Perception in the absence of external stimulation that has the qualities of real perception
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We dig into two new 2026 studies that audited five popular AI chatbots on health questions, and athletic performance came back as the weakest category of the whole audit. We cover why an LLM is a prediction machine and not an all knowing coach, the sycophancy problem that lets you argue a chatbot into agreeing with whatever you already believed, and the fake citations researchers hit when they asked for references. We also get into the hard line on steroids, peptides, and supplement advice, where AI falls short on injuries and physical therapy, why a wall of jargon is usually a sign nobody understands the question, and how Jerred is thinking about guardrails before an AI coach ever shows up inside the Garage Gym Athlete app. Chapters: 0:03 AI Study Sparks Fitness Warning 4:26 Athletic Performance Scores Worst 8:37 Better Ways to Use AI 12:29 AI's Sycophancy Problem 16:49 Dangerous Advice on Drugs 23:31 Fake Citations and Hallucinations 26:29 Guardrails for Future AI Coaching 34:53 When AI Gets Too Complex
Let's talk about the AI elephant in the room: hallucinations.
Merci à Thierry et Kevin d'être venus sur Legend. Ils ont vécu des expériences de mort imminente après avoir frôlé la mort. Thierry Joleta survécu au crash de l'avion qu'il pilotait au Gabon, faisant 7 morts sur les 10 personnes à bord. Kevin Declercq, lui, a été écrasé par un camion quand il avait 13 ans. Tous les deux se sont vus sortir de leur corps et livrent des témoignages bouleversants. Merci également au Dr. Nicolas Bilbault, neurologue, qui étudie les expériences de mort imminente depuis 20 ans. Hallucinations, molécules ou surnaturel… Il a répondu à toutes les questions sur ce phénomène fascinant. Retrouvez les informations concernant nos invités par ici ⬇️Instagram de Kevin Declercq : https://www.instagram.com/ic_kev?igsh=MXc4bHpnd3hqbzYzeA%3D%3DLe film “Témoins” de Sonia Barkallah sur les EMI : https://www.temoins-lefilm.comRetrouvez les émissions : EXPÉRIENCE DE MORT IMMINENTE : LE PLUS GRAND SPÉCIALISTE MONTRE LES PREUVES DE LA VIE APRÈS LA MORT : https://youtu.be/7FnBWkTQv8EÀ L'ÂGE DE 16 ANS, ELLE EST LA SEULE SURVIVANTE D'UN CRASH D'AVION ! : https://www.youtube.com/watch?v=gmxqzxANGCo&t=165sPour prendre vos billets pour le LEGEND TOUR c'est par ici ➡️ https://www.legend-tour.fr/ Retrouvez la boutique LEGEND ➡️ https://shop.legend-group.fr/
There's been some social media talk about a psychoactive mushroom that causes visions of small people, like the Lilliputians from Gulliver's Travels. This is a rare but real phenomenon with several causes. Dr. Sydnee talks about the documented experiences of seeing these little guys, where they comes from, and what is happening in the brain to cause this. Music: "Medicines" by The Taxpayers https://taxpayers.bandcamp.com/ Native Women Lead: https://www.nativewomenlead.org/ Help support this show and unlock bonus content! Become a member at https://maximumfun.org/joinsawbones
For a show that deals with a plethora of awful movies, this one really stands out. We return this week for another Polonia CLASSIC. John and Mark Polonia star in their own movie that consists around a plot that makes 0 sense. These are their stories. NEXT EPISODE ➟ Paranormal Activity 2 PATREON (BONUS EPISODES, VIDEO CONTENT, AND MORE!) ➟ https://patreon.com/screampodcast SCREAM! SOCIALS: Instagram ➟ https://z-p42.www.instagram.com/screampodcast/ Facebook ➟ https://www.facebook.com/thescreampod/?ref=py_c HORRORMOVIEREQUESTS@YAHOO.COM SCREAMPODCAST@YAHOO.COM HORROR SOUP SOCIALS: Instagram ➟ https://www.instagram.com/horrorsoup/?hl=en YOUTUBE ➟ https://www.youtube.com/c/HorrorSoup LETTERBOXD (MOVIE REVIEW APP) ➟ https://letterboxd.com/horrorsoupcaleb/ ~Music Credits~ ETHAN HURT – WWW.ETHANHURT.COM KYLE HERMAN - @iamkyleherman on Instagram Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.
A wide-ranging conversation about identity, therapy, psychedelics, and artificial intelligence. He shares how his own origin story shaped his understanding of narrative agency, self-knowledge, and the limits of expert systems. The discussion also digs into the promise and risks of combining EMDR, ketamine, MDMA, psilocybin, and AI in therapeutic work. MARK A. MICHAELS, a writer and conceptual artist. Mark's work examines fundamental paradigm dilemmas in both traditional psychotherapy and contemporary psychedelic therapy. In his forthcoming preprint, BINARIES, MULTITUDES, AND BEYOND, Mark focuses the lens on his own journey and uses narrative analysis and critique to explore how authority, expertise, and healing are currently defined. https://markamichaels.com/about/ Key topics In this episode, Mark Michaels explains why he calls himself an "uncontrolled experiment" and how being conceived through artificial insemination shaped his worldview. The conversation covers the secrecy and social shame around sperm donation, infertility, and the long-term effects of not knowing one's biological origins. Mark describes the eight-year process of uncovering his biological father through research and DNA testing, and how that discovery changed his sense of self. He connects that origin story to his interest in identity, narrative agency, psychotherapy, and the promises and limits of science. Mark discusses his early exposure to therapy as a child and his teenage experimentation with LSD, including an early bad trip and later renewed interest in psychedelics. The episode centers on his recent treatment program combining EMDR, ketamine, MDMA, psilocybin, and AI-assisted reflection after a severe post-divorce mental health crisis. Mark argues that ketamine-assisted EMDR can accelerate therapeutic work by making clients more open and reducing psychological defenses. He is sharply critical of therapy culture when it becomes passive, overly rigid, or unwilling to share power with clients. The conversation examines the shortcomings of therapist training, especially when practitioners cling to one modality and fail to support client-led exploration. Mark and the hosts discuss the "wild west" state of psychedelic therapy, including concerns about over-medicalization, bad actors, and unregulated practice. The AI section focuses on both usefulness and danger, including hallucinated facts, false confidence, "kissing my butt" responses, and the risk of reinforcing poor psychological ideas. Mark explains that AI is best used as a high-speed organizer and drafting tool, but only if the user already has enough subject knowledge to challenge errors. The episode closes with Mark encouraging people in crisis to get help, evaluate whether therapy is actually working, and seek support rather than enduring alone. 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For second half of video (when applicable and audio) go to our Substack and Subscribe. https://grimericaoutlawed.substack.com/ or to our Locals https://grimericaoutlawed.locals.com/ or Patreon https://www.patreon.com/grimericaoutlawed Support the show directly: https://open.spotify.com/show/2punSyd9Cw76ZtvHxMKenI?si=ImKxfMHgQZ-oshl499O4dQ&nd=1&dlsi=4c25fa9c78674de3 Watch or Listen on Spotify https://www.simulationmaps.com/#products Disaster Maps, Volcano Sim, Asteroid Sim, Shipwreck Map, UFO Map etc https://grimericacbd.com/ CBD / THC Tinctures and Gummies https://grimerica.ca/support-2/ Our Adultbrain Audiobook Podcast and Website: www.adultbrain.ca Check out our next trip/conference/meetup - Contact at the Cabin www.contactatthecabin.com Join the chat / hangout with a bunch of fellow Grimericans Https://t.me.grimerica grimerica.ca/chats Discord Chats Darren's books www.acanadianshame.ca Sign up for our newsletter http://www.grimerica.ca/news InstaGRAM https://www.instagram.com/the_grimerica_show_podcast/ Purchase swag, with partial proceeds donated to the show www.grimerica.ca/swag ART - Napolean Duheme's site http://www.lostbreadcomic.com/ MUSIC Tru Northperception, Felix's Site sirfelix.bandcamp.com Timestamps 00:00 - Mark Michaels introduction and the intersection of psychotherapy, psychedelics, and AI 02:06 - Why he calls himself an "uncontrolled experiment" 03:11 - How commercial sperm donation worked in the 1950s and 1960s 05:08 - The secrecy around donor insemination and the lack of research on its effects 06:07 - Searching for his biological father and the role of DNA testing 08:40 - Genealogical bewilderment and how his origin story shaped his identity 10:06 - Why he feels uniquely positioned to think about AI and artificial reproduction 11:35 - Early therapy, teenage LSD use, and returning to psychedelics later in life 12:42 - The family crisis that pushed him to investigate his origins 14:33 - The emotional impact of learning the truth about his conception 17:01 - Shame, infertility, and the cultural pressure to become a parent 18:48 - How his background informs his preprint on binaries, multitudes, and beyond 20:19 - Building a treatment protocol with EMDR, ketamine, MDMA, and psilocybin 22:56 - Suicidal ideation, post-divorce crisis, and the need for a different intervention 24:21 - Using recordings and AI transcripts as part of the therapeutic process 25:23 - Reclaiming narrative agency after a decade of stress and distortion 27:05 - Problems with therapy when the therapist refuses to adapt or share power 29:37 - Training gaps and the limits of single-modality thinking in therapy 33:13 - Why intensive therapy may be more effective and cost-efficient than weekly sessions 34:38 - SSRIs, suicidal ideation, and Mark's experience with Prozac and stimulants 37:42 - Concerns about over-regulating or over-medicalizing psychedelic therapy 39:23 - His current ketamine practice as a creative, spiritual, and therapeutic tool 40:18 - Sweat lodges, ceremonial practice, and cultural context 41:18 - Plastic shamans and the long history of spiritual tourism 44:07 - Similarities across indigenous traditions and why they do not mean the same worldview 45:35 - Brain structure, recurring symbols, and the limits of universal explanations 47:56 - AI as a psychological tool and the problem of overly agreeable responses 49:41 - How AI absorbs watered-down pop psychology 50:47 - Hallucinations, invented citations, and the danger of trusting AI blindly 53:44 - What AI is genuinely good at: organizing large amounts of material 54:42 - Why Mark wrote the paper now and what he hopes it sparks 55:43 - Advice for people in crisis: get help and reassess therapy if it is not working 56:22 - Why ketamine can function as a rapid antidepressant stopgap 58:01 - What's next: peer review, the MFA program, and more writing
On this episode of Vibe Check, Saeed and Zach dive into AI – the promises vs. the reality, and what the future might hold. You can find everything Vibe Check related at our official website, www.vibecheckpod.comWe want to hear from you! Email us at vibecheck@stitcher.com, and keep in touch with us on Instagram @vibecheck_pod.Get your Vibe Check merch at www.podswag.com/vibecheck.Subscribe to SiriusXM Podcasts+ to listen to new episodes of Vibe Check ad-free.Start a free trial now on Apple Podcasts or by visiting siriusxm.com/podcastsplus. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.
The following article of the Tech industry is: “AI Hallucinations: Causes, Cures, and Human-in-the-Loop Oversight” by Carlos Aguilar, Managing Director, Globant Mexico.
From 1-peso cactus pads in Mexico to multi-thousand-dollar FIFA World Cup tickets, host Marcos Lopez pulls back the curtain on how ancient barter, the birth of currency, and corporate storytelling trick your brain into accepting manufactured prices. #Value #Currency #Marketing---------- ☕ Support the Show:Toss a coffee into the virtual jar to keep our studio mic hot and research moving forward:
I interviewed Kenji Ishimaru about If You See a Cat on Tuesday, September 2, 2025 at Venice Immersive in Venice, Italy. Here is the story synopsis for If You See a Cat: "The boy's only friend was a hallucination of his beloved cat. Concerned for his mental health, his mother admitted him to a psychiatric hospital. After three months of medication-based treatment, the hallucinations fade, and he is discharged — but he has lost his emotional anchor and appears even more lifeless than before. Withdrawing into himself, he shuts his mind to the world and isolates in his room. One day, from his window, he sees a girl playing with a stray cat in the parking lot. This quiet moment becomes the catalyst for his recovering." Here are the contextual domains that are explored: Mental Health in Japan [6] & within various professional medical contexts with doctors [6]. Overall feelings of exile [12] Main scenes take place at home [4], within the context of a local Japense neighborhoods [3], as well as the hospital [6]. Episode triggered by the death [8] of his cat [6]. You watch the experience through the eyes of these cats [6], often low-level to the ground looking up. Here is the Elemental Center of Gravity: 1st Center of Gravity of Emotional Presence: Narrative-driven, immersive animation.2nd Center of Gravity of Earth Element / Environmental and Embodied Presence: Into these different worlds, and often from the perspective of the cat - to get some distance over the dehumanization depicted by the Japanese medical establishment.. Some spatial metaphors of illusions3rd Center of Gravity of Air Element / Mental and Social Presence: Dialogue amongst the characters - English and no subtitles4th Center of Gravity of Fire Element / Active Presence: No narrative agency or any embodied Interactions Archetypal Themes and Character Explored: The Fragility of Mental Health. The oppression from insitutionalization + transgression of boundaries of Human Rights & violations of dignity. Also withdrawling from society and feelings of exile and grieving death and then starting to see Hallucinations. Empathizing with the types of taboos against mental health within the context of Japan - TITLE: If you see a cat, trying to connect to mundane realities of pets we all have. Universal experiences of the death and loss of pets, but also how mental health issues may also ripple out and impact you or people close to you Artist Statement: "Fortunately, I have never experienced mental illness. In depicting the boy who is at the mercy of his surroundings — including being labelled as mentally ill — I portrayed him as a kind of outsider. But I was always aware that I myself could be in the same position tomorrow." https://www.youtube.com/watch?v=FkUwnPSzxHk This is a listener-supported podcast through the Voices of VR Patreon. Music: Fatality
Hallucinations are an eternal problem in generative AI in particular, and while it's true that large language models (LLMs) require vast amounts of data, the quality of that information will affect the quality of the output.What can businesses do to ensure they're using AI both effectively and responsibly?In this episode of the ITPro Podcast, Jane and Ross are joined by Amanda Stent, head of AI strategy and research in the office of the CTO at Bloomberg, to examine what responsible AI is, how organizations can use it, and what has been achieved at Bloomberg.Highlights"The (Bloomberg) terminal gives users access to more than 17,000 news providers, not just Bloomberg News, more than 1000 research brokers, more than 400 million documents from companies themselves, and billions and billions and billions of ticks – that's prices – every day for equities, bonds, commodities, derivatives, any kind of financial instrument you can think of. So, in that context, accuracy is paramount. If we hallucinate or do something that's otherwise incorrect, markets may move, and that might be bad.""We have guardrails that we run on every input to and output from a Gen AI system ... (which) are specific to financial services. For example, we don't want users to be injecting code into our systems. That's a generic guardrail, and we also are in the business of offering financial information, but not financial advice. So if you say. What's a buy case for IBM? We should give it to you. If you say, 'Should I buy IBM?' we should say, 'Nope, I can't answer that question because that's not something we're in the business of doing', and that's a finance-specific guardrail.""We have analysts using our AI systems to ... help them write their research reports more quickly, more easily to cover more companies, to understand the context of a company with its sector and its industry, to write on-demand reports for clients instead of a monthly or a quarterly report. We have portfolio managers doing the same thing, writing on-demand reports for clients using AI instead of quarterly reports. So these are some of the new ways in which people are using AI. But to me, traditionally it was about efficiency and signal generation. And today I think it's about effectiveness. So it's helping people become more effective in how they use AI."LinksAI hallucinations, accuracy still top concerns for UK tech leaders as adoption continuesThis new technique could improve AI output accuracy by 80% – and tackle hallucinations once and for allThe ITPro Podcast: Why doesn't more data produce better results?Bloomberg's Responsible AI Research: Mitigating Risky RAGs & GenAI in FinanceBloomberg Survey: How London's finance workforce is embracing AI on its own terms
The Jews “poisoned the wells.” Now the Jewish state apparently controls migrants. Every crisis still needs the same familiar scapegoat.
This week Kevin and Patrick go solo and take the time to deep dive into this what just happened this week! Want to see the Big Picture Trading's weekly COT Report? Click here: https://www.cotsignal.com Sign up for a FREE 14-day trial at Big Picture Trading: https://secure.bigpicturetrading.com/membership/signup/fOY4YJYX Subscribe To Patrick's YouTube Channel: https://www.youtube.com/@Patrick_Ceresna Visit our merch store!!! https://www.themarkethuddlemerch.com/
Former professional wakeboarder Brad Smeele was living an adrenaline fueled life, travelling the world and pushing the boundaries of his sport, until a devastating wakeboarding accident left him a C4 Quadriplegic.A raw and open conversation about the realities that often sit behind the inspirational headlines. Exploring grief, identity, masculinity, mental health, relationships, carers, accessibility, Disability Schemes and the difficult process of accepting a life you never expected to live.Brad also discusses his memoir, Owning It: The Ride That Changed My Life, the determination required to write his story and how he eventually began rebuilding a sense of happiness, purpose and control.This is not simply a story about “overcoming” disability. It is a genuine conversation about confronting your darkest moments, adapting without losing yourself and taking ownership of the life that remains in front of you.Whether you live with disability, support someone who does, or are navigating your own major life change, Brad's perspective will challenge how you think about resilience, acceptance and what it means to truly own your life.Want to become a Keep Rolling Patron and help further support the channel, hit the Patreon link below and Roll with the Squad! https://www.patreon.com/street_rolling_cheetah Add, Follow or Contact Brad SmeeleInstagram: https://www.instagram.com/bradsmeele/?hl=en Add, Follow or Contact me: Email: streetrollingcheetah@gmail.com Instagram: https://www.instagram.com/street_rolling_cheetah/?hl=en X-Twitter: https://twitter.com/st_rollcheetah Face book: https://www.facebook.com/StreetRollingCheetah/ LinkedIn: https://www.linkedin.com/in/jake-briggs-77b867100/ Timestamps(00:00:00) Intro(00:01:47) Sponsors: Culture Connex and Permobil(00:03:55) Welcome Brad Smeele(00:05:20) Home on an Auckland Lifestyle Block(00:07:46) Writing A Book To Reflect on Life(00:09:42) Wakeboarding or Hockey?(00:15:50) Winter Training(00:18:07) Winning World Champs and NBDs(00:28:56) The Trick That Changed Everything(00:33:00) The Crash and Chaos(00:37:03) Orlando ICU, Hallucinations and Ventilator(00:41:26) Flatlining in Front of Mum(00:48:04) From Suing Sponsors to Credit Card Loophole(00:54:15) A C4 Injury and Impact on Life(01:09:46) Online Trolls, Dark Humor and the X8 Chair(01:15:51) Jet the Dog and Back in the Water(01:23:43) Travel Costs and Why Good Caregivers Matter(01:28:18) Losing Control then Educating People(01:35:16) Dating and Relationships Post Injury(01:41:24) Three Steps and Writing a Book(01:49:47) Vulnerability(01:53:15) Neuralink, Cures and Quack Injections(02:03:09) Financial Responsibility and Business(02:05:36) Where to Find Brad, and Outro
SummaryJoin us as we analyze the horror sequel Smile 2, exploring its themes, scares, and behind-the-scenes trivia. We discuss the film's portrayal of fame, hallucinations, and the impact of Naomi Scott's performance, along with fan theories and future predictions for the franchise.Sound Bites"The scale was just bigger in Smile 2""Ray Nicholson's smile is a homage to Jack Nicholson""Naomi Scott did all her songs and choreography"Chapters00:00 Introduction to Smile 2 and Horror Themes02:00 Discussion of the franchise's return and October plans04:29 Fame allegory and the horror of celebrity culture07:03 Hallucinations and the unreliable narrator in the film08:50 Ray Nicholson's smile and homage to Jack Nicholson14:43 Water drinking scene and Naomi Scott's musical talent22:18 Character names and psychiatric references23:13 The curse transmission to fans and franchise lore26:41 Ranking the kills and their impact28:44 Final thoughts and next movie preview
Competing in a Future World of Infinite Intelligence Navigation: Intro From Knowledge Workers to Judgment Workers The AI-Native Company: Org, Hiring, Culture The Human Element: Are We Underestimating It? Scenarios Our Take Conclusion Our co-hosts: Bertrand Schmitt, Entrepreneur in Residence at Red River West, co-founder of App Annie / Data.ai, business angel, advisor to startups and VC funds, @bschmitt Nuno Goncalves Pedro, Investor, Managing Partner, Founder at Chamaeleon, @ngpedro Our show: Tech DECIPHERED brings you the Entrepreneur and Investor views on Big Tech, VC and Start-up news, opinion pieces and research. We decipher their meaning, and add inside knowledge and context. Being nerds, we also discuss the latest gadgets and pop culture news Subscribe To Our Podcast Nuno Gonçalves Pedro Introduction Welcome to episode 79 of Tech DECIPHERED. Today, we take a leap into the big unknown. This is a thesis episode, not your classic analysis, in-depth sharing episode. The big idea for this episode is that we may be approaching the cognitive age, and how would one, or how would a company compete in a world of infinite intelligence? The big idea, again, is that intelligence, which has been mostly scarce and expensive for all of human history, might become abundant and cheap. If that happens, what happens to work, what happens to companies, what happens to society? This episode will be really framing a lot of these discussions. From knowledge workers to judgment workers, addressing the AI native company and how does that change, going into the human element and whether or not we’re underestimating it, and finally, ending up going into scenarios, feasible scenarios of a future where, well, intelligence is abundant. Intelligence is quasi-infinite or infinite itself.Bertrand Schmitt Yes. Big questions for this episode 79. From Knowledge Workers to Judgment Workers We can start with from knowledge workers to judgment workers. Let’s go back first to how came the knowledge worker. It’s a 20th-century invention from Peter Drucker in 1959. The idea here is that that category might be splitting. The production of knowledge itself is on its way to being commoditized by AI. However, our perspective is that judgment around production of knowledge is not disappearing and is staying for a bit control managed by humans. What’s your take on this, Nuno? Do you agree with this split?Nuno Gonçalves Pedro I think it’s a little bit more profound than that. It’s not just judgment. Definitely, human judgment will be needed. We’ve seen agents perform all sorts of funny things in the wrong way when left alone to their own devices. Even some very well-known AI researchers coming forward and saying, “Hey, I tried to use this myself, and actually I messed up some of my systems,” or “I messed some of my code. I messed up some of my flows for a period of time.” I think just having human-in-the-loop from a judgment standpoint will be needed for a significant amount of time. That is something you can’t just delegate into machines, into algorithms, et cetera. The second part is, ultimately, there needs to be contextualization, and that contextualization, I think, comes from two forms. One from actual data, where the machine, I think, at some point will catch up, or the machines will catch up. The algorithms, at some point, on the data analysis will get better and better and have probably the closest to the truth that you can get, minus all the biases that are in the data, just to be clear, because data has a ton of biases. We’ve looked at this in the past and discussed it at prior episodes. But maybe on that, I think the machine has a chance to catch up, or the machines have a chance to catch up, so there’s less of distinctiveness from the human standpoint. But then, on just the attributes, the ability when you’re judging some situation, you’re in the middle of the situation. You’re judging the person and how it’s acting, in some ways, a lot of the things that end up happening, end up happening because there’s human interaction. There’s someone on the other side. I see how they’re delivering the message, how they’re implicating. We’ll talk about it later in the context of the organization and what changes in companies. I don’t think it’s just judgment. I think there’s a little bit more than that. One of the reasons I went to the dark side of management early on in my career from being an engineer was Peter Drucker and this notion of the knowledge worker, which he later on reemphasized with the publishing of his book, which for me was seminal and defined a lot of my career in life, the post-capitalist society, which is this notion that information rich and information poor is going to be the key distinctiveness that will happen in the world. The two big camps, information rich, information poor, which links back to this invention of the term knowledge worker, that knowledge is going to be key in some ways. I think that’s what we’ve seen for the last decades. Again, I think judgment is not going anywhere, but I think it’s beyond judgment. There’s elements of humanity and involvement that won’t go away anytime soon, where human-in-the-loop are particularly critical. We’ll discuss later some scenarios, but for me, that’s my stick in the ground. I think human-in-the-loop is going to be critical for many decades to come.Bertrand Schmitt While we are talking about all of this, and we share some possible scenarios, there is always that question. This is moving so fast right now. If you think about AI 10 years ago, AI 5 years ago, AI with the launch of ChatGPT 3, and then AI the past 2 years, now we have agents that are running at scale. Things are moving very fast. I can tell you, me in 6 months, the change has been pretty dramatic in terms of what I can use AI for. There is always that question that whatever we are thinking about cannot just be connected to what we were able to do 6 months ago or even today, we have to think and project ourselves at least in the next 6–12 months. Of course, we can go beyond that, and we will do that with some future scenarios, but it’s a very fast-moving, and it’s not clear yet where are the limits.Nuno Gonçalves Pedro I think that’s a very fair point. Let me try to analyze things that I don’t think will change anytime soon for the next few years. Agreed with you that many things will change, and we’ll have a lot better tools, platforms out there. That will be difficult to predict what exactly won’t change. I think there’s elements of humanity, and some of them do relate to judgment, like having good or bad taste, having a view on it, on whether something looks good or bad. Obviously, all of this sometimes is subjective, but some of it may not be as subjective as people think it is. The elements of contextualization. I think a little bit going back to what we did at Chamaeleon ourselves, where we built this platform, Mantis, and the objective of building Mantis was not really to replace us, was that it was a core augmentation layer in some ways that we would use investment or investor judgment as humans in the loop to systematize pattern recognition and a variety of other things, but that Mantis would really elevate all that judgment, not just in terms of timing, us being more productive, but also in terms of the quality of the decisions we’re making. Think of it as a little bit like having our human judgment in the context of operating Chamaeleon at a higher altitude, where we are more aware of the things that are happening and how they actually happen. The ability to really get to the data pieces and then make decisions on top of that that generate the needed alpha in our case for investors. What I mean by this is I think there’s always going to be core elements of humanity that I do think are going to be difficult for the machines to replace. For example, the taste piece people are like, “I can figure out what’s the taste in the market.” Yeah, but that’s mainstream. That doesn’t identify what’s the next big thing, which normally doesn’t start from mainstream. It starts from something else. It could start from opinion leaders and influencers. It could start by someone having a different way of addressing a problem and having a solution that hasn’t been thought through. For example, elements of creativity, I think, in human judgment and in human operations is something that I feel the machine will still have difficulty to replace.Bertrand Schmitt Let’s not forget how today current algorithms are working by feeding them enormous quantity of data, actually as much data as we can find. Finding more data is becoming a limitation these days. What it means is that it’s very hard for AI to think beyond its training data. There is some level of logic that’s being added, but at the same time, take the launch of the iPhone. What was the opinion before launch? Is that no, it doesn’t make sense. Not enough battery life, no keyboard, no this, no that. If you just base your analysis on what’s written out there, what’s being sold out there, you would just say, “It’s going to fail.” AI might really follow that more generic advice and perspective because that’s what in the training data and that’s what they’re in volume. It’s, of course, raising a lot of questions of, how do you improve the quality of the training data? How do you separate the weed from the chaff? There are a lot of questions there, and obviously, it will get better over time. But it’s still a critical part of how it’s working today. It won’t be that easy to change. I really like your point regarding Mantis, and I will say in general, platforms that you build with AI or leveraging AI capacity. Because when we say knowledge production is going to disappear, but we’ll keep judgment, it will be a different type of judgment because the quantity and quality of knowledge we will have in front of us to build our judgment will be very different. If suddenly we have for free the work of 10 interns or 5 junior analysts or whatever, and you can run that on nearly anything you do in life or at work, it’s completely dramatic. Your judgment was not used to be exercised so often because often you were missing quality data to have a judgment. Before it was a lot of finger in the wind and trying to smell something, but you didn’t have enough to make a serious analysis. Except if you are working as a strategy consultant, as you used to do, Nuno. That part is actually quite interesting. That the judgment itself will be exercised much more often and hopefully on the base of much more in-depth analysis for a lot of things. We will work very differently.Nuno Gonçalves Pedro We will go in-depth, faster and more fact-based, more data-based along the way. The question some of you might have right now is, is there some judgment that’s going to go away? Is there some judgment? We seem to be defining that there’s this organization, we’ll talk about it later, that goes from doers more into deciders. I think there’s some nuances to that, so I’ll just hit pause on that. In terms of judgment, obviously, there’s judgment that has been hidden over the years under the pretense of being wisdom, but it’s actually not wisdom. It’s just repetitive tasking, and it’s rules-based for the most. There’s a lot of judgment done, in particular in the white-collar space, that you could say it’s just reps. People have been doing it all along like that, and so therefore to say, “I’ve done it before like this, so I’ll do it the same way.” There’s actually no best in class, no analysis, no nothing. It’s just, “I’ve done it like that before.” I think that type of judgment will disappear because, again, algorithms will be as good, if not much better at that. They’ll be better at figuring out, actually, this would be the better way to do this. That’s how you play it forward. Then the question is, if there are fundamental, wise people in the organization, people that can really take that more complex elements of judgment, how do you go from the world we have today, which is a world of apprenticeship, where people come out of college, they go and work, and they learn their way, and therefore, hopefully over time, some of them, not all of them, we know that, but some of them will develop that wisdom to be great decision makers 15, 20 years down the road? How do we do that in a world that now is saying, “I don’t need people out of college because I can do it myself, and I can do individual contributor, and I can have agents doing the work that would require some manifestation of management in the middle.” Basically, “I don’t need this stuff. I don’t need you.” It’s a little bit the story we’re in. How do you create then this apprenticeship? How do we create then wisdom? My two cents on that is that wisdom, because of what we were just discussing and what, for example, myself and Bertrand was just saying, because of more often interactions with more data-stressed information and insights, what will happen is people will get better through their own reps in whatever form they’re doing, in day-to-day life, in internships, et cetera. In some ways, that will create the accelerated growth. It’s a little bit the interactions with agents and the interactions with our beloved AI algorithms that will create that growth over time and maybe not as much with other people. That still leaves the question around social interactions, but that’s probably the way this gets sorted. Apprenticeship gets sorted through the machine and the human having more interactions in effect.Bertrand Schmitt I agree with you because when we talk about apprenticeship, in some ways a lot of time was wasted on stuff that were not that important. But in a way, that was the price you had to pay in order to be there when people make the big decision to try to get some wisdom from that one hour of interactions that’s really useful and make a difference out of your full week. But the rest of your full week was just basic stuff that you had to do like a machine in a way. Why not let a machine do that? That, for me, is a big question. You could argue there is a transition period where it could be hard. For instance, if you can work hand in hand with AI smartly while you are doing your 4, 5 years of universities, you could graduate with a very different knowledge, perspective, judgment, skill set than anyone who graduated 5 years ago. I think that part will require a question around, “How do you change education?” You see what I mean? If you keep education the same way, expecting that the output is someone that should go now into 5 years of apprenticeship, that’s not going to work because companies will be, “No apprenticeship anymore.” On the contrary, you have to come much more knowledgeable and ready to use the tools. The tools are so efficient that the bar pretty high. You need to come already very well-grounded. If the education is not doing their job, that will be trouble. That part for me, I think is often forgotten. In some ways, the new-found importance of universities as a place to, and not just universities, the trade to really deliver people who are ready for the workforce. If on the business side, the expectation can change, of course, you have to change the education on the other side. My worry probably right now is that it doesn’t look like universities are in touch with what businesses are looking for, businesses are working on. Of course, that’s very worrisome because the cost of university has increased very significantly. It’s not clear quality of education has improved at all. If anything, it could be the opposite. It’s pretty scary. Of course, it’s going to raise a lot of questions. How much is education worth in that type of situation? Maybe another point because we talk a lot about apprenticeship, how this stuff was useful, but at the same time, if we go back in time, not long ago in the ’50s, if you wanted to be a developer, for instance, ’50s, ’60s, the job was very different. There was barely any programmation language out there. You had to use punch cards. Your time truly spent doing the coding was very limited. Once you had your stuff working, then, the debugging was a total nightmare. My point is that no one is looking back to that time saying, “You know what? It was great. It was a great way to learn and to do an apprenticeship for 5 years. To do that crappy job of punching cards for the boss.” There was little value in this. Guess what? Everyone is happy it’s not being done anymore by anyone. I think we also have to see what AI is bringing in a similar way is that everyone’s job is going to become quite different. There are a lot of big parts of the job who are not going to look back with fondness. Just looking back as, “Wow, that was very machine-like type of job. I’m glad I’m done with it.” People will want to jump directly to the next step. You don’t need to go to the punch card phase to be able to be a good developer for the past 40 years. I guess it will be the same with AI.Nuno Gonçalves Pedro I think so. The difficulty we have as humans is to also visualize dramatically different scenarios and landscapes, professionally. It’s difficult for us to anticipate what are the jobs of the future. Jobs have changed a lot in the last few decades, not even the last century. What people do, the migration initially from the agricultural society to then the industrial society to then the services society, and in some ways, the shift within the services industry, and now we’re seeing another shift, so we can’t really anticipate what those jobs look like. Back to your point on education, because I think that’s a very important point. If you’re right now an undergraduate student or a postgraduate student, for that matter, and you’re not figuring out your own mechanisms of learning outside of your syllabus, outside of what your professors are telling you, et cetera, you’re going to face very difficult times. If you’re not right now using all these AI tools proficiently, all these cycles of vibe coding, co-working, et cetera, with agents in the mix, you’re going to have a really tough time. If you’re not at this point in time as proficient as someone like myself or Bertrand, and given that we’re nerds, we’re relatively proficient with a lot of these tools that are out there. On top of it, some of us have our own platforms in-house. If you’re not as proficient as we are with those tools, you’re going to have a very difficult time because then people like us won’t need you. I think that’s the sad truth. It’s like at some point, if you’re not needed, you’re not needed. Then again, you may find something else that’s more interesting for you to do. Start your own company, go join a new exciting job doing whatever it is that you need to do next, et cetera. But again, I think the bar is very high. If you’re in college right now, again, undergrad, postgraduate, this is the time of transition. This is the worst time. It’s not the best time, it’s the worst time. Because education and all these institutions haven’t adapted to it yet. You need to adapt. You need to adapt. You need to adapt. If you don’t, you’re going to pay for it, not just in the loans you need to repay, but also in terms of actually having difficulty finding your career path in those first few critical years.Bertrand Schmitt You need to be especially proactive when you’re facing this type of period where businesses are adapting as fast as they can because they all know it’s going to be survival of the fittest very quickly. Universities typically are working on a very different pace, and it’s pretty guaranteed they are not going to have adapted as fast as businesses. In time of big dramatic change, it will be trouble. It will be trouble. Yes, you will have not fun. Not saying it was part of the deal when you sign up for that loan and decided to go for university. But that’s life. There has been issues before. It’s not the first time. You have to do something about it. You talk about your perspective about, “Hey, why do we need you if you are not already fluent and very efficient with these tools and stuff?” The truth, in some ways, it’s even worse than that. Each time we spend with someone who is not efficient with all of this is less time we spend with the tools that are already providing magic for us.Nuno Gonçalves Pedro Exactly.Bertrand Schmitt It’s a very big choice of, “Hey, do I spend more time training this person?” Do I just… there is an opportunity cost. Or, do I spend more time staying at light speed? Why do I slow down to do something else in the hope that maybe I will get to return versus the light speed I’m already on? It’s a lot of tension. Again, it’s certainly new. But if we want to look back, I think you talk about the switch from agriculture and society, industrial society, and now the service industry. The reality is that, yes, we have made dramatic changes in the past before. 140 years ago, we were 90% agricultural society in Europe, in the US, 90% of us. Today, it’s what? 2%. So my point is that that’s a normal evolution. There is no progress without change. Sometimes the rate of change is soft, and sometimes you have a step function. Now it’s a step function, and it’s also a pretty fast step function. Before, it could take decades to get new stuff being put in place, to have electricity come up, this or that. Now we see that the rate of investment in AI is insane, way beyond anything we have seen before. Two, in a way, a lot of the architecture behind the scene was already there to support an even faster transition. What’s new might be the pace of the transition, how unnatural it might look. But at the same time, if you put yourself in the shoes of someone who lived 150 years ago, I mean, this was also a dramatic change for them. From horses to cars to planes to rockets, pretty big change, maybe even bigger change.Nuno Gonçalves Pedro Maybe the silver lining, just to bookend this section, is one, there will be new roles. There are a lot of things we can’t anticipate. There will be new roles, there will be new jobs being created, and new things that we can’t really quite grasp yet. The second part is that the rules are changing, and they’re changing, I would say, in general, for the better. If you are a decision-maker or an organization, and you still have your job, you’re probably making more important decisions with more data, with more tooling around you, with less red tape, hopefully over time. I know that will not hold true for all the big corporations out there that are listening to us, but it is starting to happen. Things are making an impact on how decision-making is made. There’s less and less red tape along the way in certain organizations. There are more and more fact-based discussions happening as we move along. The silver lining is better jobs, more jobs, different jobs in the future, hopefully as well. Secondly, the second part of the silver line is that the jobs that exist today, hopefully, will be more interesting, certainly on the knowledge space and on this judgment space that we’re now introducing as part of this episode. The AI-Native Company: Org, Hiring, Culture Switching gears, maybe to how does that shift? How does the company of the future look like? How does an AI native company look like? I feel there are a lot of discussions on, “Oh, you only need one person to run everything.” Let’s not go to that level. We’ve had a couple of episodes where we focused on AI as your co-founder and a couple of other elements that you guys can go back to. Let’s focus on a more evolutionary view of what’s happening to organizations, and maybe start with the org structure. In general, we should see more flat organizations where mid-level managers have to justify their pay in some ways because middle management are routers. They are normally routing tasks. It’s sometimes aggregating it, synthesizing it, and pulling it back up. Guess what? AI and agents in general are very good at that. The synthesis piece, et cetera, is not as well needed. One could say there are several elements of middle management that are valuable, like the coaching of people, the creation of apprentices, and the accountability that comes with some of middle management. But lo and behold, most of middle management is seen as a little bit of a thin line that doesn’t need to necessarily exist. I feel we’re moving into a world of smaller teams, more senior teams, where there’s more judgment at the top, where you’ll have people that both do a mix of what we used to call management in its new form, but also a lot of individual contribution. If you’re not used to that, if you’re not used anymore to be an individual in the future, again, and if you’re a very senior in an organization, maybe this is the right time to either reinvent yourself, find some other job that doesn’t require as much of that, which we’ll have plenty of those jobs for the next few decades, or maybe retire. I’ve actually, shockingly enough, seen people who have said, “You know what? This thing is changing too fast, too dramatically. My industry is changing quite aggressively right now. I’m about to retire in a couple of years. I’m just going to retire now.” I’ve literally met two people who have done that. Again, there’s nothing wrong about it. I think we’re, again, going through a step function and a huge shift, but figuring out where you fit in this new model of organizations, more senior at the top, smaller teams, more of a mix of individual contribution with management than ever was done before.Bertrand Schmitt I agree with you. In some ways, I’m not surprised that some people might say, “You know what? It’s now time to retire.” I feel a bit sad, maybe because it means you don’t like to keep reinventing yourself and changing your habits and thinking about new stuff. You were a creature of habits, I would say, if that’s your conclusion. But everyone is entitled to their own opinion, obviously, and a way of life. I guess that’s what happened, again, at regular times in the past in terms of big change. What I can see is that the rise of, you can call it the full-stack individual, someone who will have multiple roles inside the team. Before, you had to really separate the role. Especially in the US, there is such a clear separation between every role you can have in a company. Let’s take a tech company. You will have people doing design, people doing different types of designs, people doing front-end development, back-end development, and operations. You see step-by-step hyper-specialization. I have seen that, and it’s true that the level of complexity you had to deal with at some point requires some level of hyper-specialization because it will take you 6, 12 months in order to be really, really strong on a specific topic, a specific language. God forbid, trying to go deep into something that you had no real experience into. But I feel with AI, it’s a big change, actually. It’s the opportunity to go beyond that. It’s the opportunity to do more, to touch more. You can combine designing and shipping code, product managing and shipping code, being an analyst and deploying. Of course, we have to think how it works because putting a marketer shipping code to production, maybe that will get you into trouble. But I think that there must be some change. We see it changing dramatically, how fast we can get into something, something different from what we are used to. I think it would be crazy not to take that opportunity to dramatically change the scope of many positions and put an end to that hyper-specialization. I think for me, in some ways, hyper-specialization was bad. There is only so much you want to be a specialist in because a lot of things, a lot of opportunities are actually coming from the mixing of many different ideas, many different perspectives, and you lose if you go to hyper-specialization.Nuno Gonçalves Pedro I don’t think the age that is coming is the age of the generalist. I think it’s going to be the age of the multispecialist. We’re going to go into an age of multispecialization, which is a little bit, we’ve mentioned it as well in the past, what Amazon defines as an athlete or T-shaped or pie-shaped people, people that have on top an amazing ability to do general management, strategy, managing teams, et cetera, then have spikes. Spikes into business development, corporate development, product management, whatever it is. With AI and with agents, the development of those spikes, as we’ve been discussing in this episode, will actually be easier. It’s almost like a given. If you want to go deeper and deeper into a certain area, you can go much faster. I think that level of multispecialization is going to be really cool to observe. I’m not sure we’ve had an age of multispecialization over the years. Maybe people would point out, well, the Da Vinci example, people that are great across very different areas. Maybe that’s an example of multispecialization. But honestly, from my perspective, this is going to be an exciting time because of that, because you’ll have people who, instead of being just focused on this area of sales, and I only do that, they can actually and should actually do a lot of other things. So the work, as we were talking before, can be more interesting. More demanding as well, because the judgments you need to make are more complex. The context you need to actually gain needs to be gained much faster. At a level of magnitude, you haven’t been able to do it before. Talk about information overload. But actually, ultimately, the roles can be a lot more interesting, a lot more exciting, because I can jump around. If I’m an investor, in this case, we have two investors on this conversation. But if I’m an investor, one of the things that we start looking at is actually not just looking at a startup as, is this startup doing something in AI or not? Is it AI-enabled or not? Is it an AI platform or not? But actually, more fundamentally, is this an AI native startup? Meaning, organizationally, culturally, is this the company that’s already in the AI age? How is the team working? How are they defining things? It’s not just that they only have two or three people. It’s like, what are those two or three people doing? How are they doing it? What cadence are they doing it on? What tools are they using? How are they making decisions? I feel we’re still actually relatively early on that track. It’s very interesting because we’ve had all these companies raising mega rounds. First round out, we invested in one of them, but there have been many frontier labs out there raising a ton of money. But a lot of them don’t have a fundamentally different way of doing business. Of organizing themselves, of how they do the day-to-day. Although they’re working on cutting-edge stuff, with very notable exceptions, they’re actually not using it themselves. They’re not actually shifting how they do stuff themselves.Bertrand Schmitt For me, that’s very interesting because in the past, I used to be quite conservative on how you manage and run a company in the sense that if you’re already in tech, if you are already on the cutting edge of what technology can deliver, and this and that, don’t waste time trying to invent a new org structure. Just focus on delivering something great, amazing, and be great at technologies. That’s already your huge differentiator. At the time, there was no real reason to innovate on the team organization. I have seen so many teams that tried to innovate, and it was just catastrophic because there was not much to innovate on, because we had decades of optimization that we could leverage. There was no reason to invent. But here it’s very different. There is a dramatic shift in how you can organize differently a company. I don’t think there are any blueprints yet on what’s the best way to do it because it’s too new. But at the same time, I would feel very bad to invest or support a company that first is not focused on AI or AI-enabled, but at the same time is not trying to innovate on the team itself. Because if you don’t do that, you’re going to get killed by someone who is going to innovate better than you on not just the product, but on the org as well.Nuno Gonçalves Pedro Indeed. The shifts are pretty substantial. If you look, for example, just at hiring, what do you hire for? Certainly, there’s this element of the multispecialized orchestrator, which normally will be someone with quite a lot of wisdom and expertise. It doesn’t necessarily mean someone who’s old, but someone who has the ability to work with all the AI tooling and platforms out there and be an orchestrator of agents. Why do they make judgments, make decisions, move stuff forward really, really, really quickly? Again, those jobs are going to be the best jobs. The second part, I think that is very interesting, around hiring, is you’re going to skew towards the elements that are potentially either very aligned with the use of AI tooling and platform, AI expertise, or being AI native, or someone who’s used to using AI. That’s one side of the fence. On the other side, you’re going to actually be optimizing to hire people that have the characteristics that will be difficult for AI to replace immediately, like taste and the notion of fundamental accountability and notion of implications, the notion of how you affect change in organizations, how you affect change in individuals, the elements of coaching, and beyond coaching. You’ll be optimizing for those kinds of hires as well. Then, last but not least, for me, I feel that there is a momentum already happening. I think it will happen even more, which is the tendency to under-hire rather than over-hire. The moment of the good old days of blitz scaling, “Oh, let me go and hire 300 people to scale my go-to-market and just land grab market.” Now, that’s not how it’s going to work. People are going to try and first get the efficiencies in-house with top talent and see if there’s, at the end, the need to hire more people or not, rather than the other way around. I think the issue here is a little bit of what we alluded to before in this episode. There is a tax on individuals. If you hire more people, you’ll have to manage people, you’ll have to work with them, et cetera. If I don’t need to, I might as well work with the agents that the tools and platforms that I use give me access to. Because that’s a world that’s much more efficient, right?Bertrand Schmitt I’m in total agreement with you on this. It’s definitely raising way more questions than before because, again, on one side, you have the product, the technology used to build products that are completely different. At the same time, all of this is also enabling new ways to design organizations and to scale differently, especially in a world where, as we have seen in 3, 6, and 12 months, stuff that you thought were impossible are suddenly becoming possible. So you’re, “Hey, I’m going to scale and burn a shitload of money for 6 months before I know if there is any return.” Versus, “You know what? Maybe I just wait 6 months. The AI has improved enough so that we don’t need this new team. We don’t need these people to do stuff.” Because actually, if you just wait 6 months, we will have stuff coming for free from either new AI models or new AI tools or this or that. If you remember, we used to say that in mobile, things were going three times as fast as on the web in terms of pace of innovation and speed of development and stuff. I mean, with AI, it’s 5X mobile.Nuno Gonçalves Pedro Maybe even more. Yes, well.Bertrand Schmitt Maybe even more, maybe 10X. Every assumption around blitz scaling or scaling in general was based on past assumptions. It’s not based on how is the industry evolving today. Might make more sense for you to really grow your agents and spend more money on more tokens. I remember, of course, Jensen is selling his business interest, but he was saying, “Hey, for each one of my 450K engineers, he better spend 250K in tokens a year.” I’m not saying it’s the right way to say it, but I think there is some truth in it, and that would be something to think about. Have we maxed out the token usage per employee? I’m not talking in a stupid way because token maxing and wasting money has no value and is as stupid as it gets. But if you are truly getting a return on these tokens, can you use more? Can you generate more? Can you create more loops so that one engineer manages not just 10 agents, but 50 agents, but 200 agents? I think that’s the big question. We’re trying to add more people. More people means more management, more issues, more this, more that. That would be a fair question. Another piece of the puzzle is how do you build in a way your… I don’t know if it’s a digital twin, but more like the digital version of your companies represented by agents. How do you make sure that everything you do as a business is truly captured, is truly leveraged so that your agents are getting better and better? Not just because the model gets better, but because you are putting more data into it, because it has more opportunity to learn, and as a result, gets better at your specific business.Nuno Gonçalves Pedro The next big thing is culture. How does culture change? I think the biggest shift that I see is, why would you do meetings all the time?Bertrand Schmitt Yes.Nuno Gonçalves Pedro At least at Chamaeleon, we have a very small team, just by the way. We have a very small team at Chamaeleon. We’ve reduced by way more than 50% the time we spend on meetings between each other across the board, one-on-ones, partner meetings, et cetera. I think we’re really pushing to be more and more asynchronous. There’s stuff you can process via message. I was just asking one of my colleagues, “Can you just send me that prompt for that so I can just do that on CoWork?” Or “Can I just go on Mantis and do this? Can you tell me the cycle?” Or vice versa. Basically, it’s a little bit like you’re just going to do it. I don’t need to meet. I don’t need to meet all the time. There are some things where we still need to meet and interact, and we need to brainstorm at times, and we need to go to a different level of abstraction on the top end. Then on the lower end, there might be things that are a little bit more specific and governance-related and operational-related that we need to agree on that are more sticky. But otherwise, the culture is going to be biased towards build. “Go and do it,” rather than, “Let’s do a meeting.”Bertrand Schmitt Yes.Nuno Gonçalves Pedro Async is the thing. I’m more and more like we have a couple of interns this summer. “Can we async this?” They’re like, “What does that mean?” “Can we make this interaction asynchronous?” Because synchronous interactions for me are very expensive. Can you send me something that I can process, and then I can send it back to you? We don’t waste time on you giving me context and whatever. Then I’m not ready quite yet because I need to process it. Maybe I’m in between two meetings that I’m actually thinking about other things in my mind.” Again, I feel that shifts how stuff is done. One, build rather than meeting. Two, asynchronous versus synchronous. In some way, millennials had it right when they shifted a lot to messaging and stuff like that. Let’s do more asynchronous rather than synchronous, those two elements from just an operating model of the company are significant. Maybe this is a good time for me just to put one parenthesis because there’s this thing that’s bugging me as we’re talking here. Everyone who is listening to us at this point in time might be saying, “Cool, but I work for this large organization. We’re just now…” Everything we’re saying here is contextualized by time. We’re giving you extreme situations. We’re looking into the future. Some companies that we’re talking about might be doing this already as we speak. Some of them might be in the process of doing this and might in the next couple of months be doing it like we are describing it here. Some of them might take years to get there. Then again, some of the companies that might take years might actually be destroyed in between or meanwhile, and be disrupted. Some of them might not because they’re in very legacy businesses, and it’s fine, and it’s okay. Again, don’t take everything that Bertrand and I are saying today as this is gospel, and it’s going to happen tomorrow, and why the hell are we not doing it? We think that aspirationally, this is where you should be moving to as an organization, whatever size you’re at. Speed will matter, as we discussed before, but not everyone, obviously, is going to move as fast as we’re describing it here.Bertrand Schmitt Yes. Me, for instance, take inspiration often with what some of the AI labs, frontier AI labs, are doing, the way they are working, especially in OpenAI and Anthropic. They are clearly at the top of the spear in terms of what is it that you can do because they have access to models we don’t have access to, because they have unlimited tokens they can use for tasks. They hire people who are, of course, 100% on AI. They are the best example of what is achievable if you have the top minds, if you have the latest models, if you have unlimited tokens. From there, you can take that for our needs and for our situation, and others in industries that are not as advanced. Definitely, you have some time. But as you say, things are moving fast, things are changing. Wall Street is going to expect better returns because when we discuss all of this, the conclusion is that you should be able to do more with less. That’s as real as it gets at some point. By the way, that’s what you see. You see better performance, a better business performance right now. So even if you might not get disrupted, you’d better start there. For some, it might take more time, and they might still be fine.Nuno Gonçalves Pedro Maybe to bookend this section, clearly what we’re saying is organizations are going to change. Their MOs are going to change, the structures are going to change. There are elements of what we discussed before in terms of judgment that are fundamental to this. The ability that in some ways, one would say a lot of the technique of getting solutions out there, even in brainstorming or problem-solving, is going to get democratized. The algorithms are able to do that. On the other hand, having points of view and having wisdom is not necessarily democratized, necessarily by the machines. It can be facilitated, it can be more productive in achieving that level of wisdom, but wisdom still will matter at the end of the day. We’re not saying that’s out of the question. Actually, that’s going to be the asset. People who have fundamental wisdom that can come to the table and frame things. We see this even today in prompt engineering, on just creating prompts. The better your prompt is, the better the outcome is going to be, the result that you get from the algorithms. That’s not going to change, in my opinion, anytime soon. That UI interaction piece is not going to change anytime soon. Again, if you’re an organization thinking through organizational structure, culture, if you’re thinking through hiring, these are some of the elements that we think will give you an opportunity, but I would actually go one step further. On the positive side, I would say, they give you arbitrage. If you’re able to move faster than your competitors and really adapt your org faster, you’ll reap the benefits faster as well. That’s what many still say and relate to as the word innovation. That’s how innovation gets accelerated. I think there’s a huge opportunity right now for arbitrage. If you move fast, experiment, experiment on new org structures, experiment with talent, you’ll know that some of them will work well, some of them will fail miserably, so you can’t experiment on literally everything. On the other side, I think the doomsday scenario is if you don’t, if you’re on the other side and your competitor is outpacing you on trying these different organizational models, structure, hiring models, and operating models, they’ll potentially just disrupt you. They’ll do stuff that you thought you had the moat on, and lo and behold, you don’t anymore. Sometimes it comes just from org, just from injection of people with a different MRO, different operating model.Bertrand Schmitt The Human Element: Are We Underestimating It? Maybe we can move to our next section about the human elements. Are we underestimating it or are we overestimating it? The three things that are a big part of the human elements, emotion, creativity, and synthesis. Is it just soft skills, replaceable part? On the contrary, is it the durable part now that we have automated intelligence?Nuno Gonçalves Pedro I’ll start with emotion first because I think it’s probably the easiest of all the ones you’ve mentioned. Emotion is key. Many of you listening to us will know this. The way you deliver a certain message, the emotion that you have when you deliver it, just in and of itself, this could be a sentence, it’s something verbal, et cetera. Makes a difference between the person or the people on the other side actually adopting it or actually just resisting it. Emotion is critical. It’s what runs the world. Everyone talks about a bunch of things, but emotion is a currency that is still naturally human. It will be, I feel, difficult for these AI tools and platforms to recreate it fully until there’s some literally very high-definition manifestation of them as avatars or some physical manifestation of them as robots and all that stuff. It will take a while for that emotion to be manifested. Emotion, I think, is still something that we as humans have as a moat, and it’s critical. As you mentioned before, I was a strategy management consultant at McKinsey, and getting people to action is actually 80% about the delivery, communication, the emotion that you surround the project itself, more than sometimes the truth. It’s great to have the truth and to have something that is similar to the truth in terms of analysis, but in some ways, that’s not what really moves change. Change is moved by, I would argue, a significant amount of emotion and alignment on emotions.Bertrand Schmitt You could argue that’s something that most politicians have perfectly understood. If you look at most campaigns these days, everything on emotions, maybe the tagline might be one word. It’s interesting when you see from that perspective that actually it’s very little on facts, very little on all of this, but more about emotion. You could argue it’s the same for businesses in the future? That’s a fair question. I think creativity is another one that’s quite important. At the same time, it’s not so easy because I must say I’m quite amazed when I’m looking for creativity from AI, either to generate the image, to generate video, to generate audio, or to generate text. AI can be pretty creative. I still think you need to control its creativity; you need to understand what’s good, what’s bad, what’s quality, but at the same time, I can see even in creative tasks, AI can be a very strong partner. I’m talking about any creative task, like invent a name for a product, let’s brainstorm the mission for the company. AI can actually be doing a pretty impressive job. That’s the type of job where you will hire experts, where you will use some of the best people in your team to help you for days. We say, “You can do quite a lot.” It’s an interesting one because I think there is some unique human creativity, and at the same time, AI can be pretty strong at creative task as well.Nuno Gonçalves Pedro I agree. In particular, if it represents benchmarking, if it represents repetition, if it represents seeing the world and then coming up with something that presents itself as creative, to be honest, it can actually outpace humans. If it’s like genuine light bulb moments of creativity, angles that haven’t been tried before, certainly not in the same way, I think humans still have the advantage. To your point, I agree. This is not a humans-win situation. On the previous one, on emotion, still, part of it is because, also on emotion, there are exchanges. You and I might be looking at each other, and from the facial expressions and the reactions, where you judge that for AI to get there, it’s going to take a long time. There’s going to be a lot of very complex algorithmic stuff put into that for AI to be able to create synthetic emotional behaviors, but creativity, I agree with you. There are a lot more nuances to it today, where AI does have significant advantages at the end of the day. Synthesis depends. Synthesis, I feel, if we’re talking about holding a bunch of messy assumptions, contextualized inputs with different layers of data adjacent to them and then trying to create and form one coherent, fully accountable point of view that you stake something on, like a decision, a company, a business unit, whatever, I think humans have the advantage. Part of it is the complexity of what we have today with generative, pre-trained transformers, today with GPTs, where the hallucination comes through, where it’s really more statistical analysis. Over time, maybe synthesis will be a forte for AI. Right now, I think we still have that ability to really be the ultimate decision-makers and judge-makers and have that wisdom put at the table to make those decisions. Honestly, models are very good on balancing both sides, so ended up, as we say in Portuguese, neither fish nor meat. It’s to balance both sides’ answers. That’s not helpful in most cases. When you’re in a difficult position where, for example, the future of a company, company is almost dying, what do you do? I’m not sure your AI algorithms that are going to give you a great solution. Because it will give you a median or average solution, which likely will lead you to a median or average outcome, which in this case would be failure. Again, on synthesis, there are some areas of advantage for human beings. If you are looking for clearly synthesized perspectives on certain elements that are maybe less edge-focused, they’re more than the normal part of the normal distribution, then probably AI agents are brilliant at that. All the tools we have today are pretty good at that, and I think they’ll just get better over time. That’s how I see synthesis.Bertrand Schmitt I think a lot of improvements will come with a better fine-tuning of agents to what’s special about your company. Because if you just take a general agent, there is only so much. It can understand your industry, your company, and your way of working. I think that part of making sure your agents are finely trained, finely tuned on your own business, so that they can give you a really well-calibrated feedback, will have a lot of importance.Nuno Gonçalves Pedro I think that’s absolutely spot on. Maybe to end it, what is definitely different about humanity? Definitely, emotion, as we discussed, some pieces of synthesis. Creativity, maybe the light bulb creativity, not the more repeatable creativity, the one that you can put and encapsulate into processes in some ways. There are elements of us being physical, which robots can’t still recreate. That’s definitely an advantage. The embodied, we’re embodied. That’s obviously a huge advantage. With that also comes advantages because we have to interpret each other, and we have to see the complexities in physicality that land to it. Is human and the human element categorical difference? If we’re having a more philosophical discussion around this, I think it is. I think it will be for at least the foreseeable future and maybe decades to come, even in whatever scenarios we’ll discuss, which is our next section, scenarios.Bertrand Schmitt I would say projecting beyond 10 years is always pretty hard on this because, again, some of the improvements we are talking about we can imagine based on how it has evolved, but at the same time, there will be disruptions in AI. Stuff that we take for granted in terms of weakness, especially, might not be there in a few years from now. Either because it has been solved through brute force or because the field will have made significant change and improvements and discoveries, making some of our points moot. If we talk about embodiment, obviously, robots are coming. How fast, how cheap? That will be a big question. Right now, they’re not very smart. They’re usually very specialized. The more we move to a more general form factor, humanoid form factor, the more I think it will change. Also, another piece of the puzzle is that we have the assumption of agents having trouble to convince humans and stuff. At some point, we keep assuming that humans in the loop. If we’re talking about agents convincing another agent, not having embodiment might be even more efficient. That will be another perspective. Going forward, we will have not just agents we control who are doing a job and scanning the job, but agents truly interacting with other agents. You have agents controlled by one person, one team in your company, working either together or maybe not confrontationally, but trying to think and having different perspectives with another agent, controlled by other teams. I don’t think we have seen much of that now. We have seen mostly agents that are controlled by one team doing one job in one direction. Not multiple teams agents working together, or against or in parallel with another team agent. I think we will see some interesting things coming out of that.Nuno Gonçalves Pedro Scenarios Switching to scenarios, we love our two-by-twos. We haven’t done one in a while. This time it’s a two by two. We have four scenarios. I think on one axis, we would have potentially the capabilities of AI. One side would be more incremental. The other side would be the extreme full AGI. I’ll define it in a bit so that we can at least have a little bit of a definitional view on what the AGI is. Then the other axis would be how gains are distributed, concentrated versus broad. Obviously, if they’re very concentrated, it’s more unequal. It only goes to a few companies, a few people, a few individuals. If it’s broad, it’s much more dispersed through society, et cetera. AGI, just to try to define it, the formal definition of it is that it’s a hypothetical AI that matches or exceeds human capabilities across virtually all cognitive and practical tasks. In some ways, AGI can learn, reason, and adapt to novel situations across any domain. Then there are several mutations on this, but there’s one notion, or rather, there are three notions that normally are across a lot of these definitions. One is generalization, ability to seamlessly transfer knowledge from one domain to another without needing retraining, which is a very impressive skill that we humans still seemingly have. Autonomy in agency, the capacity to operate independently, set goals, plan and execute complex tasks. I think AI is their issue with agents to a lot of that extent. Then, last but not least, human parity, performing economically valuable work at or above the level of a typical human knowledge worker. If you listen to one of our last episodes, you’ll realize that Bertrand and I have slightly different views on AGI, and if it’s already here or not. I think, definitionally, maybe we have slightly different views on what the definition actually is. For me, maybe AGI is a little bit more what some would call superintelligence and generalized superintelligence. Strict to census, Bertrand is more connecting to AGI as in its prime definition. It behaves as well or better than a human thing. Maybe that’s what’s leading us to differences on whether AGI has arrived or not.Bertrand Schmitt Personally, I will have a different scale where I will put AGI, as you just said, in some ways, relatively similar in performance to your average human being. On top of it, it’s able to touch different domains that most humans are not able to do. Usually, there is some level of specializations where in AI, it can be more generic. I will put ASI, Artificial Superintelligence, as clearly the step beyond. Something that, on any dimension you pick, it’s able to beat a human expert. From my perspective, I think we already discussed that, but we are at AGI already. We have AI that can do way better, not just way better, but at least as well as humans on many topics, sometimes better. Yes, there are some topics that are not for AI yet. Embodiment, for instance, to flock with your humanoid robot in 2026. For me, we are partially there or fully there in AGI. If we take the stricter definition, ASI, we are definitely not there, but my guess is that it’s moving quite fast. We might be there in a few years from now. I don’t think we are talking about multi-decades. It’s 5 years, maybe 10. Of course, there are questions because people will say, for instance, “Hey, how do you become truly super-intelligent when all your training is based on human data?” That’s not an easy one because how do you train on that? To be way better, not just a bit better, but way better. Maybe I’m going on a tangent, but some are looking at AI learning from AI, AI being taught from AI, AI fighting with AI, AI challenging AI. The same way we saw this AlphaGo moment where AI was not trained anymore, like in chess with human moves, but has been trained to play against itself. That’s when it reached superintelligence in Go. It reached superintelligence by playing against itself and basically letting go of that human baggage, if you want, and going to the next level. What I found interesting in that, actually, first, that’s what happened, but two, there was some analysis that the average level of Go players and the top players went up after AlphaGo because AlphaGo, in a way, opened doors that humans didn’t believe were open in front of them, or they didn’t see them. They didn’t see these doors, so they didn’t bother to open them. AI opened new doors, but interestingly enough, humans improved after that, thanks to AI. You see what I mean? It was an interesting, okay, that self-learning from AI was the way to go beyond the current level of human knowledge and human expertise, but at the same time, humans were able to follow up. It was not like suddenly humans are totally useless crap. They improved. Did they still beat AI? Maybe not, but it was definitely also helpful.Nuno Gonçalves Pedro Back to our scenarios. We’re going to take the definitional extreme just for argument’s sake for scenarios. We’re going to talk about maybe what you were saying, ASI rather than full AGI, but like ASI. Again, artificial superintelligence as the extreme on the one hand. Let me talk about maybe the first scenario that would come to mind. Maybe we can call it the plateau scenario. All of this was great, but it was all smoke and mirrors. They were great at some cognition stuff. They’re a great tool. At some point, they’re going to hit a wall. Hallucinations are never going to be a thing of the past. We can’t fully trust them on really hardcore stuff. We’ll gain productivity enhancements. We’ll keep gaining those productivity enhancements, but at some point in time, we really won’t reach ASI. We really will be stuck with what we have. It’s a little bit like we get the next big thing, the next big spreadsheet, the next big internet, but it’s not going to change the whole world beyond just productivity, enhancements, and amazing tools that we have available to us that makes us much better. In that scenario, the winners will continue being fast adopters, probably small and medium businesses, because there won’t be a push for maximum speed either, so they’ll catch up at some point. Then AI native companies will be better companies than other companies, but not necessarily overall disruptors across the board. It’s not necessarily a new species of companies. It’s just companies that are a little bit better at doing stuff, which we also saw during the internet phenomenon and that first big push forward and then bubble, where we had some companies that were fundamentally different on how they operated. It took us another couple of decades for companies to be more and more digitally native along the way. Basically interesting, but it’s boring. It’s like, cool, we got tools, we got promised the world. What are the implications? All these companies that are worth trillions and trillions of dollars are not worth trillions and trillions of dollars. Because at some point we’ll face competition, commoditization. It will just be tools and platforms. They will not unlock that next stage. Therefore, this will have been a bubble, and likely it would be a hard landing to that bubble. That’s the implication.Bertrand Schmitt I would just say that, yes, I agree with you, but I would just say overall, even if it stopped today in terms of quality improvement, speed or stuff, or it barely improves, I still think we will have 10 years of madness just to leverage everything that we have today.Nuno Gonçalves Pedro Understood, Bertrand. This is a scenario. I understand, but maybe we’re going to hit a wall, and we’re going to hit that wall next year, or we’re going to hit that wall in 2 years or whatever.Bertrand Schmitt Possibly. I’m just saying we still have 10 years of goodness from that big push in AI we experienced the past few years.Nuno Gonçalves Pedro Absolutely. Agreed, but it’s boring.Bertrand Schmitt It’s boring. It’s a plateau.Nuno Gonçalves Pedro It’s a plateau. The second one is more of something that we have AI, but humans in the loop are going to be critical along the way. The judgment work that we described earlier in the episode is going to be critical to everything that happens. It’s, I would call it the augmentation scenario. The AI will be a great augmentation tool for humans, but humans will never really quite stop being in the loop. Some of the gains that AI has are broadly distributed in society and in the startup, big corporation and small medium business world. Everyone will have access to them. We humans, are still very important. We have all these augmentation things, and AI is mostly benign. There will be a couple of issues, but honestly, at the end of the day, we’re just better. We’re better, faster, more data-driven, more factually current. We’re doing stuff faster, but humans
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Anil Seth is a professor of cognitive and computational neuroscience at the University of Sussex and one of the world's leading researchers on consciousness. His central claim is both unsettling and liberating: we do not passively receive reality, we actively generate it. The world that appears when you open your eyes is not a transparent window onto what is actually out there. It is the brain's best guess, a controlled hallucination, built from the inside out.What We Dive Into:1. Your brain is not reading out the world like a camera. It is making constant predictions about what is out there and updating them with sensory signals. What you perceive is much more a product of those internal predictions than of the raw sensory data coming in. In that sense, all perception is a form of hallucination, just one that happens to be grounded in reality.2. Just as the brain generates a model of the external world, it generates a model of the self. The feeling of being a continuous, stable "I" is another best guess, not a fixed essence waiting to be discovered. This is both humbling and freeing, because a constructed self can be examined and updated in ways a fixed one cannot.3. As AI systems grow more sophisticated, they will increasingly appear to have inner lives. Anil draws a sharp line between intelligence and consciousness, arguing that current AI lacks the biological, embodied, self-regulating properties that we believe underlie genuine experience. Confusing performance for presence is not just a philosophical error. It shapes regulation, design, and how we treat these systems.Know Thyself, but not by yourself. A guided space to return home to yourself.https://www.knowthyselfcollective.com✨THANK YOU TO OUR SPONSORS:https://www.im8health.com/knowthyselfcode KNOWTHYSELF for an exclusive offerhttps://www.mudwtr.com/knowthyselfcode KNOWTHYSELF for up to 43% off sitewide___________00:00 Intro01:47 We Don't Perceive the World — We Generate It04:29 The Brain as a Prediction Machine07:22 Controlled Hallucination: What That Term Really Means09:37 Reality Is Real, But We Never Perceive It Directly13:25 Optical Illusions as Windows Into Perception16:42 Perceptual Humility and Echo Chambers19:34 The Perception Census: Mapping Our Inner Diversity25:24 Perception, Priors, and Personal Suffering29:17 Memory, Identity, and What We Expect to See33:19 The Minimum of Consciousness: Bare Awareness38:15 Pure Consciousness and the Feeling of Being Alive41:51 Can the Contents of Experience Tell Us About Consciousness?45:46 Out of Body Experiences and the First-Person Perspective50:31 Psychedelics, Reality, and the Sense of Realness57:12 Anil's LSD Experience and Mary's Room1:03:47 Defining Consciousness: Nagel's "What Is It Like"1:07:25 The Distribution of Consciousness: Animals, Trees, and AI1:17:27 Steel-Manning Panpsychism1:21:09 Pragmatic Materialism1:34:23 Intelligence vs. Consciousness: Why AI Is Different1:38:17 Why Current AI Is Probably Not Conscious1:43:53 The Danger of Believing AI Is Conscious1:49:24 What Happens When We Take the Self Too Seriously1:52:01 Reflections on Death and What Dies1:57:34 The Self as Process, Not Essence2:02:00 Free Will as a Useful Perception2:05:43 Closing Message: Celebrate the Everyday Miracle of Consciousness___________✨MORE FROM ANIL SETH↳Instagram: https://www.instagram.com/profanilseth/↳Books: https://www.anilseth.com/read/books/↳https://www.anilseth.com
By the end of this episode, you'll understand how lawyers are actually AI maxxing in 2026, and how you can, too.Timestamps:0:00 Intro1:58 Why it's the best time to be a lawyer3:03 Hallucinations and cognitive surrender 5:33 Quality over efficiency8:13 Why AI upends legal work15:20 60% of contracts filed to EDGAR have mistakes20:04 How LLMs actually work25:59 Zero data retention, explained29:16 The privacy risk beyond training39:16 How to prompt 47:03 Michael Showalter's AI-native litigation stack55:27 Spellbook's Compare to Market Feature1:03:10 Building a regulatory agent1:09:50 The judgment crisis for junior lawyers1:12:15 Cooley's AI training methodYou'll hear from 10 people at the cutting edge of legal AI:Zack Shapiro, Founder and Managing Partner at Rains LLPMolly Abraham, General Counsel at CoinbaseSujit Raman, Chief Legal Officer at TRM LabsMichael Showalter, Founder of Showalter PLLCErich Dylus, attorney, programmer and creator of CamoTextAaron Kelly, General Counsel and open source AI expertDavid Wang, Chief Innovation Officer at CooleyScott Stevenson, CEO of SpellbookJustin McCallon, CEO of StrongSuitSamson Enzer, Partner at Cahill Gordon & ReindelThis episode is presented by Altitude, visit altitude.xyz/law to learn more about their financial operating system.Newsletter: Stay updated on emerging tech law for free at lawofcode.fm.Any feedback on this episode? Or how to improve the podcast? Click here: https://docs.google.com/forms/d/1QAcE1sQAKZIkma20DbyB5frgKdiK8UB6Fkb6CwaVP1I/edit Sponsors: Thank you to the Hyperliquid Policy Center and Solana Policy Institute for supporting this podcast.To get in touch with the Cahill team about how any issues discussed in this episode apply to your situation, email mtomsky@cahill.com. Disclaimer: This podcast is for informational and educational purposes only and does not constitute legal or investment advice. Views expressed by guests are their own and do not necessarily reflect those of their employers. Listening to this podcast does not create an attorney-client relationship.
In this seven-year anniversary episode of the Events Demystified Podcast, Anca Platon Trifan uses live event production examples to show how confident, premature conclusions can waste time, then connects that dynamic to AI hallucinations: confident but false “confabulations” such as fabricated citations, links, dates, and quotes. She explains how large language models generate plausible text without guaranteeing truth, highlights the 2023 Mata v. Avianca case where lawyers filed non-existent cases generated by ChatGPT, and argues hallucinations reveal existing gaps in human critical thinking. The episode covers why polished answers feel credible (illusory truth effect, confirmation bias), why critical thinking matters more than prompt engineering, and how overreliance scales risk in event work (contracts, permits, schedules, accessibility, bios, stats). She outlines verification practices: assess consequence, identify key claims, return to original sources, avoid “AI-to-AI” verification, preserve uncertainty, and limit agent authority based on impact.
Consciousness and the Bicameral Mind - The Julian Jaynes Society Podcast
"Modern psychiatry treats hearing voices as a rare, severe symptom of a broken mind, associating it almost exclusively with conditions like schizophrenia. In this framework, an unbidden voice is proof of a biological malfunction. However, today, hundreds of recent studies have documented auditory hallucinations across a wide spectrum of perfectly healthy populations. These are people with no history of mental illness, who nonetheless report hearing voices with the same clarity as an external speaker. ..."Learn more by reading "Conversations on Consciousness and the Bicameral Mind," currently on sale for a limited time:https://www.amazon.com/dp/1737305534/https://www.julianjaynes.org/book/conversations-on-consciousness-and-the-bicameral-mind/Produced by Marcel Kuijsten using generative AI tools and reviewed by human editors for accuracy and clarity.
This week on the Bad Taste Video Podcast we are LIVE to discuss one of the first Polonia brothers films, this week we covered "Hallucinations" from 1986!!!Join us every Tuesday night at 10PM EST!!!https://www.youtube.com/@badtastevideopodcastVisit our website!!!!https://www.badtastevideo.com/#horror #film #Hallucinations#BadTasteVideo ★ Support this podcast on Patreon ★
In this episode, Tudor Achim, Co-Founder and CEO of Harmonic, the AI lab behind Aristotle, a mathematical reasoning system that won gold at the International Math Olympiad, makes the case that AI hallucinations aren't the problem with today's models. The real problem is that nobody can verify whether a hallucination is right or wrong. Tudor explains why his team bakes formal, computer-checkable verification (using a language called Lean) directly into how Aristotle reasons, so instead of trusting an AI's word, you can mathematically prove it's correct. Liam and Tudor go deep on what "truth" actually means in mathematics versus the real world, why Andrew Wiles's famous proof of Fermat's Last Theorem had a two-year hidden flaw, and why Tudor believes math is in the middle of its first fundamental shift in 4,000 years, moving from proofs written in English to proofs written in verifiable code. They also get into a spirited debate about the U.S. education system, what Harmonic actually looks for when hiring (hint: it's not the résumé), and why Tudor thinks AI will never be trusted to grade its own homework. Key Topics Covered What "truth" means in mathematics versus science, and why logical reasoning is really just a simple form of math Why the proof of Fermat's Last Theorem had a hidden flaw for two years, even after being announced Why hallucinations are actually necessary for AI reasoning, and what separates a good hallucination from a bad one How Harmonic uses Lean and formal verification to make Aristotle's math proofs checkable step by step, like reviewing code Why Tudor doesn't think any AI will ever be trusted to fully verify its own output Why Harmonic gives away the Aristotle API for free right now, and where the business model is headed The "phase transition" Tudor believes is happening in math for the first time in 4,000 years: from English proofs to machine-verified code Why open-sourcing formal math matters more to Harmonic than keeping a competitive edge Harmonic's five-year goal: contributing to solving a Millennium Prize Problem by 2028 A debate on whether the U.S. education system actually teaches critical thinking, and what AI should (and shouldn't) change about how kids learn What Harmonic actually looks for when hiring, and why résumés carry almost no signal anymore Tudor's take on inequality, taxation, and what a healthy AI-driven economy could look like Episode Timestamps 00:00 Intro and welcome 00:33 What is truth, and is logic just math? 07:59 Why the company is called harmonic.fun 08:48 The real problem with LLMs and truth 12:57 How formal verification works inside Aristotle 16:43 Who else is building in this space 19:48 Can AI ever be verified with 100% accuracy? 23:10 Why AI can't fully verify its own answers 26:46 Open-source math vs a venture-backed business 29:39 The five-year goal: a Millennium Prize Problem by 2028 31:51 Has Harmonic's vision changed? 34:28 AI, formal verification and the future of education 44:34 Debating the US education system 45:09 How Harmonic hires and what they look for 50:21 Testing for trust and honesty in interviews 54:14 Harmonic's biggest challenge right now 58:19 The future of working with AI 59:55 Family, kids and optimism about the future 1:01:38 Inequality, capitalism and AI's role in the economy 1:04:19 Why Tudor does what he does 1:05:19 Where to find Tudor Connect with Tudor on LinkedIn: https://www.linkedin.com/in/tudorachim/ Partner Links Upgrade your AI toolkit: https://www.theaireport.ai/ai-executive-pass Subscribe to our free newsletter: https://newsletter.theaireport.ai/subscribe Join the community: https://community.theaireport.ai/checkout/the-ai-report-welcome-gift?coupon_code=WRTH Learn more about your ad choices. Visit megaphone.fm/adchoices
Hallucination is not the biggest risk in clinical AI. Omission is — and it is far harder to detect. John Laursen, SVP at IMO Health, has spent his career on the layer of healthcare AI that gets the least attention: clinical terminology and the semantic data infrastructure underneath every model deployed in a hospital. IMO Health's terminology has been built and curated since 1994 and now sits behind roughly 12 billion terminology search transactions a year across US provider organisations and every major EHR. In this interview with Tjaša Zajc, Laursen makes the case that structured data was necessary but is no longer sufficient. AI reasoning across a thirty-year patient chart needs semantic continuity — an understanding that clinical language recorded in the 1990s and language recorded today can mean the same thing. Without it, health systems are investing in models that cannot reliably interpret their own records. The conversation also covers what happens when ambient AI scribes get it wrong, why accumulated clinical data has become a computational cost rather than an asset, and why clinician trust is the constraint that determines how fast clinical AI can move. Guest: John Laursen — Senior Vice President, IMO Health (Chicago, US) Host: Tjaša Zajc — Faces of Digital Health What the conversation covers: - Why omissions, not hallucinations, are the underrated risk in clinical AI - What a semantic layer does that structured data alone cannot - How clinical terminology maps to SNOMED CT and ICD-10 — and why those code sets were built for different purposes - Ambient AI scribes: what happens when a model mishears or over-infers a diagnosis - The billing and clinical consequences of an error entering the patient record - Why problem lists hundreds of entries long now cost money in token burn - Patient-generated and AI-generated content entering the EHR, and why health systems resist it - Translating lay language into clinical terminology without losing specificity - Ambient documentation, billing intensity and friction with payers - How data quality expectations differ between the US, the NHS, the Gulf states and Singapore - Who governs clinical data as coding complexity increases - Why AI performance breaks down on rare disease and the difficult 20% of cases - Knowledge graphs as a grounding source for clinical AI models - What health systems should require from AI vendors before clinical deployment Chapters: 02:20 Why the data layer decides what clinical AI can do 03:27 Inside IMO Health: 12 billion terminology searches a year 05:36 Keeping terminology current: SNOMED, ICD-10 and clinical governance 07:35 The semantic bridge: why structured data alone is not enough 10:17 Patient language versus clinical language in the record 12:23 When an ambient scribe mishears: clinical and billing consequences 14:53 Omissions, bloated problem lists and unnecessary token burn 19:12 Outside the US: the NHS, the Gulf, Singapore and coding complexity 20:46 Who governs clinical data as complexity increases 23:35 Patient-side AI recorders and resistance to external data 26:08 Ambient documentation, billing intensity and payer friction 29:21 The last 20%: rare disease, model limits and AI governance 33:38 Grounding, clinician trust and the cost of misfiring Faces of Digital Health: Website: https://www.facesofdigitalhealth.com Newsletter: https://fodh.substack.com Spotify: https://open.spotify.com/show/4cElKJHrauyP6QJQaCkvdY Apple Podcasts: https://podcasts.apple.com/gb/podcast/faces-of-digital-health/id1194284040 LinkedIn: https://www.linkedin.com/company/faces-of-digital-health #digitalhealth #healthcareAI #clinicalinformatics #EHR #ambientAI #interoperability #healthdata #SNOMED #healthIT #medicalcoding
Guest Franz Borghardt 1,700+ Hallucination Incidents: Since ChatGPT's 2022 debut, courts worldwide have flagged over 1,700 cases of hallucinated content appearing in legal briefs, getting attorneys sanctioned or disciplined. More than lawyers judges use AI as well AI Already in Legal Research: Westlaw and LexisNexis already sell AI-powered research tools with built-in verification, unlike ChatGPT, which can fabricate entire fake cases with realistic facts and analysis. Using AI instead of humans for jurors Verification Is the Line: Borghardt argues using AI is fine if you verify its output against known real cases; the danger is trusting AI-generated citations with zero independent research. Could AI "Be" a Justice?: The hosts debate whether AI could replicate a justice like Ginsburg or Scalia using their full body of writings, though novel technology cases remain a challenge. Predictive Litigation Software: The group floats a subscription tool that predicts Supreme Court questioning and rulings using sitting justices' case history, calling it a coming financial opportunity.
In this episode, we review the high-yield topic of Delusions, Hallucinations, Illusions, and Loose Associations from the Psychiatry section.Follow Medbullets on social media:Facebook: www.facebook.com/medbulletsInstagram: www.instagram.com/medbulletsofficialTwitter: www.twitter.com/medbullets
Richard McGirr talks to Tal Shahar, CEO of Atlas Invest, reveals how they've harnessed AI and innovative infrastructure to transform real estate credit into a lightning-fast, scalable engine that closes deals in days, not months. Imagine getting a same-day term sheet for bridge loans of up to $15 million, backed by multifamily and mixed-use properties, without traditional barriers like prepayment penalties or high LTVs. Tal Shahar Co-Founder and CEO of Atlas Invest Based in: New York Where to find them: http://linkedin.com/in/tal-shahar https://atlas-invest.co/ Book your free demo today at bill.com/bestever and get a $100 Amazon gift card. Visit https://malabarhillcapital.com/ for more info. Podcast production done by Outlier Audio Learn more about your ad choices. Visit megaphone.fm/adchoices
Deepti Yenireddy, Founder and CEO of Boon, joins Gareth McGlynn to talk about building AI that can actually read construction drawings. A second-time founder, Deepti exited her first company, HR tech platform My Ally, and led product at IoT leader Samsara before turning her focus to preconstruction.Key Topics Covered:What Boon Does: Helping preconstruction teams bid more, bid better, and bid more accurately, at 10x to 100x speed.The AI Estimator: Deepti announces Boon's first preconstruction AI employee, working the full lifecycle from project invite to final proposal.Hallucination and Human Control: How estimators stay in the loop with a visual review platform and links back to the agent's sources.Accuracy and F1 Scores: Why Boon publishes accuracy scores for every trade and scope, and why below 90 percent creates user friction.Boon vs Generic LLMs: Why ChatGPT and Gemini can read the text in your drawings but cannot tell where a line starts and stops.Implementation and ROI: A one-hour onboarding, and a GC customer who generated 430 extra hours in a year with one person using the agent.And much, much more.You can connect with Deepti via her Linkedin: https://www.linkedin.com/in/deepti-yenireddy/
AI use has been increasing all over, including in the courtroom…and AI hallucinations have been increasing too. How are lawyers allowed to use AI? Is it ethical? Dane Ciolino, professor of law at Loyola, joins us.
* Political analyst: The state wants its pound of flesh from New Orleans * Table Talk: small plates, sushi, and fine wine * AI hallucinations in the legal system? Here's what you need to know * Why New Orleans remains a top destination for conventions * Should the US revamp its retirement systems?
Imagine a world where AI is not just a tool but a partner that elevates human judgment, responsibility, and ethics. That's exactly what Helena Almeida, VP of Ethics at ADP, reveals in this electrifying episode, a true call to action for every HR leader and business executive. Dive into the real talk about AI governance, responsible use, and how to stay human in a tech-driven world. Your organization's future depends on it. In This Episode: Helena Almeida's journey from litigator to AI ethics pioneer. The human side of AI: trust, hallucinations, and accountability. How responsible AI standards are shaped by legal and ethical boundaries. Practical advice for HR leaders: leveraging AI without losing the human touch. The critical role of friction in HR processes to ensure fairness and accuracy. Why AI isn't a replacement but an enhancer — and the importance of smart design. Navigating complex legal landscapes: pay transparency, union rules, and cross-state regulations. How business leaders without tech backgrounds can champion responsible AI. The future of HR workflows with AI: rethinking old processes and embracing healthy friction. What “responsible AI” really looks like for your organization. Timestamps: 00:00 - Welcome to the bold new era of AI in HR 02:00 - Helena's transition from litigation to AI ethics leadership 04:00 - The myth of AI without human oversight 07:00 - Hallucinations in AI and how to manage trust 09:30 - Balancing AI accuracy with human judgment 12:00 - Ensuring data integrity in AI-driven HR systems 15:00 - Managing variations in employment law with AI 18:00 - The challenge of complex, multi-layered HR rules 21:00 - Standards for responsible AI: high stakes decisions 24:00 - The importance of skilled designers and human oversight 27:00 - The impact of automation on HR talent and friction 30:00 - How business leaders can lead AI governance without technical expertise 33:00 - Rethinking HR workflows for AI integration 36:00 - Healthy friction: the secret to responsible AI in HR 38:00 - Final thoughts: humans and AI working together for better workplaces Resources & Links: ADP Helena Almeida - LinkedIn Responsible AI Guidelines Book: "Human + Machine" by H. Simmons Connect with Helena Almeida: LinkedIn Twitter Final Call: The future of HR is human. The future of AI is responsible. You have the power to shape both. Listen now — and be bold enough to lead with integrity, insight, and purpose.
In this episode, we step back from our case study (covered in Episode 1 Parts One and Two) to address a key question: how does GenAI compare to technology-assisted review (TAR)? David Beck (Head of eDiscovery UK & EMEA), Meghan Ryan (Senior Manager, eDiscovery) and Danbee Kim (Head of Digital Legal, US) cut through the hype to explore how these technologies work in practice. They examine why TAR remains central to large-scale review - particularly for precision, consistency and defensibility - and where GenAI adds value, including contextual insight and early case analysis. Drawing on real-world experience, they show why GenAI is often reinforcing (not replacing) TAR, and reframe the debate around a more practical question: what is the right approach for the matter, the data and the client?
VLOG July 8: A.I. in the courts, Quinn Emanuel is counter-accused of hallucination https://www.patreon.com/MatthewRussellLee/posts/ai-in-courts-in-163188403 DA Bragg & NYS DiNapoli find fraud, but case late in WebCrims https://matthewrussellleeicp.substack.com/p/webcrims-woes-as-da-bragg-and-nys Coastal Bend bank scam https://innercitypress.com/mergers18bcoastalbendffw070726.html Keep UN away from AI
(0:00) About the Boardroom Governance Summit (Aug 26-27, 2026) (0:55) Intro (2:44) About the podcast sponsor: The American College of Governance Counsel. (3:30) Start of interview. (4:16) Origin story Marie Bafus (5:30) Origin story Wendy Grasso (7:34) Diving into their article AI in the Boardroom: What Directors Need to Know Now (4:14) Why AI Needs Board Oversight (12:00) Caremark and Oversight Duties (15:12) Mission-Critical Risk Cases. Reference to Marchand case (2019) and Boeing case (2021) (19:18) Where AI Belongs in Governance (board level and board committees) (21:28) Defining Mission-Critical AI (24:45) Strategy, Capital Allocation, and Judgment (29:50) Board Minutes as Litigation Evidence (33:52) Private Companies, Same Duties (38:35) AI Washing and Disclosure Risks (43:10) How Boards (and Board Members) Can Use AI (47:08) Hallucinations, Confidentiality, and Privilege. Reference to U.S. v Heppner case (2026) (52:03) Building an AI Usage Policy (53:36) Recording Boards with AI (note taking apps) (57:05) Workforce Trust and Environmental Risk (1:00:00) AI for Oversight Itself (1:02:02) AI's Impact on Legal Practice Marie Bafus is a partner in Fenwick's Securities Litigation Practice and Wendy Grasso is counsel in Fenwick's Corporate Practice. You can follow Evan on social media at:X: @evanepsteinLinkedIn: https://www.linkedin.com/in/epsteinevan/ Substack: https://evanepstein.substack.com/__To support this podcast you can join as a subscriber of the Boardroom Governance Newsletter at https://evanepstein.substack.com/__Music/Soundtrack (found via Free Music Archive): Seeing The Future by Dexter Britain is licensed under a Attribution-Noncommercial-Share Alike 3.0 United States License
Most runners think a backyard ultra is crazy.Lukas Janulitis looked at that format and decided it wasn't hard enough.Try a Last Skier Standing, an event where athletes skin uphill 1,200 feet every hour, ski back down, and repeat until only one person remains. That means battling sleep deprivation, sub-zero temperatures, brutal winds, hallucinations, and the mental challenge of not knowing when it will end.In this episode, Lukas shares his experiences at Last Skier Standing, Bubba's Backyard Ultra, Swiss Alps 100, and Cruel Jewel 100. We discuss sleep deprivation, nutrition, heat training, problem-solving during races, New Hampshire mountain culture, and his upcoming attempt at the New Hampshire Appalachian Trail FKT.Topics include:-Last Skier Standing-Backyard ultras and sleep deprivation-Swiss Alps 100-Cruel Jewel 100-Nutrition and race strategy-Training for multi-day endurance events-Hallucinations and mental resilience-The New Hampshire AT FKT-Why people keep pushing their limitsIf you've ever wondered what happens when someone decides to stay awake for days while skiing uphill, this conversation is for you.Support our Sponsors: Sawyer: https://sawyerdirect.net/Janji (code: Freeoutside): https://snp.link/a0bfb726CS Coffee: CSinstant.coffeeGarage Grown Gear: https://snp.link/db1ba8abSubscribe to Substack: http://freeoutside.substack.comSupport this content on patreon: HTTP://patreon.com/freeoutsideBuy my book "Free Outside" on Amazon: https://amzn.to/39LpoSFEmail me to buy a signed copy of my book, "Free Outside" at jeff@freeoutside.comWatch the movie about setting the record on the Colorado Trail: https://tubitv.com/movies/100019916/free-outsideWebsite: www.Freeoutside.comInstagram: thefreeoutsidefacebook: www.facebook.com/freeoutside#Trailrunning #Runningnews #Outdoors #Outdooradventure
Host Anca Platon Trifan kicks off the Events Demystified Podcast seven-year anniversary month, reflecting on how the show has evolved since July 2019 from AV and production into broader conversations on events, technology, leadership, wellness, and AI, especially after COVID accelerated virtual and hybrid work. She notes passing 200 episodes, completing Season 10, entering Season 11's CEO edition, and recording in varied live settings, then thanks past guests, her behind-the-scenes team, partners, family, and listeners for their trust, feedback, and engagement.Anca emphasizes the responsibility of having a platform and her commitment to honest, sometimes uncomfortable discussions without sponsor control. She previews a five-Friday July mini-series on AI:
Send us Fan MailIn case you missed it.....RAG didn't just survive the LLM boom — it became the backbone of how enterprises put AI to work. In this replay, we sit down with Douwe Kiela, the AI researcher who led the team that introduced Retrieval-Augmented Generation in 2020 and went on to co-found Contextual AI. Douwe unpacks whether RAG is here to stay, why most enterprise AI dies in the gap between a great demo and production, how to tame hallucinations, and what actually separates a real "agent" from the buzzword. A clear-eyed conversation on building AI that's grounded, useful, and safe to deploy.00:46 Introducing Douwe Kiela 01:37 RAG - Here to Stay or Go? 06:59 LLMs with Context 08:20 Making AI Successful 10:34 Why Contextual AI? 17:18 LLM versus SLMs 20:28 Speed over Perfection 22:07 Hallucinations 26:02 Making AI Easy to Consume 28:50 Defining an Agent 32:53 Reaching Contextual AI 33:14 The Contrarian View 34:37 The Risks of AI 36:53 For FunLinkedIn: linkedin.com/in/douwekiela Website: contextual.aiWant to be featured as a guest on Making Data Simple? Reach out to us at almartintalksdata@gmail.com and tell us why you should be next. The Making Data Simple Podcast is hosted by Al Martin, WW VP Technical Sales, IBM, where we explore trending technologies, business innovation, and leadership ... while keeping it simple & fun.
Send us Fan MailIn case you missed it.....RAG didn't just survive the LLM boom — it became the backbone of how enterprises put AI to work. In this replay, we sit down with Douwe Kiela, the AI researcher who led the team that introduced Retrieval-Augmented Generation in 2020 and went on to co-found Contextual AI. Douwe unpacks whether RAG is here to stay, why most enterprise AI dies in the gap between a great demo and production, how to tame hallucinations, and what actually separates a real "agent" from the buzzword. A clear-eyed conversation on building AI that's grounded, useful, and safe to deploy.00:46 Introducing Douwe Kiela 01:37 RAG - Here to Stay or Go? 06:59 LLMs with Context 08:20 Making AI Successful 10:34 Why Contextual AI? 17:18 LLM versus SLMs 20:28 Speed over Perfection 22:07 Hallucinations 26:02 Making AI Easy to Consume 28:50 Defining an Agent 32:53 Reaching Contextual AI 33:14 The Contrarian View 34:37 The Risks of AI 36:53 For FunLinkedIn: linkedin.com/in/douwekiela Website: contextual.aiWant to be featured as a guest on Making Data Simple? Reach out to us at almartintalksdata@gmail.com and tell us why you should be next. The Making Data Simple Podcast is hosted by Al Martin, WW VP Technical Sales, IBM, where we explore trending technologies, business innovation, and leadership ... while keeping it simple & fun.
Host(s): Dr. Mary Goldberg, Co-Director of the IMPACT Center at the University of PittsburghGuest(s): Walter (Wally) Boot, PhD (CREATE / ENHANCE), Sara Czaja, PhD (CREATE / ENHANCE) IMPACT Center | Website, Facebook, LinkedIn, Twitter CREATE (Center for Research and Education on Aging and Technology Enhancement ) | Website https://create-center.org/ ENHANCE (Enhancing Neurocognitive Health, Abilities, Networks, & Community Engagement) Center | Website https://www.enhance-rerc.org/ Discussion Topics (Timestamps)00:00 Introduction / Guest bios01:20 Unsolved challenges in aging and technology04:30 Why products still fail older adults07:00 Misconceptions about aging and tech literacy09:30 AI and Medicare decision support14:30 Hallucinations, trust, and RAG systems18:45 AI for cognitive support and caregivers21:30 Privacy, scams, and deepfakes25:00 VR and social isolation29:00 VR risks: falls, cybersickness, home use32:00 VR for wayfinding and ADL training35:00 User-centered design and needs assessment37:30 Community implementation + closing
Anil Seth has spent more than twenty-five years asking one of the most disorienting questions a scientist can ask: what is it that makes you conscious, and are you as real as you feel? A Professor of Cognitive and Computational Neuroscience at the University of Sussex and Director of the Sussex Centre for Consciousness Science, Seth has published more than 200 research papers and is recognized by Web of Science as being in the top 0.1% of researchers worldwide in his field. His 2017 TED talk, "Your brain hallucinates your conscious reality," has been viewed more than 14 million times, one of the most-watched science talks in TED history. His 2021 book, Being You: A New Science of Consciousness, became an instant Sunday Times bestseller and a Book of the Year for The Economist, The Guardian, The Financial Times, The New Statesman, and Bloomberg. In 2023, he was awarded the Royal Society Michael Faraday Prize for his extraordinary contribution to public engagement with science. In 2025, he won the Berggruen Prize Essay Competition for "The Mythology of Conscious AI." And in 2026, he delivered a new main-stage TED talk: "Why AI is unlikely to become conscious." His central argument is as simple as it is radical: we do not perceive the world as it actually is. Instead, the brain is a prediction machine, constantly generating its best guess about what's out there, using sensory signals only to correct its errors. What we experience as reality is a "controlled hallucination." And crucially, the self, the very sense of being a "you" behind your eyes, is part of that hallucination too. In this wide-ranging and mind-bending episode, Anil unpacks the ideas at the frontier of consciousness science, exploring: The controlled hallucination: why every perception you have, color, pain, the weight of your own body, is a construction of your brain, not a window onto objective reality, and what that means for how you move through the world The predictive brain: how your mind is not passively receiving information but constantly generating predictions, and why the experience of surprise is actually your brain updating its model of the world The "beast machine" theory of selfhood: why Anil believes that consciousness is not just about computation but is deeply grounded in the biological drive to stay alive, and why that matters for debates about AI The hard problem and the real problem: why philosophers have spent decades asking why there is subjective experience at all, and why Anil thinks we've been asking the wrong question Animal consciousness and the octopus: what creatures with radically different nervous systems reveal about the many possible ways of being conscious, and why this should expand our moral circle Can AI be conscious? Why Anil argues, against the dominant view in Silicon Valley, that large language models are almost certainly not conscious, what we'd actually need to look for, and why anthropomorphizing AI carries real societal risk The Dreamachine: how Anil led a groundbreaking science-art project that used stroboscopic light to induce visual hallucinations in more than 35,000 people, and what the largest ever citizen science study into perceptual diversity revealed about how differently each of us experiences the world What the neuroscience of consciousness means for medicine: from anaesthesia and disorders of consciousness to psychedelics and mental health, how understanding the brain's generative nature opens new clinical possibilities Free will, the self, and what's left: if the self is a controlled hallucination, does that mean we aren't really in control? And is that terrifying, or strangely liberating? This is a deeply searching and surprisingly personal conversation about the most intimate fact of human existence: the experience of being you. Learn more about Anil's work at anilseth.com, and find his book, Being You: A New Science of Consciousness, wherever books are sold. His TED talks are available HERE, and his 2025 Berggruen Prize essay, "The Mythology of Conscious AI," can be read at Noema Magazine.
Saving The World From Soda: How ‘Big Soda' Has Made The World Sicker Would you believe that the soda industry's success is largely built on the manipulation of data and scientists? Our guest this week details all the behind-the-scenes scheming that created a billion-dollar industry. She discusses how Big Soda schemed their way into American homes by controlling how scientists studied nutrition, diets, and obesity. Guest: Susan Greenhalgh, author, Soda Science, John King & Wilma Cannon Fairbank Research Professor of Chinese Society Emerita, Harvard University The Fungal Frontier: How Fungi Could Trigger A Zombie Pandemic Human bodies are normally too hot for dangerous fungi to survive, creating a natural shield that protects us from infection. However, rising global temperatures are forcing these microscopic organisms to adapt and survive in hotter environments. Our guest this week breaks down the terrifying reality of what could happen if these heat-resistant fungi evolve to conquer our immune systems and trigger a real-world pandemic. Guest: Dr. Arturo Casadevall, microbiologist, infectious disease expert, Chair of Molecular Microbiology & Immunology, Bloomberg Distinguished Professor, Johns Hopkins Bloomberg School of Public Health, author, What If Fungi Win? Facebook: ingoodhealthpodX: @ ingoodhealthpodIG: @ingoodhealthpodYouTube: @ingoodhealthpodSpotify Apple Podcast In Good Health PodcastSubscribed to the newsletterFull ArchiveContact UsBecome an Affiliate Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.
The Fungal Frontier: How Fungi Could Trigger A Zombie Pandemic Human bodies are normally too hot for dangerous fungi to survive, creating a natural shield that protects us from infection. However, rising global temperatures are forcing these microscopic organisms to adapt and survive in hotter environments. Our guest this week breaks down the terrifying reality of what could happen if these heat-resistant fungi evolve to conquer our immune systems and trigger a real-world pandemic. Guest: Dr. Arturo Casadevall, microbiologist, infectious disease expert, Chair of Molecular Microbiology & Immunology, Bloomberg Distinguished Professor, Johns Hopkins Bloomberg School of Public Health, author, What If Fungi Win? Host: Greg Johnson Producer: Kristen Farrah Facebook: ingoodhealthpodX: @ ingoodhealthpodIG: @ingoodhealthpodYouTube: @ingoodhealthpodSpotify Apple Podcast In Good Health PodcastSubscribed to the newsletterFull ArchiveContact UsBecome an Affiliate Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.
https://youtu.be/b_G8krkwKv8 Ganesh Krishnan, CEO of AiHello, is helping Amazon sellers automate advertising, improve profitability, and scale their businesses using AI. Driven by a mission to give entrepreneurs more freedom and enable them to build businesses around products they love, Ganesh shares how AI can eliminate repetitive work while allowing business owners to focus on strategy, innovation, and growth. In this conversation, Ganesh introduces The AiHello Ads Framework: Tap into the Wisdom of Crowds, Find the Right Keywords, Bid at the Right Level, Dynamically Adjust Bids, and Rinse and Repeat. He explains how AI can leverage historical marketplace data to identify profitable keywords, optimize bids automatically, and continuously improve campaign performance. Ganesh also discusses the dangers of AI hallucinations, why Amazon's incentives differ from sellers' incentives, how AI has transformed his own company's operations, and his vision for building zero-hallucination AI systems capable of advancing toward artificial superintelligence. — Build AI Superintelligence with Ganesh Krishnan Good day, dear listeners. Steve Preda here, and welcome Ganesh Krishnan, the CEO of AiHello, an Amazon Ads automation company helping you grow your revenues, reduce work hours spent on ads management, and decrease your ad costs. Welcome to the show, Ganesh. Thank you, Steve. Nice to meet you Well, it’s great to have you here, and let’s jump right in. And my first question is, what is your personal ‘Why,’ and how are you manifesting it in AiHello? So it started off with my thesis that we all need to do good towards the planet. A long time ago, I started having my own natural things, selling chemical-free, ecological, sustainable, good-for-the-planet, good-for-your-wallet, good-for-your-health items, and I would sell organic items. And eventually, what I realized was that it was taking a lot of my time marketing, managing it, changing the bids, doing everything. I started working more and more on AI because I’ve worked in AI commercially. I worked in AI in my industry. That was my job. So I said, “Why not use, apply that to my own startup, to my own industry for selling organic things?” And once I started selling it, some of my friends reached out and said, “Can we use your AI for our own businesses?” And I said, “Sure, why not?” And then I started opening it up. And then one person came through and said, “Okay, let’s release it to the general public, see how it goes.” And then as we started earning money, I realized that I don’t need to do a job. I can have this startup, and I can help different people have their own lifestyle. You could have your own lifestyle. You could sell your own stuff that you like, e-commerce, usually on Amazon, and then we help you have your lifestyle. So this is my personal ‘Why’, is we need more equality. We need more people doing stuff they love rather than doing stuff they hate to do, and they hate to wake up and go to work. So do what you love. We are here to empower you. Wow, that’s amazing. So you are empowering people to start their own e-commerce businesses on Amazon, and you help them with AI tools to get up to speed and compete with the big boys. That is correct. Yeah. I love it. So on your LinkedIn profile, you mentioned that you are, I don’t know what the word was that you used, but something to do with superintelligence, AI superintelligence. So what is it that you are doing, and what is your vision of how AI superintelligence can be tapped into? It’s a very long topic. But to start off with, we used the old form of AI, which is a lot of regression, a lot of statistics, a lot of big data learning, and a lot of neural networks, if you felt fancy. And then LLMs became a huge thing. And we launched AiHello probably six or seven years ago. LLMs became a big thing two or three years ago. And it was pretty fancy. It was very good. It made life easy for us. But we cannot use it within AiHello to give it to clients, primarily because LLMs start hallucinating once you go past a certain context. The problem with hallucination is that it exponentially becomes larger and larger. Because if the previous thesis is wrong, if your previous hypothesis is wrong, then it builds on top of it, and it builds the wrong things. Hallucination exponentially becomes worse. And when it comes to finance, when it comes to ads, and when you’re working with sensitive data, this can be catastrophic. So you cannot use these large language models for finance, for situations where you need precise data, and especially when you have lots of context. It’s going to lose the context of the first part. Just because you mentioned something at the start of the conversation doesn’t mean it’s not important. It is critical. As humans, we understand what is the most critical part of a conversation, and then we keep that in mind. But LLMs, because of context limitations, just keep on going and start hallucinating. So a few months ago, we came up with the idea that we could use something like a large language model, but not based on the transformer model. And we could base it on data so that there is almost zero hallucination. So instead of building weights, we build it based on data. And we launched this. We don’t use it on AiHello, but we decided to use it on an email service because we have a lot of emails. We process a lot of emails for clients. We process a lot of emails for specialists. So we could use the zero-hallucination approach within emails, and if it is successful, then we can put it into AiHello. And we can, of course, release it as an API as well. So this is going to set the basis of artificial superintelligence because what is stopping us right now from reaching or breaching that wall of artificial superintelligence is this hallucination. And of course, there is also logic. LLMs are pretty stup*d. They don’t understand. You can teach them, they learn, but they do not question what you teach them. They always take it on blind faith. Yeah. Wow. That is genius. I love it. You are going to un-hallucinate AI. And if it stops hallucinating, essentially it becomes a lot more powerful and scalable. AI becomes scalable, or this whole process becomes scalable. That’s fascinating. So your ‘Why’, your mission, is to empower all these people to run their businesses. Do you have a framework for this that you could describe in three to five steps? How do you get someone up and running with their own business on an e-commerce platform? Or do you have any other framework that you could share with the audience? Something simple that they may be able to benefit from? One of the caveats of using AI is that it needs a lot of data. So if you’re just starting out with your e-commerce business, you need to put more of your human intelligence, more of your gut instinct, more of your thoughts, and more of your emotions into building it out. And once you have built up enough data, then you can put it into AiHello and start automating it. So what I would say, if you’re starting an e-commerce business, is hire a specialist who can help you launch off the ground. Do a bit of the hypothesis work, do a bit of the analysis, and then come to AiHello and start automating it. You can only start automating once you have a good idea of how things work for you. And finding how things work for you is something you need to do on your own. It’s like you can’t start running, or you can’t start driving a car, until you learn how to crawl and until you learn how to walk. Okay. So basically, it’s the age-old innovation thing that you have to innovate something on your own, and then you can scale it with AI. That is correct. Yeah. So let’s say I came up with some kind of formula, concept, or product that is currently not being promoted, and I believe it would work. Or maybe I’ve already tested it and I want to scale it. I want to get on Amazon and sell it there. What can you do for me? What are the steps for me to be successful with AiHello’s help? So the first thing when you select a product, is: what are the keywords for it? What keywords do you use for that product? The second would be: what are the bids for that product? For each keyword, what is the right bid to put up? And then you have other things like budgeting. Do you change the bid depending on the time of day? Do you change the bid in total? Those are the things that you need to keep adjusting continuously. With AiHello, we automatically harvest the right keywords for your product. We change the bid. We optimize the bid. We also do dayparting, where you can change the bid depending on the time of day. So there are different things that you can use AI for. You could certainly do all of it manually, but it’ll probably take you days or weeks to do what AI can do in a couple of minutes. So a couple of minutes. But doesn’t the AI also need traffic data to be able to define things? Yeah. So one of the other things about AiHello is that, because we have the wisdom of crowds, if you come up with a keyword, we know exactly how that keyword is going to perform. As you say, you have the wisdom of crowds. Can you extrapolate what you’ve experienced with other products and other customers onto a new product that doesn’t yet have a lot of traffic? Is this what you mean by the wisdom of crowds? Or what do you mean by the wisdom of crowds? Let me give you an example. Let’s assume you want to sell coffee, and you go to our platform and say, “This is my product. It’s coffee. Help me sell it.” So what we do is, we know this is coffee. What are the keywords around it that are going to help sell it? Because we’ve sold other coffee products, we know that organic coffee sells well. We know coffee in the morning sells well. Black coffee sells well. Caffeine sells well. And we also know, based on the previous performance of other keywords, what a good bid is for each keyword. If you don’t know the keywords, then of course you have to spend time researching them. And if you don’t know the bids, then you have to spend time researching what bid to put in. But we do all the research for you, and you put it in. And the second part, the bigger part, is that if the bid doesn’t work out, if you’re not selling, then we increase the bid automatically. If you are losing money, then we decrease the bid automatically. So that bid optimization is a critical part of AiHello. Yeah. We use Amazon ads to promote my books. And yes, it takes a lot of skill to find the keywords, eliminate the negative keywords, adjust the bids, have the right bids, and avoid overspending or underspending. But Amazon also does much of the machine learning. So what is it that Amazon does, and what is it that you have to do? And why doesn’t Amazon do what you have to do? The most critical piece of information to keep in mind is that your aims and objectives are the opposite of Amazon’s aims and objectives. Amazon’s aim is to make money, and your job is to make money. You don’t care if Amazon makes money or not, and Amazon doesn’t care if you make money or not. So when you put up a bid, when you run ads, Amazon will maximize that ad spend, whatever it is. In some ways, it’s like a casino. You go to a casino, and the job of the casino is to win money from you, and your job is to win money from the casino. Ads have become a lot like gambling nowadays. You throw money into it. You expect to make money. Ninety percent of people lose money, and they give up. And Amazon always finds fresh sellers to move on. You cannot depend on Amazon because Amazon is not on your side. Yeah, that makes perfect sense. Yeah, I always thought that on some platforms it was really difficult to make money with ads. Facebook, I think, is so competitive that it’s probably very difficult to make money. I know a lot of people who have spent a lot of money on Facebook, but I don’t know very many who have figured out a formula that continues to work. Okay. So you’ve helped someone find their keywords, the right bids, and how to adjust those bids. But what we’ve found is that at some point, ads die, and then we have to switch things up. It actually happens quite frequently that you have to create new campaigns and new ads. So what’s the dynamic there? How do you optimize so that you’re not still supporting ads that don’t work anymore, and you switch at the right point? So when we say ads, it’s not technically the campaigns. A campaign is just a container for all of your ads. You have products inside it, and you have keywords inside it. So a campaign is made up of products and keywords. And the question is, when you say ads die, did the keywords die? Then you need to add new keywords, right? You always have to keep adding new keywords and testing new keywords. It’s a continuous job of trying to find the right keywords for your book or your product, and then optimizing the bids constantly to make sure that you’re profitable. You have to make sure that your ads don’t die because of a lack of fresh keywords. And of course, there’s always a limit to the number of keywords you can add because each product has a limited number of keywords that people are searching for. Maybe there’s a long-tail keyword that’s going to make money, but there’s not enough search volume. Or maybe there’s a high-volume search keyword, but it’s not profitable for you. So you have to figure out what the right strategy is for you. Eventually, if your product is good, you’ll make money. If your product is not good, you won’t make money. That’s the bottom line. With ads, you quickly find out if your product… So essentially, it’s a cyclical thing. So you find the keywords, you figure out the right bids, you adjust the bids, and then you have to find new keywords and keep doing this. Yeah. So why do keywords go stale? Do people not search for certain things anymore? There could be multiple reasons for it. One reason is that a competitor has come in and taken your search volume. And you have to know: are you losing search volume? Are you gaining search volume? Has your search volume dropped off? The second reason is that people are not searching for that keyword anymore. Is it out of fashion? The third is: are you underbidding? Is the bid too low? Again, you would know by the number of impressions. Have the impressions dropped off? If the impressions have dropped off, is it because of a competitor? If it’s not because of a competitor, are people searching less? Are your bids too low? If the search volume is the same, are people clicking less? Why are they clicking less? Is it your images? Is it your product? Is your product no longer in fashion? I mean, I don’t know. Maybe a few months ago, fidget spinners were really in fashion, and nowadays no one uses them. So those things go out of fashion. Yeah. The spinners, I remember. They’ve been out of fashion for a while. Yeah. Yeah, that’s fascinating. So it’s a never-ending cycle of innovation and figuring out what works and what doesn’t work. So let me ask you this: What drives growth in your business? Most of the growth is… There are different ways to put it. Four years ago, we used to create a lot of blogs. We used to create lots of content. We used to create lots of YouTube videos. And then ChatGPT came along. If you ask kids now, “Do you Google that?” They don’t know what Google is. They really don’t know what Google is. And that’s not a cliché. It’s surprising. They’ll be like, “What Google?” Everything goes through ChatGPT. So for us, growth went from Google to ChatGPT. And we didn’t spend enough time optimizing for LLMs on our site. So what drove growth before was blogs and YouTube. And what drives growth now is large language models like ChatGPT and Claude. People just ask ChatGPT, “What do I do about this on Amazon?” It recommends solutions, and then we go through them. So how do you leverage large language models or AI applications? This was one of the biggest boosts to our company. We managed to set the processes right. We managed to create the templates. We managed to bring structure to our company. Development work has become ten times faster. The turnaround is ten times faster. We’re able to release features quickly. We’re able to find bugs in our existing code quickly. There are a lot of things going on. If I were to say that our company is no longer the same company it was even a year ago, that would not be an exaggeration. It would be the truth. What we were a year ago is not at all what we are right now. So in what way did you change? Is it coding that accelerated and changed everything? I mean, in what other ways did you change as a company? So the code is all done with AI first. Our developers use AI. They put in the prompt, they check the results. There is a second developer who checks whether everything is okay and whether everything is done. And then finally there’s QA, and then we push it to staging. We used to do roughly one-month or forty-five-day sprints. Now we do weekly sprints. So it has gone four times faster. The biggest hurdle for us was managing clients and how we manage them. We never had any structure. So we talked a lot with ChatGPT. We talked a lot about what the right way was to bring structure and accountability into the system. We managed to set up all the software required for accountability. It helped us fix those issues. It created structure. It created accountability for all the people, and then we implemented that. Finally, the last one, which was the most debatable, is that we require a lot of content. We require a lot of graphics. We require a lot of videos for clients on Amazon. I actually went to buy something on Amazon a few days back, and what was puzzling was that when I zoomed in on the images, you could see they were AI-generated because they all had these silly AI mistakes—spelling mistakes, random words. So almost everything on Amazon right now, all the images, are kind of AI-generated. It’s hard to blame them. We ourselves use AI for a lot of the images. We make sure we don’t have the silly mistakes, but we do use AI as well. So the turnaround time for graphics is faster because of AI as well. Though some clients do complain that they don’t like AI-generated assets. And if a person looks a bit too AI-generated, they just reject it outright. So that is the most debatable part of it. But overall, our company is called AiHello. It’s AiHello. And if we don’t say hello to AI, then we’re not AiHello. Yeah. Love it. I love the head and the one arm. Yes. The hello, and that’s it. Yeah. So what is one thing that you’re actively trying to figure out in your business right now? We are a remote-first company, and I’m struggling to bring about accountability among all the team members. We do have a good number of employees. Ninety percent of our employees are good. Ten percent still have accountability issues. And for me, that is a bit of a hurdle. It is a bit of a challenge to push those people who are dragging their feet about AI. Yeah. Because they are not comfortable with AI. They want to do what they are good at and don’t want to do something new. There is also a bit of hesitation that they might lose their jobs because of AI, although we’re not planning to let go of anyone. Rather, we are hiring more people because we’re able to grow faster. There is an old saying that companies won’t go extinct because of AI, but companies that don’t use AI will go extinct because of AI. Because we are using AI a lot, there is a chance for us to scale, for us to expand significantly. And I want to tap into this advantage and grow. I want to hire more people, and I want to grow. I don’t want to let people go. So this is a very good opportunity. You hear about Coinbase letting people go. You hear about Facebook letting people go because of AI. And I think those are all nonsensical excuses. Those companies are not growing very well, and they are blaming AI for letting people go, which I think is absolutely nonsensical. There is a very good opportunity for people to grow and for companies to grow using AI and increase their hiring. If you’re letting people go because of AI, it’s just a nonsensical excuse. So what do you think is the mental hang-up for people? What prevents better AI adoption or faster AI adoption? A long time ago, when computers were being introduced into many industries, I remember there were huge protests because people thought computers would take away jobs. And it did happen. People did lose jobs because of computers. There were many people pushing papers who lost their jobs. And a lot of people refused to learn about computers because they said, “This is nonsensical. I can do it better by hand.” Can you imagine telling people right now that it’s better to do things by hand than to use a computer? I mean, if you want to do calculations, please don’t use Excel or Google Sheets. Use a pen and paper and tell me you can do it better. It would be absurd to think that way. But at that time, people really did have the mentality that it was better to do things by hand than with Excel. Now, the AI revolution is probably a thousand or a million times bigger than that. And you can drag your feet. There will always be people who drag their feet and say, “I can do it better. AI is just nonsensical.” And sure, some of that is true. But the overwhelming majority of tasks are going to be done extremely well with AI. And it’s not just large language models. It’s everything. Regression analysis, data analytics, big data analytics, forecasting, calculations. I’m not even talking about transformer models. I’m talking about everything related to AI. So much can be automated and done by AI that if you’re not involved with it, you’ll get left behind, just like the people who didn’t use computers. Do you feel like people have to be highly educated to be able to use AI? Or can people with less formal education benefit from it as well? I don’t think it has anything to do with education. I think the learning curve for AI is smaller than the learning curve for computers. If you’re already using computers, you can just install a command-line interface and have things running. Actually, you can go to ChatGPT and ask some questions, and you can build something. But if you want to build serious applications, you can use a command-line interface and build them out. I think the learning curve is probably just a couple of hours to become proficient with these tools. I’m thinking more about this: As AI tools develop and take many of the routine, repeatable tasks off our shoulders, doesn’t that mean we will spend more of our time on high-level thinking and orchestration? And won’t that require some kind of mental ability to do that? It requires you to understand context, understand the implications of things, and be able to connect the dots. So that’s what I mean. The people who can really use AI tools have this higher level of awareness and thinking. They can combine ideas and create new things. But are there AI tools that people with less advanced analytical skills can also use? Absolutely. And you’re 100% right. You’re 101% right. This is what I’ve been advocating for a very long time. Don’t spend your time doing mundane, repetitive daily activities that can be automated. Let AI handle them. You should focus on the things AI cannot do right now, which is human-level intelligence: Strategizing. Planning. Working on the bigger-picture tasks. So you’re 100% right, and that’s the direction we should be moving in. And this brings me back to the point I made earlier: You should do what you love. The things you don’t love, the repetitive tasks, should be done by AI. Yeah. Love it. So what is your vision, ultimately, for AiHello? So my vision for AiHello goes beyond AiHello. We have something called HalZero, which is the engine we want to put behind AiHello. It’s a zero-hallucination LLM. And we are working toward making it happen. We plan to release an API for it soon. If it does happen, then we would probably have a model that can take in data and answer general-knowledge questions with zero hallucination. And we’re building it based on how the human brain works. The human brain is not one-dimensional. ChatGPT is one-dimensional. Transformer models are one-dimensional. You give them data, they run it through the transformer model—the encoder and decoder—and then they give you an answer. But the human brain is built in layers. What we call the lizard brain sits at the base, and as you go higher, things become more and more complex. So the brain is information and action, and everything is filtered through it. Then we act on the filtered result. Machine learning models right now do not have these kinds of filters. They have something similar, which is called chain of thought, but that’s really thinking out loud. This kind of reasoning should exist within the latent space of the machine learning model. It should be built into the model itself. I’ll give you an example. If you had been taught all your life that the sun is green, and tomorrow you woke up in Virginia, went outside, and saw that the sun was yellow, you’d say: “Oh my God, I’ve been lied to all my life. The sun isn’t green.” You would question what you had been taught based on a single observation. But if a machine had been trained for years that the sun is green, and then it saw that the sun was yellow, it might conclude: “The sun is wrong today because I’ve been taught that the sun is green.” The real test of intelligence is this: Can it question its training data? And the answer is no. It won’t, because it has been trained on that data. It has been trained on those tokens. Yeah. So that’s AI superintelligence? The ability to question the training data? That is correct. Yeah. So we build it based on connections. How strong is this connection? How many people have stated this fact? What is my own observation? Which observation is stronger? There is always conflict. In the human brain, there is always a conflict between what people say and what we think. Then our logical brain chooses what is usually the best answer. That is how we have a collective consciousness. We also have a personal consciousness. We always have to decide which one is best. Love it. Well, that’s great. So if you’re running a business and you need to sell a product, and you want to figure out how to be successful on Amazon, how to leverage your ads, and how not to overspend, where should you go? How can people get in touch with you, Ganesh, and your team? And what’s the first step for listeners? You can send me an email at ganesh@aihello.com. You can connect with me on LinkedIn. I’m always available, and I’m happy to have a chat with you. All right. So if you’re listening out there and you’re in e-commerce, or you want to get into e-commerce, and you don’t know how to leverage all the tools that are out there, don’t forget: Amazon is in the business of making money, not necessarily making your business profitable. So you can use AiHello to help you. Reach out to Ganesh on LinkedIn and get your team involved. And if you enjoyed listening to this episode, make sure you check back every week because I have successful entrepreneurs sharing their ideas—or at least some of the good ones—with you. So thanks, Ganesh, for coming. Thank you, Steve. And thank you for listening. Important Links: Ganesh's LinkedIn Ganesh's website Ganesh's email: ganesh@aihello.com
What happens when AI starts building the next generation of AI—and even its creators admit they don't know what comes next?This week, we explore a convergence of breakthroughs, billion-dollar bets, government oversight, and legal accountability that could reshape business faster than most leaders are prepared for. Anthropic's latest research suggests we're approaching an era where AI systems increasingly improve themselves, while governments are simultaneously looking for ways to slow, regulate, or gain visibility into the process. For business leaders, this isn't a future problem. It's a present-day strategic challenge. The organizations that understand how these forces connect—from AI capability acceleration to trillion-dollar capital markets and industry-wide disruption—will be far better positioned to navigate what's coming next.In this session, you'll discover: Why Anthropic believes recursive self-improvement may arrive sooner than most institutions are prepared for. How AI is now generating the majority of code used to improve future AI systems. What the latest AI performance gains mean for software development, research, and innovation. Why OpenAI and Anthropic are pursuing trillion-dollar-scale IPOs. How AI-driven consolidation could transform industries such as accounting. The emerging government response to increasingly powerful frontier AI models. Why policymakers are exploring new forms of oversight, ownership, and control of AI infrastructure. The growing debate around legal liability when AI-generated mistakes create real-world consequences. What business leaders should be watching over the next 12–24 months.About Leveraging AIThe Ultimate AI Course for Business People: https://multiplai.ai/ai-course/YouTube Full Episodes: https://www.youtube.com/@Multiplai_AI/Connect with Isar Meitis: https://www.linkedin.com/in/isarmeitis/ Join our Live Sessions, AI Hangouts and newsletter: https://services.multiplai.ai/eventsIf you've enjoyed or benefited from some of the insights of this episode, leave us a five-star review on your favorite podcast platform, and let us know what you learned, found helpful, or liked most about this show!
Andreas Rotenberg is Co-founder and COO of Pulley, an AI-powered permitting platform helping developers and operators move projects through approvals faster. Before Pulley, he was part of the team at Honest Buildings through its acquisition, then served as Chief of Staff at Procore through its IPO. Pulley has supported over $15 billion in projects approved across the U.S. Live from ICSC+Proptech in Las Vegas.(0:00) - First ever ICSC+Proptech live podcast(1:47) - Why Permitting Is a Growing Bottleneck(2:41) - What's Happening During Permitting Timelines(4:13) - Jurisdictional Complexity Across the U.S.(5:08) - What CRE Teams Underestimate About Permitting(7:35) - Why Pulley(8:18) - The Origin Story(10:53) - Combining Technology with Local Expertise(14:26) - Where AI Creates Real Value in Permitting(17:36) - Trust, Hallucinations & Accuracy(19:07) - Municipalities & Public Sector Modernization(20:40) - Second & Third Order Effects of Faster Permitting(22:41) - Collaboration Superpower: Vaclav Smil
AFB, Anna-G, Josh and Phred welcome back ultrarunner, hospice nurse, and Vermont mountain crusher Lila Gaudrault back to the Cultra Trail Running Podcast to break down her experience at the legendary Cocodona 250. Lila takes us deep into the Arizona suffering machine, explaining how she showed up to a 250-mile race with a surprisingly loose game plan, then spent the better part of the first half battling nausea, dehydration, and the reality that 250 miles is a very long way to travel on foot. We talk about sleep deprivation, hallucination-adjacent trail weirdness, crew and pacer support, and the problem-solving mindset required when you're three days into a race and still have mountains to climb. The conversation explores how Cocodona differs from 100-milers, why the atmosphere at 200+ mile races feels more collaborative than competitive, and what Lila learned about managing fatigue, recovery, and the physical toll of multi-day events. We also dive into the science of gender differences in ultrarunning, pacing strategies, and the unique culture that develops when everyone is equally exhausted. Along the way, we discuss * Cocodona 250 race recap * Sleep strategy and managing fatigue * Nausea, dehydration, and race-day troubleshooting * Crew and pacer support in 200+ mile races * Hallucinations and sleep deprivation * Gender dynamics in ultrarunning * Vermont 100 and Backyard Ultras * Balancing hospice nursing and elite ultrarunning * Future race plans and FKTs * The upcoming CUT112 fundraiser for Connecticut Forest & Parks A four-day journey through the Arizona desert, countless lessons learned, and proof that sometimes the best race plan is figuring it out one aid station at a time. Subscribe to Lil's Substack "Running too Much" Cocodona 250 Get your official Cultra Clothes and other Cultra TRP PodSwag at our store! Outro music by Nick Byram Become a Cultra Crew Patreon Supporter basic licker. If you lick us, we will most likely lick you right back Cultra Facebook Fan Page Go here to talk shit and complain and give us advice that we wont follow Cultra Trail Running Instagram Don't watch this with your kids Twitter @BlueBlazeRunner Buy Fred's Book Running Home More Information on the #CUT112
What if some of the most promising tools for treating depression, PTSD, and trauma have been misunderstood for decades? In this episode, I sit down with Dr. Keith Kurlander and Dr. Will Van Derveer, co-founders of the Integrative Psychiatry Institute and authors of Psychedelic Therapy, to unpack the science, risks, and potential of psychedelic-assisted therapy. We discuss MDMA, psilocybin, ketamine, trauma, healing, and why these treatments are gaining so much attention in modern mental healthcare. → Leave Us A Voice Message! Topics Discussed: → What is psychedelic-assisted therapy? → Can MDMA help treat PTSD? → How does ketamine therapy work? → Is psilocybin effective for depression? → What are the risks of psychedelics? Sponsored By: → Timeline | Timeline's clinically proven formula is now more accessible. Mitopure starts at $99, and listeners can get 20% off at: https://timeline.com/KELLY → Be Well By Kelly Protein Powder & Essentials | Get $10 off your order with PODCAST10 at https://bewellbykelly.com. → Fatty 15 | Fatty15 is on a mission to replenish your C15 levels and restore your long-term health. You can get an additional 15% off their 90-day subscription Starter Kit by going to https://fatty15.com/KELLY15 and using code KELLY15 at checkout. Timestamps: → 00:00:00 - Introduction → 00:04:25 - From Traditional Psychiatry To Psychedelic Medicine → 00:06:20 - Root Causes Of Mental Health Conditions → 00:07:20 - MDMA Therapy For PTSD → 00:10:20 - Keith's Personal Psilocybin Experience → 00:15:40 - Why Psychedelic Experiences Can Feel Scary → 00:19:00 - Kelly's Personal Trauma Healing Story → 00:24:00 - MDMA, Ketamine & Psilocybin Explained → 00:25:40 - Ketamine Therapy For Depression → 00:27:00 - Why MDMA Works For Trauma → 00:31:40 - Lifestyle, Nutrition & Mental Health → 00:34:30 - Who Is A Good Candidate For Psychedelic Therapy? → 00:39:30 - What Trauma Actually Is → 00:42:10 - How Psychedelics Help Process Trauma → 00:47:50 - The Latest Psychedelic Research → 00:49:50 - Ibogaine, Addiction & Brain Injury Recovery → 00:51:10 - Mystical Experiences & Healing → 00:55:20 - Psychedelics For Personal Growth → 01:00:30 - Hallucinations, Memory & Reality → 01:04:40 - Risks, Integration & Challenging Experiences → 01:09:20 - Finding A Qualified Psychedelic Therapist → 01:12:30 - Psychedelics vs Antidepressants → 01:14:50 - Why DIY Psychedelics Can Be Dangerous → 01:18:30 - Final Thoughts Further Listening: → Why Achievement Never Feels Like Enough | Bill Burnett + Dave Evans Check Out: → Keith Kurlander | https://www.instagram.com/keithkurlander.ma/ → Will Van Derveer | https://www.instagram.com/will.vanderveer.md/ Check Out Kelly: → Instagram → Youtube → Facebook
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Are we prepared for the massive socio-economic divide of the looming quantum computing era?In this deep-dive episode of The Edge of Show, sponsored by Datavault AI, we welcomed Nathaniel Bradley, CEO and co-founder of Datavault AI. A prolific inventor holding over 70 patents , Bradley unpacks the shift from binary computing to quantum light computing, and what it means for human talent, data sovereignty, and security.Discover how Datavault AI is building the ultimate "toll booth" for digital assets. And how they outline their agnostic blockchain framework, which allows corporations to manage, evaluate, and monetize data using NASDAQ-backed systems. Also discover a groundbreaking perspective on robotics: introducing high-definition audio and wireless interoperability to give robots a universal communication layer.If you want to know how blockchain, AI, and quantum keys are turning data from a cost center into a massive revenue generator, this episode is a must-watch.Support us through our Sponsors! ☕ Want to make content like ours? Sign up with Castmagic to make your creative process easy: https://bit.ly/CastmagicReferral Work smarter, grow faster. Automate your SEO, get AI insights, and manage all your clients in one place with Helm. Start today 50% off your first month at helmseo.com
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