that up across the board, probably.Swyx [00:33:34]: And is specialization the primary cardinality at... That's the word that comes to mind.Janie [00:33:40]: Sometimes, depending on the product or the use case. And so if we're making a note improvement or feature for a particular specialty, definitely but we have products that are for nurses. We have products that, are really aimed at making the document or the output a lot more billable. And so we'll want to work with coding teams and not necessary clinicians. And so likeJacob [00:34:05]: Coding meaning healthcare coding.Janie [00:34:06]: Yes. Yes.Jacob [00:34:07]: NotChai [00:34:07]: Yes. I see you.Swyx [00:34:07]: Other kinds.Janie [00:34:09]: But is this output proportional to the work that was delivered? Is there sufficient documentation to justify the amount that a health system may end up charging? And so, specialty sometimes but also domain, very different across all of the different products that we're working for. And building out that network is, not easy and is where a lot of our operational investments have gone into.Chai [00:34:35]: And I view a lot of analogies to self-driving cars here, where, part of it is we really want progressive rollout of features to test in the real world is this useful? Is this going to work? One big difference compared to past lives is before I'd build a product, maybe I'd alpha it and then I'd like GA it the next week, ‘cause I'm “Go, move fast, ship,” and whatnot. But the mentality is like you... I want to make contact with the reality as quick as possible but I want a progressive rollout. Because as much as I get as large of an offline eval set, I want the distribution of that to match real-life distribution. And over time, by rolling out early, similar to Waymo has a tagline, “The world's most experienced driver,” another thing that can, at least linearly increase for us is, both the size of our evaluation offline and online, that and it all feeds back.Janie [00:35:25]: Something that's been earned over time, speaking of evolution, is just the trust we've gotten with customers. Historically, a lot of these health systems, when they bring on new vendors, their release cycles are quarters, sometimes twice a year. We've gotten our customers onto monthly release cycles, which is pretty fast for health systems but what is more exciting over the last, call it, few quarters, has been, a subset of our customers have said, “We want to innovate with you. We trust you,” and we have a pretty, decent chunk of our customers who say, “We'll develop with you outside of these monthly release cycles. We have a higher tolerance. We know that the stakes are very high but we want to be the first ones using these products, giving you feedback.” And so for a pretty substantial set of our customers, we've been able to convince them to be able to ship, in this gradual way before GA. Something we talk about a lot internally is, trust is earned in drops, earned in buckets and so we still can't do what I used to do when I worked at Loom. We had 30 million users. I'd just be, rolling out experiments left and. The bar is still quite high for iterative rollout but because of the trust we've earned, we're able to learn at pretty high volume very quickly.Privacy, HIPAA, and De-IdentificationSwyx [00:36:45]: Your scale is still pretty huge.Swyx [00:36:47]: One thing I want to... We were going to go into scale? In a sec. One thing I wanted to call up, follow up on evals, which, again, just coming from a generalist engineer point of view, just thinking through what would people be scared of in doing this, the privacy and HIPAAJacob [00:37:00]: Elements of this. I have zero experience in that. What do you have to do? What is surprisingly not that bad?Chai [00:37:06]: So one thing that's really important here from a compliance perspective is very much that any of the data we use needs to be de-identified, any real-world data we use as a basis of online eval sets we're learning from. And so you have to — And there's, very clear, government guidelines, what counts as PHI. And so we've even have built models that can take, for example, a clinical transcript and remove all the key PHI indicators and so you have a scrubbed/de-identified version. And then once you... And so one thing that's important is first you've got to get confidence in that model in the first place? And prove that out. Because, now you have, multiple probabilistic systems on top of each other.Chai [00:37:46]: But once you have that, then you can train on it use it for evaluation and so forth, provided one of the cool things also that you can do from a business side is the right data contracting as well with your partners.Jacob [00:37:57]: Is the anonymization one way? Once it's done, you cannot undo it? Or is there someoneChai [00:38:01]: YesJacob [00:38:02]: Who holds the master key that can... Yeah, okay. So it's one way.Chai [00:38:05]: It's one way. Yeah.Jacob [00:38:06]: That's how it works. I just wanted to... Because, there's a lot of this, learning from feedback and everything that, you would want to debug more but you can't because you just physically don't allow yourself to.Janie [00:38:17]: Some of it's also written in our customer contracts in terms of who can or can't access PHI data, how long do we retain it,Jacob [00:38:27]: Very goodJanie [00:38:27]: Before it gets de-identified. And so we have a pretty high bar for who can access that PHI data, just to make sure that we always respect our customer data and privacy. But that's something that we partner with our customers on too, to make sure that as we want full, as close to precision as possible in that qualityJanie [00:38:48]: We can still use it.Jacob [00:38:50]: But it'll be fascinating to see how that space evolves? Because you think about, I used to work at a company that, did a lot of healthcare data in the cancer space and if you asked, the average cancer patient, “Hey, do you want people, do you want other patients to be able to learn-”Chai [00:39:03]: Take it.Jacob [00:39:03]: “... Learn from your experience?”Chai [00:39:04]: Take it all.Jacob [00:39:05]: They're “Please.”Jacob [00:39:06]: “I'd love, nothing more than for other people to be able to learn fromJacob [00:39:10]: The experience that I had.” And so in the past it was a lot harder to do that learning. But with this technology, that might really be practical and so it'll be fascinating to see how that continues to evolve.Chai [00:39:21]: There's so much in our data set of 100 million conversations.Chai [00:39:26]: You can imagine things like insights that you can give to the clinician. How could you, oh, how could you have reacted to this? In coaching or insights around, which treatments are effective or, like... Because you have this, again, this data source that was never captured before but that's, where, intuition or experience is created from, going back to this idea that the conversation is the agent of truth.Operating at Scale: Reliability, Cost, and Token EfficiencyJacob [00:39:46]: Back to the 100 million conversations, I feel like you have this insane scale that maybe only a few other AI app companies have and everyone else dreams of. So not everyone has had to confront this yet but maybe just talk about some of the challenges of operating at that scale and what, our listeners have to look forward to if they ever get to this level of scale.Chai [00:40:05]: At large and larger in scale, so of course there's a general, infrastructure reliability. When you... In any given startup, you're building the plane while it's flying. So there's some notion of that. But what gets interesting on the AI and ML side for sure is this, as you get at more and more scale, so one, you have the data to first and foremost do this. But, you start thinking about costs or infrastructure in a whole different way at scale versus, a prototype.Chai [00:40:34]: You can use the most expensive model, you can burn as many tokens as you want but when you're doing 100 million conversationsJacob [00:40:41]: Token max on leaderboards are less upsetting than that context.Chai [00:40:45]: . When you're doing that and so that comes for we have the data and we also have the team that's able to post-train based on this and you can optimize for efficiency, especially in areas where you believe that maybe a lot of the quality headroom is less so and you don't expect the other off-the-shelf models to go that way, such that you want to do, efficiency maximization, in terms of compute and tokens.Jacob [00:41:08]: I feel like you guys live in the future in some way where most use cases today are really just in use case discovery mode, where it's “God, I really hope I can find something that can get to scale,” and so you're always going to use the most powerful model. And then the few things that do get to this level of scale, you start to do those optimizations.Chai [00:41:22]: It's a natural trajectory where it's like zero-to-one, we're not talking about any of these optimizations.Chai [00:41:26]: But when maybe we're in the one-to-100 or so forth, then we're in optimization mode and, what works out really well is you've got all this data from zero-to-one that lets you do this.What Comes Next: The Conversation as the Shared Healthcare PlatformJacob [00:41:36]: That's fascinating. I feel like one thing that's so interesting about the Abridge footprint is that you're in the doctor-patient visit in real-time. I always like to say, there's like probably 50 years' worth of product you could build on top of that. What gets each of you, I don't know, what are you most excited about building, either in the short term or medium term or even, long down the line?Janie [00:41:53]: Something that I get really excited about is that the same conversation can serve so many stakeholders. If you think about the conversation, a doctor needs to know what is the documentation, how do I make sure that this fully represent the care I gave? A patient needs to know, “What the heck just happened? This was really overwhelming. What are my next steps?” A payer needs to know, was this the proper and appropriate care given? A pharma company might want to know why isn't this drug being properly used or is there a good candidate for this clinical trial that I'm about to run? And where I get excited is that our product and our platform and our infrastructure can be the same product across all of those things and start to what's today, separate, very expensive, complex systems that serve each one of these stakeholders in very different ways, start to collapse all of that into a singular platform that enables not just more efficiency across the board but also better outcomes for everyone. And, all of us experience healthcare in probably very painful ways and knowing that there is a world in which we can simplify a lot is really exciting to me and it all starts with the conversation.Chai [00:43:15]: It's interesting. Of it very similar to going back to the KPIs that any AI product cares about. How do you increase quality of care? How do you reduce latency to care? And how do you reduce costs? Which is a huge, in healthcareJacob [00:43:28]: They call it the triple aim in healthcare.Chai [00:43:30]: But very similar to building AI products and the thing that really excites me is when we talk about that latency piece, we talked about one example earlier of prior authorization, can you reduce the latency to care? But you can imagine so much more. Oh, as soon as the lab value gets updated, do you have like a background agent that, kicks off and uses all the context to be “Oh, hey, the patient should do this next,” for example. And of flagging that to the clinician who's always in the loop but reducing that latency, to care. And then you can imagine this is much further down the road but it's like even connecting that to the direct patient and the consumer. And so how can you, how can you build a bridge to all of these things?EHR Partnerships and the Clinical Intelligence LayerJacob [00:44:10]: Very cool. The connections piece is just an ever-growing thing. And one of the key partners is the EHR and I wonder what that relationship is like. Will they, look at this as, something that is valuable enough that they want to own someday?Janie [00:44:29]: Our partnerships with the EHR is, we know that we have to be extremely close partners with all the EHRs who we partner with. Being able to not only pull and push all of the data into the right places is, not only table stakes, if we can't do that, health systems don't want to use us. The second and the reality of today is clinicians spend a lot of their days in the EHR. So much of what allowed us to win in the largest health systems was pretty direct and, very close partnerships with some of the largest electronic health records that allowed us to pull and push data with APIs that weren't ready out of the box. And clinicians want to save clicks. Anytime we introduce a new product that, adds two clicks for them in their day, they're “We're not going to use it.”Janie [00:45:21]: They have 15-minute back-to-back appointments with their patients. They're spending, hours during pajama time doing documentation. Every second and every minute counts and so we really think about being deeply integrated into the EHR as also table stakes to getting real usage and adoption. And anything that we build or introduce, we really talk about earn the right internally a lot, which is we have to provide so much value or save so much time that people will use us. But those are the two things that are close to us, is we know that the product won't be used unless it is deeply interoperable.Chai [00:46:01]: And strategically, to your point, it's like what does EHR want to own versus us? EHRs are really focused on the clinical workflows and so forth but some of the things that we're talking about here, I do these traditionally are outside of the domain where it's oh, connecting pairs and providers together with provider policies or the clinical trial matching, as Janie brought up. And so these are, entirely — we position ourselves as building this entirely new intelligence, clinical intelligence layer across, again, providers, pharma and, payers.Chai [00:46:33]: And so that's a it's a whole different ballgame that we try to playChai [00:46:36]: In combination with them.Jacob [00:46:37]: But it's like a different layer of scope.Healthcare AI Regulation, Technical Depth, and What Changed Their MindsJacob [00:46:39]: I'm curious, you are both relatively newcomers to healthcare. People have these, there's lots of futuristic healthcare AI takes of “Oh, everything will look different.”, now that you've been in healthcare for a bit, you live at the edge of AI, what have you, changed your mind on around this, as you think about what healthcare looks like in ten, 20 years? Any updates to your mental model from the time being close to the problems?Chai [00:47:02]: One thing that IChai [00:47:04]: Was hesitant about before and it's a common thing when I'm trying to recruit engineers that people ask me around, is definitely oh, healthcare, heavily regulated space. And it is, rightfully so. You want to keep, the patients at the end of the day safe. But one of the interesting things that, is a that surprised me how much it is coming to the company is there's a lot of really favorable regulatory tailwinds as well. Where you think about, government really wants interoperability between all these systems that we talked about and so agents can access this information. The government just in January, the FDA released updated guidance on clinical decision support, what I work on in such a way that they used to have guidance from like 2022 that required you to have, mention all these options and do all these other things but it's a very forward and forward-looking way. And so for me, what's been really cool to work on is this, there's this very special moment both in AI in general, we all know that but there's a special moment also regulatory in healthcare as well.Janie [00:48:05]: One thing I would call out is for the very reasons things are higher stakes or, potentially considered more difficult in healthcare, it's where some of the hardest AI problems will get solved first, just because the bar is so high. When I first joined, I was “Oh, this is where we'll be on the tail end of where, all of the AI innovation will be able to be applied.” But when you think about, zero error evals or multi-step workflows that have really low tolerance, a lot of the innovation will happen here just because we have to or else we can't ship.Jacob [00:48:42]: ‘Cause like in other domains, you'd much rather just solve the 80%-is-good-enough problems firstJanie [00:48:46]: 80/20 doesn't work hereChai [00:48:48]: And building off that, traditionally, there was a bit of stigma that, oh, healthcare companies are not that interesting from a technical perspective or I've seen that or faced that myself. But these are really hard and fun problems from a pure technical perspective beyond just the impact. How do you bring the latency of this thing down and make it really high-quality?Reducing Latency: Clinical Workflows, Agents, and Implementation RealityJacob [00:49:07]: How do you bring the latency of things down?Chai [00:49:10]: Yeah. Yeah. Yeah. So okay, let's answer the latency question. And maybe hopefully not too redundant with some of the things I've said earlier but some part of it is with any latency, you have to like what is, what is really your bottleneck. In a lot of workflows, it's sometimes it's the model itself. And so that's where like our data flywheel, our post-training team and so forth come in so that can you make the models far more efficient. So that's one aspect of latency. But there's whole other aspects of latency where it's okay, on top of that, if you use a constellation of different models, can you use — can you first use like a — it's like thinking fast and slow. Can you use a cheap, fast model that triages and hands it off to a larger model where you get more intelligence and so forth and so all theseChai [00:49:56]: Clever tricks to make it work.Chai [00:49:58]: And by the way, we are totally — we also realize that the parameter frontier is changing and so these tricks will — may not get us to where we want to be in five years but we need to if we want to build a useful product right now.Jacob [00:50:11]: Should we go to the quick-fire or you want to ask more about Abridge? We can stuff everything that's not Abridge into the quick-fireSwyx [00:50:16]: I don't mind. I was — I feel like Janie was on the topic of more long tail stuff, which isSwyx [00:50:21]: Not the eighty/twenty thing and that really matters. And I'll —, if you have any tips or cool stories or just general approaches that have worked for you that's interesting to dig into.Janie [00:50:32]: One of them is even just how we staff our teams looks different than a traditional software engineering team, I'd say.Swyx [00:50:40]: Let's go.Clinician Scientists, Edge Cases, and Evals at ScaleJanie [00:50:41]: We have a bunch of folks with different roles who are clinicians and so we have this role called the clinician scientist and I heard one of our leaders refer to them as mutants recently. But they are people who've had clinical backgrounds, so MDs typically, who are also deeply technical, somewhere, on the spectrum of like a full stack engineer all the way to like extremely scrappy prompter. But having each of these people embedded within our teams instantly raises the bar for everything that we build because not only are they determining, is this product clinically useful but they're deeply embedded in our whole evals process. And so when we talk about LFDs, when we talk about what is our actual evaluation criteria, you don't want Chai or me creating what those are because we don't have clinical background. But is probably unique to Abridge but has been game changing. And when you think about where the puck is going, you have people build with clinical backgrounds who are technical and where AI tools are going, they just becomeJanie [00:51:53]: More and more, critical and like the killers of the team. And so that's one. And then the second is just the scale at which we do evals to catch that long tail up front before anything ever gets into production is something that we've pretty much like really started to fine-tune, both from a scale but when do we know we need to get several hundred versus several thousand offline responses, what helps us make that quick decision and make this less of an art and as much of a science as possible. But that's also been something we've had to tune over time.Swyx [00:52:27]: And you have partners who opted in to give you those evals.Janie [00:52:31]: So we work either internally or with third-party for offline evals and then we have customers who also agree to give us, whether it's like thumbs up, thumbs down to like choose this or that, a lot of data to get us to what is as close to fully confident as possible.Swyx [00:52:51]: The term that comes to mind isSwyx [00:52:53]: Like active learning on things where you're weak. I feel like it's a lost artSwyx [00:52:58]: Is a lot of the polish that comes into doing something like this.Janie [00:53:02]: Really.Chai [00:53:03]: Hundred percent.Lessons from Glean: Technical Foundations and AI App InfrastructureJacob [00:53:04]: Maybe, on a totally unrelated note, Chai, you had a very, storied run at Glean b

Is your bank being unbundled without realizing it? Most banking leaders are still watching the obvious disruptors. But the bigger threat may be happening behind the scenes, as software platforms, embedded finance, and agentic AI begin to reshape how financial services are delivered and who owns the customer relationship. In this episode, Rex Salisbury, founder of Cambrian, joins me to discuss what these shifts mean for traditional banking, why legacy moats are weakening, how seriously bankers should take Nubank's long-term U.S. potential, and what leaders need to do now to stay relevant. In this episode: • Why financial risk often shows up in strategy long before it shows up in earnings • What Nubank's model signals for the future of competition in banking • Why banks need to think beyond products and toward platforms, ecosystems, and execution #BankingTransformed #Fintech #RetailBanking #DigitalTransformation #AgenticAI #EmbeddedFinance #Nubank #BankStrategy #FutureOfBanking #Cambrian #RexSalisbury

David covers three Wednesday stories: Coinbase laying off 14% of staff, a16z Crypto raising $2.2 billion, and Strategy losing $12.5 billion as CEO Phong Le floats selling bitcoin. We also unpack the AI-first layoff narrative, the potential return of the infra supercycle ($6B+ raised across crypto VCs in 2026), and why a Cambrian explosion of DATs and ETFs might absorb whatever Saylor has to offload. Enjoy! -- TIMESTAMPS: (00:00) Intro (01:17) Coinbase Layoffs (07:15) Nexo Ad (07:50) Coinbase Layoffs (Cont.) (10:30) a16z Crypto Fund 5 (15:17) Nexo Ad (16:09) a16z Crypto Fund 5 (Cont.) (22:30) Strategy Losses FOLLOW THE SHOW › David — https://x.com/dcanellis › The Breakdown — https://x.com/TheBreakdownBW SPONSORS › NEXO Nexo is the premier digital wealth platform. Receive interest on your crypto, borrow against it without selling, and trade a range of assets. Now available in the U.S with 30 days of exclusive privileges. Get started at http://nexo.com/breakdown Get top market insights and the latest in crypto news. Subscribe to the Blockworks Daily Newsletter: https://blockworks.co/newsletter/ DISCLAIMER As always, remember this podcast is for informational purposes only, and any views expressed by anyone on the show are solely their opinions, not financial advice.

Let’s learn about some of the oldest life ever discovered! Further reading: Microbiologists Find Living Microbes in 2-Billion-Year-Old Rock Chart of life extended by nearly 1.5 billion years Show transcript: Back in episode 168 we talked about the longest-lived organisms known, and finished the episode by discussing endoliths. I'll quote from that episode as a refresher. An endolith isn't a particular animal or even a group of related animals. An endolith is an organism that lives inside a rock or other rock-like substance, such as coral. Some are fungi, some lichens, some amoebas, some bacteria, and various other organisms, many of them single-celled and all of them very small if not microscopic. Some live in tiny cracks in a rock, some live in porous rocks that have space between grains of mineral, some bore into the rock. Many are considered extremophiles, living in rocks inside Antarctic permafrost, at the tops of the highest mountains, in the abyssal depths of the oceans, and at least two miles, or 3 km, below the earth's surface. Various endoliths eat different minerals, including potassium, sulfur, and iron. Some endoliths even eat other endoliths. We don't know a whole lot about them, but studies of endoliths found in soil deep beneath the ocean's floor suggest that they grow extremely slowly. Like, from one generation to the next could be as long as 10,000 years, with the oldest endoliths potentially being millions of years old—even as old as the sediment itself, which dates to 100 million years old. That episode was almost five years ago, and in October of 2024 some new information was published. The study mentions the 100-million-year-old limit known so far, where living microorganisms were indeed discovered in geological layers below the ocean floor. But what they found was even older. The scientific team analyzed rock samples from northeastern South Africa, specifically rock that formed when magma cooled below the surface of the earth. It's called the Bushveld Igneous Complex and is very large, very old, and very stable. The team drilled core samples of the rock from 50 feet down, or 15 meters, and cut it into thin slices to examine. To their surprise, they discovered microbial life in the rock's cracks, which were sealed tightly with clay so that nothing should be able to get in or out of the rocks. To be sure the microbes hadn't been introduced during the drilling or preparing process, they used infrared spectroscopy to compare the proteins in the microbes with the proteins caught in the clay. They matched, meaning the microbes had been there as long as the clay had been there, which was basically almost as long as the rocks had been in place. They were also able to verify that yes, the microbes were definitely alive. So, how old are the rocks? TWO BILLION YEARS OLD. Billion with a B! While the individual microbes probably aren't actually that old, the population of microbes has been living in those cracks far within the rock for two billion years. Scientists are excited to learn more about them, because by studying organisms that have been separated from all other life for that long, they can learn about how early life on earth evolved. Even more exciting, at least if you're me, NASA's Perseverance rover on Mars is going to be bringing some rocks back to earth that are about 2 billion years old. Scientists are really excited to see if there is any evidence for microbial life inside the Martian rocks! I know I won't live long enough to see the first macrobial life from another planet, but I really hope I'm alive when we discover the first microbial life. I don't think life is rare on other planets, it's just that the distances are so enormous that getting to another planet and sending information back home is an almost insurmountable problem right now. The closest planets to us are Mars and Venus, and these days Mars just doesn't seem like it would be very habitable for anything but microbes. But microbes can live just about anywhere! Also in 2024, a team from Virginia Tech has put together a chart marking when various life forms started appearing in the fossil record and when they also stopped appearing in the fossil record. Versions of this chart of life have been made before, but they typically only go back to about half a billion years ago, around the time of the Cambrian. Before that, life was much less likely to fossilize, or the rocks containing the fossils have been worn away. The team gathered fossil data from scientists and institutions around the world and compiled it into a chart of life that extends back two billion years. The farther back you look, the less changes there are among the type and differences in species. There's even a huge stretch of time called the boring billion where things really weren't changing much at all, at least not according to the fossil record we have available. It wasn't until the earth's climate became much cooler and then warmed again, between 720 and 635 million years ago, that things really began to change. The team is considering factors that contributed to the stability of the boring billion, and why it all changed so radically. It's a good thing it did from our perspective, since if the boring billion had continued over the next billion years until today, we'd all be single-celled organisms. I wonder if the microbes in those two billion year old rocks even noticed the changes. Probably not. They were in rocks. Thanks for your support, and thanks for listening!

The world is full of food, but in order to make the most of it, living organisms need to be able to break it down and extract the useful nutrients. This episode, we explore the fundamental process of digestion and the many, many ways that life has adapted this process to various diets. And we'll take a look at what the fossil record has to tell us about ancient diets and the evolutionary history of digestive systems. In the news: predator bites, coelacanth lungs, Cambrian claws, and squid evolution. Time markers: Intro & Announcements: 00:00:00 News: 00:05:55 Main discussion, Part 1: 00:38:10 Main discussion, Part 2: 01:23:30 Patron question: 02:06:50 Check out our website for this episode's blog post and more: http://commondescentpodcast.com/ Join us on Patreon to support the podcast and enjoy bonus content: https://www.patreon.com/commondescentpodcast Got a topic you want to hear about? Submit your episode request here: https://commondescentpodcast.com/request-a-topic/ Lots more ways to connect with us: https://linktr.ee/common_descent The Intro and Outro music is “On the Origin of Species” by Protodome. More music like this at http://ocremix.org Musical Interludes are "Professor Umlaut" by Kevin MacLeod (incompetech.com). Licensed under Creative Commons: By Attribution 3.0 http://creativecommons.org/licenses/by/3.0

Cambrian Park Plaza, a strip mall in San Jose, was once the heart of the neighborhood. Featuring a bowling alley, grocery store, post office, clothing stores and more, it had everything nearby residents might need. But it's faded significantly since it's heyday in the 1960s-1980s. Now the smattering of stores that are there are on short term leases, and may storefronts sit empty. On today's show, we explore the fight to redevelop this space, and the market conditions that have made moving forward a challenge. It's the story of one neighborhood, yes. But it's also the story of San Jose, and in many ways the entire San Francisco Bay Area. Additional Resources: Cambrian Park Plaza, a Beloved San Jose Strip Mall, Awaits a New Future Read the transcript for this episode Sign up for our newsletter Got a question you want answered? Ask! Your support makes KQED podcasts possible. You can show your love by going to https://kqed.org/donate/podcasts This story was reported by Katrina Schwartz. Bay Curious is made by Katrina Schwartz, Christopher Beale and Olivia Allen-Price. Additional support from Jen Chien, Katie Sprenger, Maha Sanad, Ethan Toven-Lindsey and everyone on Team KQED. Learn more about your ad choices. Visit megaphone.fm/adchoices

Is the Philippines the next major tech hub in Southeast Asia? In this episode of BRAVE, venture capitalist Jeremy Au sits down with Paulo Campos, Founding Managing Partner of Kaya Founders. From leaving a lucrative career at Boston Consulting Group (BCG) to pioneering cash-on-delivery (COD) as a co-founder of ZALORA Philippines, Paulo has been at the forefront of Southeast Asia's digital revolution. We dive deep into the "Cambrian explosion" of startups in Manila following the 2020 pandemic, why returning "sea turtles" (diaspora talent) are reshaping the local economy, and what it really takes to build a sustainable, profitable tech company in today's venture capital climate. Whether you are building in Calabarzon or raising funds in Singapore, this is a masterclass in emerging market entrepreneurship. 0:00 - Introduction & Growing Up in 1990s Manila 4:40 - A Grandfather's Lesson: "The Best Way to Help is to Create Jobs" 7:55 - Choosing the Philippines Over Wall Street & Ayala Group 11:54 - Harvard Business School (HBS) & Discovering Venture Capital 14:00 - Why BCG is the Ultimate Training Ground for Founders 20:50 - The Leap of Faith: Leaving Corporate to Co-Found ZALORA 28:00 - Building a Company in a 10sqm Room & "Founder Energy" 29:10 - The Masterstroke: Unlocking E-Commerce with Cash on Delivery (COD) 34:50 - The Pandemic Pivot & Angel Investing in Local Builders 38:40 - Launching Kaya Founders to Fuel the Philippine VC Ecosystem 44:20 - The "Language Lock" Advantage & The Return of the Diaspora 51:50 - The Bravery Required to Build and Invest in Tech Watch, listen or read the full insight at https://www.bravesea.com/blog/paulo-campos-kaya-founders-zalora Get transcripts, startup resources & community discussions at https://www.bravesea.com WhatsApp: https://whatsapp.com/channel/0029VakR55X6BIElUEvkN02e TikTok: https://www.tiktok.com/@jeremyau Instagram: https://www.instagram.com/jeremyauz Twitter X : https://x.com/jeremyau LinkedIn: https://www.linkedin.com/company/bravesea English: Spotify | YouTube | Apple Podcasts Bahasa Indonesia: Spotify | YouTube | Apple Podcasts Chinese: Spotify | YouTube | Apple Podcasts #Philippines #VentureCapital #Startup #Podcast #southeastasia #techpodcast

At first glance, anemones look like soft blossoms anchored to rock, their tentacles swaying with the tide. But look a little closer and you'll see a skilled predator at work. Each of those delicate arms is armed with nematocysts—microscopic, harpoon-like cells loaded with venom—ready to stun passing prey in a split second.Sea anemones belong to the class Anthozoa, making them close relatives of corals and jellyfish. Unlike jellyfish, though, they've traded a life of drifting for one firmly planted in place, attaching themselves to reefs, rocks, and seafloors across the globe—from shallow tide pools to the deep sea.Now, for us fossil folk, anemones present a bit of a challenge. They are soft-bodied, with no shells or bones to readily fossilise. So their presence in the fossil record is rare—more whisper than shout.But we do have some beautiful clues.Exceptional fossil sites, like the Burgess Shale in British Columbia—dating back over 508 million years—have preserved soft-bodied organisms in stunning detail. Here, we find anemone-like creatures that give us a glimpse into early anthozoan life during the Cambrian Explosion, a time when complex life was just beginning to flourish in Earth's oceans.We also find trace fossils—subtle impressions left in ancient seabeds. Circular marks and anchoring traces hint at where anemones once lived, even when their bodies themselves have long since vanished.Modern anemones also host fascinating partnerships. Many live in symbiosis with algae, gaining energy from photosynthesis, while others form famous alliances—like clownfish weaving safely among their stinging tentacles.So while they may seem delicate, anemones are ancient survivors—holding fast through mass extinctions and vast shifts in Earth's history.

Send a textWhat does the Bible actually teach about creation? Is Genesis literal? What are the main Christian views on old earth vs young earth? And where do dinosaurs, fossils, and the flood fit into all of this?In this episode, I sit down with Eric Hovind from Creation Today to talk through the major creation views, where old earth theories came from, why Genesis matters theologically, and how this connects to death, sin, and the gospel itself.We also get into:creation vs evolutiongap theory, day-age theory, and framework hypothesisdinosaurs and dragonsfossil records and the floodwhy soft tissue in dinosaur bones matterswhy this debate is about more than scienceWhether you're brand new to this conversation or have wrestled with it for years, this episode will give you a lot to think through.If you enjoyed this episode, share it with a friend and leave a review.Find Eric Hovind / Creation Today:https://creationtoday.org/?srsltid=AfmBOorm6-x9TKK08sxSP5JecV5SVGziTrjdjWfTJ1rJzrfYQXbOo2gA Creation Today and Eric Hovind on YouTube, X, Instagram, Facebook and more Search Creation tool mentioned in the episode: searchcreation.orgHer Theology Book Club: We're reading Christian classics together in community. Find out more at: https://hertheology.supercast.com/Timestamps:00:00 Intro: creation, evolution, old earth, new earth, dinosaurs 01:56 Why Eric Hovind is passionate about creation apologetics 04:09 What are the main Christian views on creation? 09:01 Where old earth theories came from 15:38 Is Genesis poetic or literal? 24:01 Why Genesis matters theologically 28:17 Sin, death, and the logic of the gospel 31:11 The genre argument and reading Genesis 36:41 Were dinosaurs real? 41:03 Dinosaurs, the flood, and extinction 46:53 Dinosaur soft tissue and DNA discussion 52:44 How accurate are dinosaur reconstructions? 57:02 Fossils, flood geology, and the Cambrian explosion 01:02:22 Global flood stories across cultures 01:04:50 Search Creation AI tool 01:06:08 Where to find Eric Hovind 01:07:12 Final encouragement: trust God's WordFollow @hertheology on Instagram & YouTube. Head to hertheology.com to find out more.

If you aren't elbow-deep in the code, you're flying blind. We are currently witnessing a "Cambrian explosion" of AI, and most investors are watching from the sidelines without a clue of what's actually happening under the hood.In this episode of Demo Day, Adam Struck, Managing Partner at Struck Capital, explains why he fundamentally changed his firm's DNA to survive the AI revolution. Adam argues that the traditional venture capital model is no longer enough; to truly understand tech innovation in 2026, you have to be a builder. By launching Struck Studio and hiring full-time PhD AI researchers, Adam has gained proprietary insights into how AI agents are about to dismantle the traditional B2B SaaS landscape.In this masterclass on founder success and AI strategy, we discuss:The Building Requirement: Why "vanilla" VC is dying and why proprietary access to deal flow now requires operational expertise.Systems of Action vs. Systems of Record: Why agentic workflows are eating the software budget and moving into the labor budget.The 2026 Pivot: Why 2025 was the year of "Intelligence," but 2026 is officially the year of AI Memory.Founder Resilience: The "punched in the face" philosophy that separates unicorn founders from the rest.The Death of "Per-Seat" Pricing: How AI agents are forcing a total rethink of unit economics and enterprise sales.Whether you're a founder looking for startup fundraising tips or an investor trying to navigate the venture capital landscape, this episode is a wake-up call. Adam shares the "spidey sense" he uses to identify winning teams and why he moved his entire firm's focus from GPT to Google Gemini for agentic reasoning.Stop watching from 30,000 feet and start building.

Starting with a mini celebration: Dave defends his ski racing crown, before Sam declares software dead and capitalism broken. Even among GPs at Upfront Summit, the mood is uncertain: nobody knows whether to invest in software anymore, and many are quietly struggling to raise.The debate heats up over whether AI will democratize software creation or just accelerate capitalism's race to zero margins. Sam argues that when intelligence becomes abundant, it becomes worthless, making the entire AI industry, and by extension Silicon Valley, "pretty bad business." Dave counters that we're about to see a Cambrian explosion of software creators, finally giving billions of people agency over their digital lives.Plus: whether Stripe should buy PayPal during this opportunistic Trump-administration window, Gen Z panic-buying original iPods, Sam's shitposting-to-funding pipeline, and whether OnlyFans has the best KYC in fintechChapters:We're also on ↓X: https://twitter.com/moreorlesspodInstagram: https://instagram.com/moreorlessYouTube: https://youtu.be/Ff4-vkt5rYQConnect with us here:1) Sam Lessin: https://x.com/lessin2) Dave Morin: https://x.com/davemorin3) Jessica Lessin: https://x.com/Jessicalessin4) Brit Morin: https://x.com/brit

The gang discuss two papers of odd fossils with exceptional preservation. The first paper looks at some Cambrian vertebrates and shows that soft tissue evidence suggests the presence of two sets of camera eyes (four eyes total), and they interpret the additional set of camera eyes as being a homolog to the modern parietal eye in vertebrates. The second paper uses exceptional preservation of the Rhynie Chert to test hypotheses for the taxonomic placement of the enigmatic Prototaxites and finds evidence that suggests it is not, as previously suggested, a fungus. Meanwhile, James is marooned by weather, Amanda accidentally traumatizes her cat, and Curt imagines the flesh trees.   Up-Goer Five (Curt Edition): The friends talk about things that are weird. The first paper looks at a thing that is part of the big group that we are all a part of but is from a long long time ago and lived in the big blue wet thing. This thing has four eyes. Two of those eyes might be the things that become a part of the brain that is not the eyes today. But this shows that, early on, some of these animals could have had four eyes. This also means some animals we see later could have had parts of these other eyes that we have thought were other things. The second paper looks at a thing that is weird that people thought was from a group that is not an animal but has some animal like things like eating other things but has walls in the cells. These weird things are from a long time ago and come from a place where the parts were saved from breaking down by glass getting inside the cells. This means you can see lots of cell stuff, and you can also break down the glass to get at some of the cell bits. This paper looks at a lot of this weird thing and they say that it is not part of the group people thought it was from. In fact, it is so weird that it is not like any group we have today. It is maybe something that is not around today that we did not know about.   References: Loron, Corentin C., et al. "Prototaxites fossils are structurally and chemically distinct from extinct and extant Fungi." Science advances 12.4 (2026): eaec6277. Lei, Xiangtong, et al. "Four camera-type eyes in the earliest vertebrates from the Cambrian Period." Nature (2026): 1-6.

In this episode, I'm joined by Mandy Mooney — author, corporate communicator, and performer — for a wide-ranging conversation about mentorship, career growth, and how to show up authentically in both work and life.   We talk about her path from performing arts to corporate communications, and how those early experiences shaped the way she approaches relationships, leadership, and personal authenticity. That foundation carries through to her current role as VP of Internal Communications, where she focuses on building connections and fostering resilience across teams.   We explore the three pillars of career success Mandy highlights in her book Corporating: Three Ways to Win at Work — relationships, reputation, and resilience — and how they guide her approach to scaling mentorship and helping others grow. Mandy shares practical strategies for balancing professional responsibilities with personal passions, and why embracing technology thoughtfully can enhance, not replace, human connection.   The conversation also touches on parenting, building independence in children, and the lessons she's learned about optimism, preparation, and persistence — both in the workplace and at home.   If you're interested in scaling mentorship, developing your career with intention, or navigating work with authenticity, this episode is for you. And if you want to hear more on these topics, catch Mandy speaking at Snafu Conference 2026 on March 5th. 00:00 Start 02:26 Teaching Self-Belief and Independence Robin notes Mandy has young kids and a diverse career (performing arts → VP of a name-brand company → writing books). Robin asks: "What are the skills that you want your children to develop, to stay resilient in the world and the world of work that they're gonna grow up in?" Emphasis on meta-skills. Mandy's response: Core skills She loves the question, didn't expect it, finds it a "thrilling ride." Observes Robin tends to "put things out there before they exist" (e.g., talking about having children before actually having them). Skill 1: Envisioning possibilities "Envision the end, believe that it will happen and it is much more likely to happen." Teaching children to see limitless possibilities if they believe in them. Skill 2: Independence Examples: brushing their own hair, putting on clothes, asking strangers questions. One daughter in Girl Scouts: learning sales skills by approaching strangers to sell cookies. Independence builds confidence and problem-solving abilities for small and big life challenges. Skill 3: Self-belief / Self-worth Tied to independence. Helps children navigate life and career successfully. Robin asks about teaching self-belief Context: Mandy's kids are 6 and 9 years old (two girls). Mandy's approach to teaching self-belief Combination of: Words Mandy uses when speaking to them. Words encouraged for the children to use about themselves. Example of shifting praise from appearance to effort/creativity: Instead of "You look so pretty today" → "Wow, I love the creativity that you put into your outfit." Reason: "The voice that I use, the words that I choose, they're gonna receive that and internalize it." Corrective, supportive language when children doubt themselves: Example: Child says, "I'm so stupid, I can't figure out this math problem." Mandy responds: "Oh wow. That's something that we can figure out together. And the good news is I know that you are so smart and that you can figure this out, so let's work together to figure it out." Asking reflective questions to understand their inner thoughts: Example: "What's it like to be you? What's it like to be inside your head?" Child's response: "Well, you worry a lot," which Mandy found telling and insightful. Emphasizes coming from a place of curiosity to check in on a child's self-worth and self-identity journey. 04:30 Professional Journey and Role of VP of Internal Comms Robin sets up the question about professional development Notes Mandy has mentored lots of people. Wants to understand: Mandy's role as VP of Internal Communications (what that means). How she supports others professionally. How her own professional growth has been supported. Context: Robin just finished a workshop for professionals on selling themselves, asking for promotions, and stepping forward in their careers. Emphasizes that she doesn't consider herself an expert but learns from conversations with experienced people like Mandy. Mandy explains her role and path Career path has been "a winding road." Did not study internal communications; discovered it later. Finds her job fun, though sometimes stressful: "I often think I might have the most fun job in the world. I mean, it, it can be stressful and it can't, you know, there are days where you wanna bang your head against the wall, but by and large, I love my job. It is so fun." Internal communications responsibility: Translate company strategy into something employees understand and are excited about. Example: Translate business plan for 2026 to 2,800 employees. Team's work includes: Internal emails. PowerPoints for global town halls. Speaking points for leaders. Infusing fun into company culture via intranet stories (culture, customers, innovation). Quick turnaround on timely stories (example: employee running seven marathons on seven continents; story created within 24 hours). Storytelling and theater skills are key: Coaching leaders for presentations: hand gestures, voice projection, camera presence. Mandy notes shared theater background with Robin: "You and I are both thespian, so we come from theater backgrounds." Robin summarizes role Sounds like a mix of HR and sales: supporting employee development while "selling" them on the company. Mandy elaborates on impact and mentorship Loves making a difference in employees' lives by giving information and support. Works closely with HR (Human Resources) to: Provide learning and development opportunities. Give feedback. Help managers improve. Wrote a book to guide navigating internal careers and relationships. Mentorship importance: Mentors help accelerate careers in any organization. Mandy's career journey Started studying apparel merchandising at Indiana University (with Kelley School of Business minor). Shifted from pre-med → theater → journalism → apparel merchandising. Took full advantage of career fairs and recruiter networking at Kelley School of Business. "The way that I've gotten jobs is not through applying online, it's through knowing somebody, through having a relationship." First role at Gap Inc.: rotational Retail Management Training Program (RMP). Some roles enjoyable, some less so; realized she loved the company even if some jobs weren't ideal. Mentor influence: Met Bobby Stillton, president of Gap Foundation, who inspired her with work empowering women and girls. Took a 15-minute conversation with Bobby and got an entry-level communications role. Career growth happened through mentorship, internal networking, and alignment with company she loved. Advice for her daughters (Robin's question) Flash-forward perspective: post-college or early career. How to start a career in corporate / large organizations: Increase "luck surface area" (exposure to opportunities). Network in a savvy way. Ask at the right times. Build influence to get ahead. Mentorship and internal relationships are key, not just applying for jobs online. 12:15 Career Advice and Building Relationships Initial advice: "Well first I would say always call your mom. Ask for advice. I'm right here, honey, anytime." Three keys to success: Relationships Expand your network. "You say yes to everything, especially early in your career." Examples: sit in on meetings, observe special projects, help behind the scenes. Benefits: Increases credibility. Shows people you can do anything. Reputation Build a reputation as confident, qualified, and capable. Online presence: Example: LinkedIn profile—professional, up-to-date, connected to network. Be a sponsor/advocate for your company (school, office, etc.). Monthly posts suggested: team photos, events, showing responsibility and trust. Offline reputation: Deliver results better than expected. "Deliver on the things that you said you were gonna do and do a better job than people expected of you." Resilience Not taught from books—learned through experience. Build resilience through preparation, not "fake it till you make it." Preparation includes: practicing presentations, thinking through narratives, blocking time before/after to collect thoughts and connect with people. "Preparation is my headline … that's part of what creates resilience." Mandy turns the question to Robin: "I wanna ask you too, I mean, Robin, you, you live and breathe this every day too. What do you think are the keys to success?" Robin agrees with preparation as key. Value of service work: Suggests working in service (food, hospitality) teaches humility. "I've never met somebody I think even ever in my life who is super entitled and profoundly ungrateful, who has worked a service job for any length of time." Robin's personal experience with service work: First business: selling pumpkins at Robin's Pumpkin Patch (age 5). Key formative experience: running Robin's Cafe (2016, opened with no restaurant experience, on three weeks' notice). Ran the cafe for 3 years, sold it on Craigslist. Served multiple stakeholders: nonprofit, staff (~15 employees), investors ($40,000 raised from family/friends). Trial by fire: unprepared first days—no full menu, no recipes, huge rush events. Concept of MI Plus: "Everything in its place" as preparation principle. Connecting service experience to corporate storytelling: Current business: Zandr Media (videos, corporate storytelling). Preparation is critical: Know who's where, what will be captured, and what the final asset looks like. Limited fixes in post-production, even with AI tools. Reinforces importance of preparation through repeated experience. Advice for future children / young people: Robin would encourage service jobs for kids for months or a year. Teaches: Sleep management, personal presentation, confidence, energy. "Deciding that I'm going to show up professionally … well … energetically." Emphasizes relentless optimism: positivity is a superpower. Experience shows contrast between being prepared and unprepared—learning from both is crucial. 16:36 The Importance of Service Jobs and Resilience Service jobs as formative experience: Worked as a waitress early in her career (teenager). Describes it as "the hardest job of my life". Challenges included: Remembering orders (memory). Constant multitasking. Dealing with different personalities and attitudes. Maintaining positivity and optimism through long shifts (e.g., nine-hour shifts). Fully agrees with Robin: service jobs teach humility and preparation. Optimism as a superpower: "I totally agree too that optimism is a superpower. I think optimism is my superpower." Writes about this concept in her book. Believes everyone has at least one superpower, and successful careers involve identifying and leaning into that superpower. Robin asks about the book Why did Mandy write the book? Inspiration behind the book? Also wants a deep dive into the writing process for her own interest. Mandy's inspiration and purpose of the book Title: "Corporating: Three Ways to Win At Work" Primary goal: Scale mentorship. Realized as she reached VP level, people wanted career advice. Increased visibility through: Position as VP. Connection with alma mater (Indiana University). Active presence on LinkedIn. Result: Many young professionals seeking mentorship. Challenge: Not sustainable to mentor individually. Solution: Writing a book allows her to scale mentorship without minimizing impact. Secondary goals / personal motivations: Acts as a form of "corporate therapy": Reflects on first 10 years of her career. Acknowledges both successes and stumbles. Helps process trials and tribulations. Provides perspective and gratitude for lessons learned. Fun aspect: as a writer, enjoyed formatting and condensing experiences into a digestible form for readers. Legacy and contribution: "I had something that I could contribute meaningfully to the world … as part of my own legacy … I do wanna leave this world feeling like I contributed something positive. So this is one of my marks."   21:37 Writing a Book and Creative Pursuits Robin asks Mandy about the writing process: "What's writing been like for you? Just the, the process of distilling your thinking into something permanent." Mandy: Writing process and finding the "25th hour" Loves writing: "I love writing, so the writing has been first and foremost fun." Where she wrote the book: Mostly from the passenger seat of her car. She's a working mom and didn't have traditional writing time. Advice from mentor Gary Magenta: "Mandy, you're gonna have to find the 25th hour." She found that "25th hour" in her car. Practical examples: During birthday party drop-offs: "Oh good. It's a drop off party. Bye. Bye, honey. See you in two hours. I'll be in the driveway. In my car. If you need anything, please don't need anything." Would write for 1.5–2 hours. During Girl Scouts, swim, any activity. On airplanes: Finished the book on an eight-hour flight back from Germany. It was her 40th birthday (June 28). "Okay, I did it." Realization moment: "You chip away at it enough that you realize, oh, I have a book." Robin: On parents and prioritization Parents told him: "When you have kids, you just find a way." Children create: Stricter prioritization. A necessary forcing function. Mandy's self-reflection: "I believe that I am an inherently lazy person, to be totally honest with you." But she's driven by deadlines and deliverables. Kids eliminate "lazy days": No more slow Saturdays watching Netflix. "They get up. You get up, you have to feed these people like there's a human relying on you." Motherhood forces motivation: "My inherent laziness has been completely wiped away the past nine years." Writing happened in small windows of time. Importance of creative outlet: Having something for yourself fuels the rest of life. Examples: writing, crocheting, quilting, music. Creativity energizes other areas of life. Robin mentions The 4-Hour Workweek by Tim Ferriss. Advice from that book: Have something outside your day job that fuels you. For Robin: Physical practice (gym, handstands, gymnastics, ballet, capoeira, surfing). It's a place to: Celebrate. Feel progress. Win, even if work is struggling. Example: If tickets aren't selling. If newsletter flops. If client relationships are hard. Physical training becomes the "anchor win." Mandy's writing took over two years. Why? She got distracted writing a musical version of the book. There is now: "Corporating: The Book" "Corporating: The Musical" Three songs produced online. Collaboration with composer Eric Chaney. Inspiration from book: Time, Talent, Energy (recommended by former boss Sarah Miran). Concept: we have limited time, talent, and energy. Advice: Follow your energy when possible. If you're flowing creatively, go with it (unless there's an urgent deadline). You'll produce better work. She believes: The book is better because she created the musical. Musical helps during speaking engagements. Sometimes she sings during talks. Why music? Attention spans are short. Not just Gen Z — everyone is distracted. Music keeps people engaged. "I'm not just gonna tell you about the three ways to win at work. I'm gonna sing it for you too." Robin on capturing attention If you can hold attention of: Five-year-olds. Thirteen-year-olds. You can hold anyone's attention. Shares story: In Alabama filming for Department of Education. Interviewed Alabama Teacher of the Year (Katie). She has taught for 20 years (kindergarten through older students). Observed: High enthusiasm. High energy. Willingness to be ridiculous to capture attention. Key insight: Engagement requires energy and presence. 28:37 The Power of Music in Capturing Attention Mandy's part of a group called Mic Drop Workshop. Led by Lindsay (last name unclear in transcript) and Jess Tro. They meet once a month. Each session focuses on improving a different performance skill. The session she describes focused on facial expressions. Exercise they did: Tell a story with monotone voice and no facial expressions. Tell the story "over the top clown like, go really big, something that feels so ridiculous." Tell it the way you normally would. Result: Her group had four people. "Every single one of us liked number two better than one or three." Why version two worked best: When people are emotive and expressive: It's more fun to watch. It's more entertaining. It's more engaging. Connection to kids and storytelling: Think of how you tell stories to five-year-olds: Whisper. Get loud. Get soft. Use dynamic shifts. The same applies on stage. Musical integration: Music is another tool for keeping attention. Helps maintain engagement in a distracted world. Robin: Hiring for energy and presence Talks about hiring his colleague Zach Fish. Technical producer for: Responsive Conference. Snafu Conference. Freelancer Robin works with often. Why Robin hires Zach: Yes, he's technically excellent. But more importantly: "He's a ball of positive energy and delight and super capable and confident, but also just pleasant to be with." Robin's hiring insight: If he has a choice, he chooses Zach. Why? "I feel better." Energy and presence influence hiring decisions. Zach's background: Teaches weekly acrobatics classes for kids in Berkeley. He's used to engaging audiences. That translates into professional presence. Robin: Energy is learnable When thinking about: Who to hire. Who to promote. Who to give opportunities to. Traits that matter: Enthusiasm. Positivity. Big energy. Being "over the top" when needed. Important insight: This isn't necessarily a God-given gift. It can be learned. Like music or performance. Like anything else. 31:00 The Importance of Positive Work Relationships Mandy reflects on: The tension between loud voices and quiet voices. "Oftentimes the person who is the loudest is the one who gets to talk the most, but the person who's the quietest is the one who maybe has the best ideas." Core question: How do you exist in a world where both of those things are true? Parenting lens: One daughter is quieter than the other. Important to: Encourage authenticity. Teach the skill of using your voice loudly when needed. It's not about changing personality. It's about equipping someone to advocate for themselves when necessary Book is targeted at: Students about to enter the corporate world. Early-career professionals. Intentional writing decision: Exactly 100 pages. Purpose: "To the point, practical advice." Holds attention. Digestible. Designed for distracted readers. Emotional honesty: Excited but nervous to reconnect with students. Acknowledges: The world has changed. It's been a while since she was in college. Advice she's trying to live: Know your audience Core principle: "Get to know your audience. Like really get in there and figure out who they are." Pre-book launch tour purpose: Visiting universities (including her alma mater). Observing students. Understanding: Their learning environment. Their day-to-day experiences. The world they're stepping into. Communication principle: Knowing your audience is essential in communications. Also essential in career-building. If you have a vision of where you want to go: "Try to find a way to get there before you're there." Tactics: Meet people in those roles. Shake their hands. Have coffee. Sit in those seats. Walk those halls. See how it feels. Idea: Test the future before committing to it. Reduce uncertainty through proximity. What if you don't have a vision? Robin pushes back thoughtfully: What about people who: Don't know what they want to do? Aren't sure about staying at a company? Aren't sure about career vs. business vs. stay-at-home parent? Acknowledges: There's abundance in the world. Attention is fragmented. Implied tension: How do you move forward without clarity? 35:13 Mentorship and Career Guidance How to help someone figure out what's next Start with questions, not answers A mentor's primary job: ask questions from a place of curiosity Especially when someone is struggling with what they want to do or their career direction Key questions: What brings you joy? What gives you energy? What's the dream? Imagine retirement — what does that look like? Example: A financial advisor made Mandy and her husband define retirement vision; then work backwards (condo in New Zealand, annual family vacations) Clarify what actually matters Distinguish life priorities: Security → corporate job; Teamwork → corporate environment; Variety and daily interaction → specific roles Mentoring becomes a checklist: Joy, strengths, lifestyle, financial expectations, work environment preferences Then make connections: Introduce them to people in relevant environments, encourage informational interviews You don't know what you don't know Trial and error is inevitable Build network intentionally: Shadow people, observe, talk to parents' friends, friends of friends Even experienced professionals have untapped opportunities Stay curious and do the legwork Mixing personal and professional identity Confidence to bring personal interests into corporate work comes from strategy plus luck Example: Prologis 2021, senior leaders joked about forming a band; Mandy spoke up, became lead singer CEO took interest after first performance, supported book launch She didn't always feel this way Early corporate years: Feel like a "corporate robot," worrying about jargon, meetings, email etiquette, blending in Book explores blending in while standing out Advice for bringing full self to work Don't hide it, but don't force it; weave into casual conversation Find advocates: Amazing bosses vs terrible ones, learn from both Mentorship shaped her framework: Relationships, reputation, and resilience Resilience and rejection Theater as rejection bootcamp: Auditions, constant rejection Foundations of resilience: Surround yourself with supportive people, develop intrinsic self-worth, know you are worthy Creating conditions for success Age 11 audition story: Last-minute opportunity, director asked her to sing, she sang and got the part Why it worked: Connections (aunt in play), parent support, director willing to take a chance, she showed up Resilience is not just toughing it out: Have support systems, build self-worth, seek opportunity, create favorable conditions, step forward when luck opens a door 44:18 Overcoming Rejection and Building Resilience First show experiences Robin's first stage production is uncertain; she had to think carefully At 17, walked into a gymnastics gym after being a cross country runner for ten years, burnt out from running Cold-called gyms from the Yellow Pages; most rejected her for adult classes, one offered adult classes twice a week That led to juggling, circus, fencing, capa, rock climbing — a "Cambrian explosion" of movement opportunities About a year and a half later, walked into a ballet studio in corduroy and a button-up, no ballet shoes; first ballet teacher was Eric Skinner at Reed College, surrounded by former professional ballerinas First internal college production was his first show; ten years later performed as an acrobat with the San Francisco Opera in 2013, six acrobats among 200 people on stage, four-hour shows with multiple costume changes and backflips Relationship to AI and the evolving world of work Mandy never asks her daughters "What do you want to be?" because jobs today may not exist in the future Focus on interests: plants, how things are built, areas of curiosity for future generations Coaching her team: Highly capable, competent, invested in tools and technology for digital signage, webinars, emails, data-driven insights, videos Approach AI with cautious optimism: Adopt early, embrace technology, use it to enhance work rather than replace it Example: Uses a bot for scheduling efficiency, brainstorming; enhances job performance by integrating AI from day one Advice: Approach AI with curiosity, not fear; embrace tools to be smarter and more efficient, stay ahead in careers 53:05 Where to Find Mandy Mandy will be speaking at Snafu Conference on March 5, discussing rejection and overcoming it. Author and speaking information: mandymooney.com LinkedIn: Mandy Mooney Music available under her real name, Mandy Mooney, on streaming platforms.  

The first multicellular animals to build reefs lived in the Early Cambrian around the time of the Cambrian explosion. They were sponges called archaeocyaths. In the podcast, Sara Pruss suggests that the rise of the archaeocyaths fostered an increase in animal diversity. But they were relatively short-lived, and when they died out in the Middle Cambrian, the diversity declined. Over geological time, reef-building organisms appear and disappear again and again until the corals we have today appeared in the Middle Triassic, about 240 million years ago.Pruss is currently trying to understand why reefs are such a persistent feature of the geological record, despite the environmental stresses imposed on them. She is a Professor of Geosciences at Smith College.

Thomas Halliday concludes with the climate-driven Ordovician mass extinction, the Cambrian explosion of modern animal body plans in China featuring predators like Omnidens, and the Ediacaran era's strange soft-bodied organisms preceding complex life.

The gang discusses two papers that use fragmentary fossils of animals to investigate the origins of major groups. The first paper describes an Early Ordovician eurypterid, and the second paper looks at mosaic evolutionary patterns in an early squamate. Meanwhile, James has bird opinions, Curt delights in not knowing, and Amanda will definitely be on time.   Up-Goer Five (Curt Edition): The friends look at two papers that are using broken bits of things to learn a lot about animals from a long time ago. Both of these papers are looking at old animals that may give us new looks at how big groups of animals changed over time. These animals may be some of the first animals in these groups, or at least let us know what kinds of things those early animals could have been doing. The first paper looks at a group of animals that lived in the big blue wet thing a long time ago and are part of a group that today has animals that make homes that they use to catch food. The new parts this paper finds shows that this group may have come around a lot earlier than we thought. The second paper looks at parts from an animal that is in a group that is cold and has hard skin, some with legs and some without legs. These parts show that the early animals in this group had a lot of changes going on in their hard parts, maybe they changed more early on then they do today.   References: Benson, Roger BJ, et al. "Mosaic anatomy in an early fossil squamate." Nature (2025): 1-7. Van Roy, Peter, Jared C. Richards, and Javier Ortega-Hernández. "Early Ordovician sea scorpions from Morocco suggest Cambrian origins and main diversification of Eurypterida." Proceedings of the Royal Society B: Biological Sciences 292.2058 (2025).

A survey of the Earth's history through the lens of the geologic timescale. We begin with a discussion of the terminology of geochronologic units and how they are specified using 'golden spikes'. We then review the development of Earth's atmosphere, geosphere, and biosphere through the Hadean, Archean, Proterozoic, and Phanerozoic eons. We cover many topics including the supercontinent cycle, the great oxidation event, the evolution of eukaryotes, the Cambrian explosion, and mass extinctions. Recommended pre-listening is Episode 156: Fossils and Dating Methods. If you enjoyed the podcast please consider supporting the show by making a PayPal donation or becoming a Patreon supporter. https://www.patreon.com/jamesfodor https://www.paypal.me/ScienceofEverything

Join Lionel on The Other Side of Midnight for a dive into the ultimate questions of existence, stripping away the "hippie dippy" nonsense to get to the core of critical thinking. Lionel challenges the "long gradual ladder" of evolution with the sudden complexity of the Cambrian explosion and the "irreducible complexity" of biological machines. From debating "Christian by birth" labels to analyzing the "God gene" and the passing of Dilbert creator Scott Adams, no topic is off-limits. Whether discussing the physics of the universe or why George Burns made a great Deity, Lionel argues that actions speak louder than dogma—and he'd rather "lick a belt sander" than talk about boring news stories. Learn more about your ad choices. Visit megaphone.fm/adchoices

Buckle up for a wild ride on The Other Side of Midnight. It begins with a fiery "Warrior Wednesday" as Lionel and Lynn Shaw declare war on Big Tech, exposing the "digital harms" creating an anxious generation and calling for bans on social media for minors. The conversation then takes a sharp turn into the Hudson Valley woods to hunt for Bigfoot, where Lionel asks why skeptics demand HD photos of Sasquatch but not of the Almighty. Finally, dive into the legend of the Jersey Devil, debate the logic of Noah's Ark versus the Cambrian explosion, and strip away the "hippie dippy" nonsense of existence—because Lionel would rather "lick a belt sander" than talk about boring news. Learn more about your ad choices. Visit megaphone.fm/adchoices

Lionel dissects the origins of the Jersey Devil, tracing the legend back to Mother Leeds and a feud with Benjamin Franklin. The conversation shifts to the philosophy of belief, challenging callers on how they distinguish between cryptids like Bigfoot, the "Shadow Man," and religious figures. From the logistics of Noah's Ark to the Cambrian explosion, Lionel debates creationism versus evolution, mocks the "watchmaker" argument, and wonders how you would explain a Bic lighter to a caveman. Learn more about your ad choices. Visit megaphone.fm/adchoices

What do carnival sideshows, government paperwork, and half-billion-year-old nightmare creatures have in common? In this episode of The Box of Oddities, Kat and Jethro explore three very different corners of history where certainty was offered in place of understanding—and where things were far stranger than advertised. First, they step into the vanished world of early 20th-century hygiene exhibits: traveling carnival attractions that promised education but delivered fear. Set up alongside Ferris wheels and midway games, these sterile tents used wax models, shock imagery, and moral absolutism to teach the public what would happen if they failed to behave “correctly.” Disease was framed as punishment. Fear wasn't a side effect—it was the lesson. Then, in a Thing in the Middle, the focus shifts from bodies to paperwork. Kat and Jethro examine bizarre bureaucratic oddities: citizens declared dead while still alive, laws that regulate technologies no longer in use, records preserved on media that can no longer be read. It's a reminder that systems meant to create order can quietly lose track of reality. Finally, the episode dives deep into the Cambrian Explosion, a brief moment in geological time when life experimented wildly with form. From five-eyed predators to spined worms reconstructed upside-down for decades, these ancient creatures reveal a world where evolution hadn't settled on any final draft yet—and where “normal” hadn't been invented. Across carnivals, governments, and deep time, a pattern emerges: confidence without nuance, spectacle over explanation, and the human desire to make complicated worlds feel simple. The tents are gone.The paperwork remains.The creatures are fossilized. But the urge to replace understanding with certainty is still very much alive. Learn more about your ad choices. Visit megaphone.fm/adchoices

Evolution says that life began with the simplest forms. It took over a billion years just to evolve algae and another billion years for living things to have more than one cell. It took half a billion years of slow development to generate today's creatures. And evolution says that this story comes from the fossil record.What most people do not know is that there is no such story in the fossil record. And when not writing textbooks or appearing on television, evolutionary scientists will admit that their story of life cannot be found in the fossil record. According to the fossil record, every major family alive today appears suddenly and fully formed in the Cambrian rocks, which contain the first clear evidences of developed life.Charles Darwin was aware of this. Believing his own theory to be true, he called this problem a real mystery and wrote that it is probably a valid argument against evolution. Darwin wrote that he expected the problem to be solved as more fossils were discovered. But today, well over a century later, the problem remains and was written about in recent history in the Scientific American.So, Christians should not feel intimidated by the claims of scientists. We Christians have our faith by which we interpret what we see in the world. But the evolutionary story of life and the fossils is nothing more than the interpretation of the world according to evolutionary faith. We agree that far greater faith is required to believe in the revelation of Charles Darwin than to believe the revelation of God.Luke 19:40"But he answered and said to them, "I tell you that if these should keep silent, the stones would immediately cry out."Prayer: Dear Lord; Men mock what You have revealed in Your Word and try to intimidate Your people by telling us how ignorant our beliefs are. Give Your people, beginning with me, a strong and bold faith in Your revealed Word. In Jesus' Name. AmenREF.: Marland & Rudwick. The great Intra-Cambrian ice age. Scientific American. To support this ministry financially, visit: https://www.oneplace.com/donate/1232/29?v=20251111

First throwback of the new year and we're starting strong with one of Wales' most criminally inclined exports. Hear all about this Cambrian bad boy in the full eppy here.

From creating SWE-bench in a Princeton basement to shipping CodeClash, SWE-bench Multimodal, and SWE-bench Multilingual, John Yang has spent the last year and a half watching his benchmark become the de facto standard for evaluating AI coding agents—trusted by Cognition (Devin), OpenAI, Anthropic, and every major lab racing to solve software engineering at scale. We caught up with John live at NeurIPS 2025 to dig into the state of code evals heading into 2026: why SWE-bench went from ignored (October 2023) to the industry standard after Devin's launch (and how Walden emailed him two weeks before the big reveal), how the benchmark evolved from Django-heavy to nine languages across 40 repos (JavaScript, Rust, Java, C, Ruby), why unit tests as verification are limiting and long-running agent tournaments might be the future (CodeClash: agents maintain codebases, compete in arenas, and iterate over multiple rounds), the proliferation of SWE-bench variants (SWE-bench Pro, SWE-bench Live, SWE-Efficiency, AlgoTune, SciCode) and how benchmark authors are now justifying their splits with curation techniques instead of just "more repos," why Tau-bench's "impossible tasks" controversy is actually a feature not a bug (intentionally including impossible tasks flags cheating), the tension between long autonomy (5-hour runs) vs. interactivity (Cognition's emphasis on fast back-and-forth), how Terminal-bench unlocked creativity by letting PhD students and non-coders design environments beyond GitHub issues and PRs, the academic data problem (companies like Cognition and Cursor have rich user interaction data, academics need user simulators or compelling products like LMArena to get similar signal), and his vision for CodeClash as a testbed for human-AI collaboration—freeze model capability, vary the collaboration setup (solo agent, multi-agent, human+agent), and measure how interaction patterns change as models climb the ladder from code completion to full codebase reasoning. We discuss: John's path: Princeton → SWE-bench (October 2023) → Stanford PhD with Diyi Yang and the Iris Group, focusing on code evals, human-AI collaboration, and long-running agent benchmarks The SWE-bench origin story: released October 2023, mostly ignored until Cognition's Devin launch kicked off the arms race (Walden emailed John two weeks before: "we have a good number") SWE-bench Verified: the curated, high-quality split that became the standard for serious evals SWE-bench Multimodal and Multilingual: nine languages (JavaScript, Rust, Java, C, Ruby) across 40 repos, moving beyond the Django-heavy original distribution The SWE-bench Pro controversy: independent authors used the "SWE-bench" name without John's blessing, but he's okay with it ("congrats to them, it's a great benchmark") CodeClash: John's new benchmark for long-horizon development—agents maintain their own codebases, edit and improve them each round, then compete in arenas (programming games like Halite, economic tasks like GDP optimization) SWE-Efficiency (Jeffrey Maugh, John's high school classmate): optimize code for speed without changing behavior (parallelization, SIMD operations) AlgoTune, SciCode, Terminal-bench, Tau-bench, SecBench, SRE-bench: the Cambrian explosion of code evals, each diving into different domains (security, SRE, science, user simulation) The Tau-bench "impossible tasks" debate: some tasks are underspecified or impossible, but John thinks that's actually a feature (flags cheating if you score above 75%) Cognition's research focus: codebase understanding (retrieval++), helping humans understand their own codebases, and automatic context engineering for LLMs (research sub-agents) The vision: CodeClash as a testbed for human-AI collaboration—vary the setup (solo agent, multi-agent, human+agent), freeze model capability, and measure how interaction changes as models improve — John Yang SWE-bench: https://www.swebench.com X: https://x.com/jyangballin Chapters 00:00:00 Introduction: John Yang on SWE-bench and Code Evaluations 00:00:31 SWE-bench Origins and Devon's Impact on the Coding Agent Arms Race 00:01:09 SWE-bench Ecosystem: Verified, Pro, Multimodal, and Multilingual Variants 00:02:17 Moving Beyond Django: Diversifying Code Evaluation Repositories 00:03:08 Code Clash: Long-Horizon Development Through Programming Tournaments 00:04:41 From Halite to Economic Value: Designing Competitive Coding Arenas 00:06:04 Ofir's Lab: SWE-ficiency, AlgoTune, and SciCode for Scientific Computing 00:07:52 The Benchmark Landscape: TAU-bench, Terminal-bench, and User Simulation 00:09:20 The Impossible Task Debate: Refusals, Ambiguity, and Benchmark Integrity 00:12:32 The Future of Code Evals: Long Autonomy vs Human-AI Collaboration 00:14:37 Call to Action: User Interaction Data and Codebase Understanding Research

From creating SWE-bench in a Princeton basement to shipping CodeClash, SWE-bench Multimodal, and SWE-bench Multilingual, John Yang has spent the last year and a half watching his benchmark become the de facto standard for evaluating AI coding agents—trusted by Cognition (Devin), OpenAI, Anthropic, and every major lab racing to solve software engineering at scale. We caught up with John live at NeurIPS 2025 to dig into the state of code evals heading into 2026: why SWE-bench went from ignored (October 2023) to the industry standard after Devin's launch (and how Walden emailed him two weeks before the big reveal), how the benchmark evolved from Django-heavy to nine languages across 40 repos (JavaScript, Rust, Java, C, Ruby), why unit tests as verification are limiting and long-running agent tournaments might be the future (CodeClash: agents maintain codebases, compete in arenas, and iterate over multiple rounds), the proliferation of SWE-bench variants (SWE-bench Pro, SWE-bench Live, SWE-Efficiency, AlgoTune, SciCode) and how benchmark authors are now justifying their splits with curation techniques instead of just “more repos,” why Tau-bench's “impossible tasks” controversy is actually a feature not a bug (intentionally including impossible tasks flags cheating), the tension between long autonomy (5-hour runs) vs. interactivity (Cognition's emphasis on fast back-and-forth), how Terminal-bench unlocked creativity by letting PhD students and non-coders design environments beyond GitHub issues and PRs, the academic data problem (companies like Cognition and Cursor have rich user interaction data, academics need user simulators or compelling products like LMArena to get similar signal), and his vision for CodeClash as a testbed for human-AI collaboration—freeze model capability, vary the collaboration setup (solo agent, multi-agent, human+agent), and measure how interaction patterns change as models climb the ladder from code completion to full codebase reasoning.We discuss:* John's path: Princeton → SWE-bench (October 2023) → Stanford PhD with Diyi Yang and the Iris Group, focusing on code evals, human-AI collaboration, and long-running agent benchmarks* The SWE-bench origin story: released October 2023, mostly ignored until Cognition's Devin launch kicked off the arms race (Walden emailed John two weeks before: “we have a good number”)* SWE-bench Verified: the curated, high-quality split that became the standard for serious evals* SWE-bench Multimodal and Multilingual: nine languages (JavaScript, Rust, Java, C, Ruby) across 40 repos, moving beyond the Django-heavy original distribution* The SWE-bench Pro controversy: independent authors used the “SWE-bench” name without John's blessing, but he's okay with it (”congrats to them, it's a great benchmark”)* CodeClash: John's new benchmark for long-horizon development—agents maintain their own codebases, edit and improve them each round, then compete in arenas (programming games like Halite, economic tasks like GDP optimization)* SWE-Efficiency (Jeffrey Maugh, John's high school classmate): optimize code for speed without changing behavior (parallelization, SIMD operations)* AlgoTune, SciCode, Terminal-bench, Tau-bench, SecBench, SRE-bench: the Cambrian explosion of code evals, each diving into different domains (security, SRE, science, user simulation)* The Tau-bench “impossible tasks” debate: some tasks are underspecified or impossible, but John thinks that's actually a feature (flags cheating if you score above 75%)* Cognition's research focus: codebase understanding (retrieval++), helping humans understand their own codebases, and automatic context engineering for LLMs (research sub-agents)* The vision: CodeClash as a testbed for human-AI collaboration—vary the setup (solo agent, multi-agent, human+agent), freeze model capability, and measure how interaction changes as models improve—John Yang* SWE-bench: https://www.swebench.com* X: https://x.com/jyangballinFull Video EpisodeTimestamps00:00:00 Introduction: John Yang on SWE-bench and Code Evaluations00:00:31 SWE-bench Origins and Devon's Impact on the Coding Agent Arms Race00:01:09 SWE-bench Ecosystem: Verified, Pro, Multimodal, and Multilingual Variants00:02:17 Moving Beyond Django: Diversifying Code Evaluation Repositories00:03:08 Code Clash: Long-Horizon Development Through Programming Tournaments00:04:41 From Halite to Economic Value: Designing Competitive Coding Arenas00:06:04 Ofir's Lab: SWE-ficiency, AlgoTune, and SciCode for Scientific Computing00:07:52 The Benchmark Landscape: TAU-bench, Terminal-bench, and User Simulation00:09:20 The Impossible Task Debate: Refusals, Ambiguity, and Benchmark Integrity00:12:32 The Future of Code Evals: Long Autonomy vs Human-AI Collaboration00:14:37 Call to Action: User Interaction Data and Codebase Understanding Research Get full access to Latent.Space at www.latent.space/subscribe

In the shadowed hollows where myth bleeds into memory, the devil's footprints mark the ancient soil of Welsh lore. In this spellbinding episode of Time Between Times with Owen Staton, we journey deep into the dark heart of folklore to uncover the devil's many disguises: the horned tempter lurking at crossroads, the twisted whisper in windswept valleys that chills even the bravest soul, and the monstrous forms born of peasant fear and firelight tales. From cavernous caves beneath Cambrian hills to lonely tracks where lost travellers swear they've heard infernal laughter, Welsh tradition paints the devil not only as a tempter of souls but as a trickster spirit shaping the very landscape of fear itself. This isn't just legend — it's the folklore of shadowed hearts and ancient woods coming alive in the dark between the worlds.In this haunting episode, host Owen Staton welcomes special guest Amy Boucher, an expert folklorist, storyteller, and chronicler of ghostly traditions. Amy brings sharp insight and passion to the underbelly of folklore, illuminating how tales of the devil and otherworldly mischief reflect the fears, morals, and imaginations of communities through time. Together, Owen and Amy weave stories that blur the line between history and myth, revealing how tales of the devil endure in our collective psyche.About our guest: Amy Boucher is the writer and folklorist behind the blog Nearly Knowledgeable History — a trove of curious stories, folklore, and cultural insights. Explore Amy's work at https://nearlyknowledgeablehistory.blogspot.com/p/about-me.html. About Owen Staton: Owen is a Welsh storyteller and host of Time Between Times, sharing myths and legends to soothe and spook. Visit his world of tales and blog at https://www.welshstoryteller.com/ and catch his writings at https://owenstaton.substack.com/. Owen's Ko-fi page www.ko-fi.com/owenstatonOwen's Patreon www.patreon.com/owenstaton7Take care my FriendsOwen x

I had the incredible opportunity to bring together some of the brightest minds in the creator economy for an evening of candid conversation about where this industry is headed. From ad tech innovations to creator authenticity, we covered the full spectrum of what it takes to turn creator content into scalable, revenue-generating partnerships. Conor McKenna from Luma and Zoe Soon from the IAB kicked things off with a macro view of the space, discussing how fragmented media is creating massive opportunities for technology to step in. We explored why brands are shifting budgets at unprecedented rates, with Unilever committing 50% of marketing spend to creator-related initiatives.The evening featured deep dives into brand integration strategies with Ali Parish from Blue Hour Studios and Jeremy Stewart from VuePlanner, followed by an eye-opening discussion with Arthur Leopolod from Agentio about how AI and automation are revolutionizing creator advertising. Perhaps most compelling was hearing directly from Sydney Jo, the creator behind the viral Group Chat series, and her manager Haley Friedman from Made By All about the reality of building a creator business. From navigating brand negotiations to maintaining creative authenticity, this conversation revealed both the opportunities and challenges facing the next generation of digital storytellers._______________________________________________Key Highlights

There's a rock layer called the Cambrian where, seemingly out of nowhere, pop fossils from forty major animal groups. It's an explosion of life and diversity.

New @greenpillnet pod out today!

The Science Points to Purpose: A Defense of Intelligent Design.  Stephen Meyer Watch the entire video at- https://youtu.be/3hx6fDOZz7k?si=lUw3u-qpCoWdEXzi John Anderson Media 772K subscribers 47,905 views Premiered Aug 22, 2025 In this conversation, John is joined by Dr. Stephen Meyer who articulates the scientific foundation that supports intelligent design, arguing that the universe's fine-tuning and the digital code in DNA point to a purposeful intelligence. He challenges materialistic assumptions, urging a re-evaluation of life's origins through rigorous scientific reasoning. Stephen analyses the shortcomings of evolutionary theory, explores the Cambrian explosion, and addresses the problem of evil, offering a rational case for theism grounded in modern scientific discoveries. Stephen C. Meyer, PhD, is a philosopher of science, the director of the Center for Science and Culture at the Discovery Institute, and the author of several books, including "Darwin's Doubt: The Explosive Origin of Animal Life and the Case for Intelligent Design," and "The Return of the God Hypothesis." Download his free mini-book "Scientific Evidence For A Creator" at https://www.discovery.org/m/securepdf... Sign up to John's newsletter here: https://johnanderson.net.au/contact/ -------------------------------------------------------------------------------------------------------------- 01:34 - Introduction and Welcome 02:02 - What is Intelligent Design? 06:07 - The Origin of Life Problem 14:46 - Intelligent Design as Pseudoscience? 29:51 - Challenges to Evolutionary Theory 39:34 - Social Implications of Darwinism 49:19 - A New Spirit of Inquiry 59:24 - The Problem of Evil 01:06:20 - The Christian Story and Human Nature -------------------------------------------------------------------------------------------------------------- Conversations feature John Anderson, former Deputy Prime Minister of Australia, interviewing the world's foremost thought leaders about today's pressing social, cultural and political issues. John believes proper, robust dialogue is necessary if we are to maintain our social strength and cohesion. As he puts it; "You cannot get good public policy out of a bad public debate." If you value this discussion and want to see more like it, make sure you subscribe to the channel here:    / @johnandersonmedia   And stay right up to date with all the conversations by subscribing to the newsletter here: https://johnanderson.net.au/contact/ Follow John on X: https://x.com/JohnAndersonAC Follow John on Facebook:   / johnandersonac   Follow John on Instagram:   / johnandersonac   Support the channel: https://johnanderson.net.au/support/ Website: https://johnanderson.net.au/ Podcast: https://open.spotify.com/show/6Qh2fEs... 2QH0QLLWRVNX5LFA -------------------------------------------------------------------------------------------------------------- Follow Stephen on X: https://x.com/StephenCMeyer Subscribe to Stephen's Channel:    / @drstephenmeyer   Follow Stephen on Facebook:   / drstephencmeyer   Website: https://stephencmeyer.org/   --------------------------------------------------------------------  Check out our ACU Patreon page: https://www.patreon.com/ACUPodcast   HELP ACU SPREAD THE WORD!  Please go to Apple Podcasts and give ACU a 5 star rating. Apple canceled us and now we are clawing our way back to the top. Don't let the Leftist win. Do it now! Thanks. Also Rate us on any platform you follow us on. It helps a lot. Forward this show to friends. Ways to subscribe to the American Conservative University Podcast Click here to subscribe via Apple Podcasts Click here to subscribe via RSS You can also subscribe via Stitcher FM Player Podcast Addict Tune-in Podcasts Pandora Look us up on Amazon Prime …And Many Other Podcast Aggregators and sites ACU on Twitter- https://twitter.com/AmerConU . Warning- Explicit and Violent video content.   Please help ACU by submitting your Show ideas. Email us at americanconservativeuniversity@americanconservativeuniversity.com   Endorsed Charities -------------------------------------------------------- Pre-Born! Saving babies and Souls. https://preborn.org/ OUR MISSION To glorify Jesus Christ by leading and equipping pregnancy clinics to save more babies and souls. WHAT WE DO Pre-Born! partners with life-affirming pregnancy clinics all across the nation. We are designed to strategically impact the abortion industry through the following initiatives:… -------------------------------------------------------- Help CSI Stamp Out Slavery In Sudan Join us in our effort to free over 350 slaves. Listeners to the Eric Metaxas Show will remember our annual effort to free Christians who have been enslaved for simply acknowledging Jesus Christ as their Savior. As we celebrate the birth of Christ this Christmas, join us in giving new life to brothers and sisters in Sudan who have enslaved as a result of their faith. https://csi-usa.org/metaxas   https://csi-usa.org/slavery/   Typical Aid for the Enslaved A ration of sorghum, a local nutrient-rich staple food A dairy goat A “Sack of Hope,” a survival kit containing essential items such as tarp for shelter, a cooking pan, a water canister, a mosquito net, a blanket, a handheld sickle, and fishing hooks. Release celebrations include prayer and gathering for a meal, and medical care for those in need. The CSI team provides comfort, encouragement, and a shoulder to lean on while they tell their stories and begin their new lives. Thank you for your compassion  Giving the Gift of Freedom and Hope to the Enslaved South Sudanese -------------------------------------------------------- Food For the Poor https://foodforthepoor.org/ Help us serve the poorest of the poor Food For The Poor began in 1982 in Jamaica. Today, our interdenominational Christian ministry serves the poor in primarily 17 countries throughout the Caribbean and Latin America. Thanks to our faithful donors, we are able to provide food, housing, healthcare, education, fresh water, emergency relief, micro-enterprise solutions and much more. We are proud to have fed millions of people and provided more than 15.7 billion dollars in aid. Our faith inspires us to be an organization built on compassion, and motivated by love. Our mission is to bring relief to the poorest of the poor in the countries where we serve. We strive to reflect God's unconditional love. It's a sacrificial love that embraces all people regardless of race or religion. We believe that we can show His love by serving the “least of these” on this earth as Christ challenged us to do in Matthew 25. We pray that by God's grace, and with your support, we can continue to bring relief to the suffering and hope to the hopeless.   Report on Food For the Poor by Charity Navigator https://www.charitynavigator.org/ein/592174510  

Charles Wong, CEO and Co-founder of Bifrost AI, tells Host Llewellyn King and Co-host Adam Clayton Powell III that we are in a "Cambrian explosion moment" with humanoid robotics, "where someone drops a product that is so powerful, so general that everyone will want one." Exciting.

The gang discusses two papers that have very little in common with each except for the word "stem". The first paper uses birth death models to simulate the fossil record in order investigate if neutral models can produce patterns similar to the "crown"/"stem" evolutionary dynamics that have been observed in real data. The second paper investigates stem mandibulate fossils to investigate the timing of major key innovations in the evolutionary history of this arthropod group. Meanwhile, Amanda decides, James bullies, and Curt explains.   Up-Goer Five (Curt Edition): The friends talk about two papers that have very little to do with each other, other than the fact that they have one of the same words in them. The first paper looks at the ways in which animals change over time and how they make more of each other and how the ways things live and die can make it look like there are some groups that do better than others. The paper shows that some of this is something we should see even if it is just because of how things make more things and the fact that we care more about the things that live today than the things that do not live today. The second paper looks at how animals that have many parts that repeat make their arms and legs. This paper looks at very very old animals from groups that are not around today but maybe could be close to those groups. The group of animals today that this group is close to has a lot of things that all of them share, like that they make mouths from a lot of arms, and also they have things on the front they use to feel things, and that they are three parts. This paper is using these old animals that are close to this group to try and see which things today in this group appeared first, and which things may have taken some time before they appeared.   References: Budd, Graham E., and Richard P. Mann. "The dynamics of stem and crown groups." Science Advances 6.8 (2020): eaaz1626. Liu, Yao, et al. "A tiny Cambrian stem-mandibulate reveals independent evolution of limb tagmatization and specialization in early euarthropods." Scientific Reports 15.1 (2025): 19115.

Matt Turck (VC at FirstMark) joins the show to break down the most controversial MAD (Machine Learning, AI, and Data) Landscape yet. This year, the team "declared bankruptcy" and cut over 1,000 logos to better reflect the market reality: a "Cambrian explosion" of AI companies and a fierce "struggle and tension between the very large companies and the startups".Matt discusses why incumbents are "absolutely not lazy" , which categories have "largely just gone away" (like Customer Data Platforms and Reverse ETL) , and what new categories (like AI Agents and Local AI) are emerging. We also cover his investment thesis in a world dominated by foundation models, the "very underestimated" European AI scene , and whether an AI could win a Nobel Prize by 2027.https://www.mattturck.com/mad2025

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Join Ellen & special guest, zoology and science communication powerhouse Lindsay Nikole, for a review of some of the animal kingdom's greatest hits throughout the history of the Earth. We discuss ecological gossip, mass extinctions, the Cambrian explosion and evolution's “experimental” phase, swimming potatoes with googly eyes, giant bugs, bizarre prehistoric sharks, imaginary friend lore, and so much more.Links:Pre-order your copy of Lindsay's book, Epic Earth!Follow Lindsay on Instagram, YouTube, and TikTok!For more information about us & our podcast, head over to our website!Follow Just the Zoo of Us on BlueSky, Facebook, Instagram & Discord!Follow Ellen on BlueSky!

We've covered AI's massive power appetite in depth over the past year – with good reason. It's the driving force behind much of the change and uncertainty in the energy world right now, from the error bars around our demand for electricity to the lineup of technologies vying to meet that demand.  In this episode Shayle talks to his colleague Andy Lubershane, head of research and partner at Energy Impact Partners, about five big questions arising in this uncertain load-growth environment. They cover topics like: The underappreciated factors that could flip the supply crunch to oversupply, like algorithmic efficiency gains, on-device inference, and off-grid data centers The winners of the AI-drive power boom, including utilities and grid equipment suppliers, and the potential losers like industry that relies on cheap power Whether there will be a “Cambrian explosion” or consolidation of nuclear reactors designs The prospects for enhanced geothermal after Fervo's Cape Station comes online The future of grid-enhancing technologies like advanced conductors and dynamic line ratings, and whether they will make it out of “utility pilot hell” Resources: Steel for Fuel: Why does nobody know how much energy AI will consume? Open Circuit: How do we know if we're in an AI bubble?   Catalyst: The US nuclear groundswell   Catalyst: How geothermal gets built   Latitude Media: In Georgia, stakeholders still can't agree on data center load growth numbers   Credits: Hosted by Shayle Kann. Produced and edited by Daniel Woldorff. Original music and engineering by Sean Marquand. Stephen Lacey is our executive editor.  Catalyst is brought to you by EnergyHub. EnergyHub helps utilities build next-generation virtual power plants that unlock reliable flexibility at every level of the grid. See how EnergyHub helps unlock the power of flexibility at scale, and deliver more value through cross-DER dispatch with their leading Edge DERMS platform, by visiting energyhub.com. Catalyst is brought to you by Bloom Energy. AI data centers can't wait years for grid power—and with Bloom Energy's fuel cells, they don't have to. Bloom Energy delivers affordable, always-on, ultra-reliable onsite power, built for chipmakers, hyperscalers, and data center leaders looking to power their operations at AI speed. Learn more by visiting BloomEnergy.com.

In this engaging conversation, Rachel Ignotofski discusses her new book Dinosaurs, exploring the fascination with these ancient creatures, the impact of mass extinctions, and the evolution of life on Earth. She highlights the importance of paleontology, the legacy of Mary Anning, and the artistic choices made in illustrating the book. The discussion also touches on the audience for the book, quirky anecdotes from paleontological history, and the significance of understanding deep time in relation to our current ecosystem.AD| To sign up for The Curiousity Box go to http://curiositybox.com/BreakingMath and get 25% off your first box with breakmath25Takeaways Most of us fall in love with dinosaurs around the age of six. Dinosaurs and birds evolved together, sharing the Earth. There have been five major mass extinctions in Earth's history. Nature always bounces back after mass extinctions. Paleontology is constantly evolving with new discoveries. Mary Anning was a pioneer in paleontology, often overlooked. Dinosaurs were not just big lizards; they were diverse and complex. The Cambrian explosion marked a significant evolutionary milestone.Chapters 00:00 The Fascination with Dinosaurs 03:42 Mass Extinctions and Geological Time 06:16 Paleontology and Misconceptions 09:08 Mary Anning: The Mother of Paleontology 11:53 Evolution of Dinosaurs and Marine Reptiles 13:06 The Evolution of Whales 13:42 The Cambrian Explosion and Ancient Creatures 16:12 Favorite Time Periods in Prehistory 18:48 The Book's Audience and Its Appeal 19:03 Anecdotes from the Fossil World 21:53 Art and Illustrations in Science 26:11 The Vastness of Earth History 28:21 Upcoming Events and Future ProjectsFollow Rachel Ignotofsky on Twitter, Instagram, Website, and find her new book here.Subscribe to Breaking Math wherever you get your podcasts.Follow Breaking Math on Twitter, Instagram, LinkedIn, Website, YouTube, TikTokFollow Autumn on Twitter, BlueSky, and InstagramBecome a guest hereemail: breakingmathpodcast@gmail.com

HEADLINE: Cambrian Explosion, Apex Predators, and the Morphological Mysteries of the Ediacaran BOOK TITLE: Other Lands GUEST AUTHOR NAME: Thomas Halliday 200-WORD SUMMARY: This final segment explores the deep past, focusing on the Cambrian and Ediacaran periods. The Cambrian (520 million years ago) is known as the Cambrian Explosion, where representatives of all modern phyla (body plans) emerged, including early vertebrates. Sites like Chengjiang, China, illustrate this diversification. The apex predator of this era was Omnidens, a six-foot-long, many-legged arthropod that fed using a circular, spined mouth array. The emergence of predation fundamentally altered evolution, driving the development of armor, hard teeth, and the origin of eyes. Prior to the Cambrian was the Ediacaran (550 million years ago). Ediacaran organisms, which existed in a relatively peaceful pre-Cambrian world, were morphologically distinct from later life. Examples include the spiral-shaped Eoandromeda. The sea floor during this time was stabilized by microbial mats. Though life was bizarre, scientists are confident in classifying early life forms; for example, organisms like Dickinsonia are confirmed animals based on unique chemical markers such as cholesterol. Living Stromatolites (mounds of microbes) that persist today also existed during this time.

On this episode, NLW goes deep on OpenAI's release of Sora 2—its next-generation video generation model—and the launch of the new Sora social app, which some are calling an AI-powered TikTok. Is this the beginning of a Cambrian explosion of creativity, or just the next wave of AI brain rot? The show explores what makes Sora 2 different, how the cameo feature could reshape social media, the early cultural backlash, and what this moment says about society's growing rebellion against attention-draining digital platforms.

Ben Shapiro, Dr. Stephen Meyer. Why Science Still Needs God. ACU Saturday Series. Why Science Still Needs God. Dr. Stephen Meyer x Ben Shapiro Darwinian evolution is often used to dismiss belief in God, but what if the science tells a different story? In this interview, Dr. Stephen Meyer Cambridge PhD and author of Darwin's Doubt—joins Ben Shapiro to explain why the standard evolutionary narrative falls short. From the sudden explosion of life in the Cambrian period to the discovery of digital code in DNA, Meyer reveals how science increasingly points toward purpose, not chance. Watch this video at- https://youtu.be/BgRtIbrMUQM?si=B50vJ0y_JHrLoq6_ Stephen Meyer 56.2K subscribers 3,809 views Jul 18, 2025 ====================================================== Are you interested in the origins of life and the universe? Get this free book and explore the debate between Darwinian evolution and intelligent design. If you're intrigued by the origins of life, this is a must-read. It might change the way you view our world. As a special gift Dr. Meyer would like you to download his 32-page mini-book Scientific Evidence for a Creator for FREE: https://evolutionnews.org/_/sefac This is the official Youtube page of Dr. Stephen C. Meyer, director of Discovery Institute's Center for Science & Culture. Meyer received his Ph.D. in the philosophy of science from the University of Cambridge. His latest book is Return of the God Hypothesis: Three Scientific Discoveries that Reveal the Mind Behind the Universe. He is also the author of The New York Times best selling book Darwin's Doubt: The Explosive Origin of Animal Life and the case for Intelligent Design (HarperOne, 2013), and Signature In The Cell: DNA and the Evidence for Intelligent Design (2009). For more information about Dr. Meyer, his research, and his books visit https://stephencmeyer.org/. The Center for Science & Culture is the institutional hub for scientists, educators, and inquiring minds who think that nature supplies compelling evidence of intelligent design. The CSC supports research, sponsors educational programs, defends free speech, and produce articles, books, and multimedia content. Visit other YouTube channels connected to the Center for Science & Culture Discovery Institute:    / discoveryinstitute   Discovery Science Channel:    / @discoverysciencechannel   Follow Dr. Meyer on social media: X: @StephenCMeyer   / stephencmeyer   Facebook:   / drstephencmeyer     / discoverycsc   Instagram discoverycsc   / discoverycsc   Tik Tok discoverycsc   / discoverycsc