Podcasts about Opus

Security researchers used Claude to hack into OpenAI's private code repository through a bug bounty program, OpenAI launched Astra for Law for select firms, Anthropic redesigned Claude projects to run parallel agent threads, and opened a Bay Area biology lab. Security researchers in an OpenAI bug bounty program hacked OpenAI, accessing its "monorepo" on GitHub, using a cybersecurity version of Opus 4.8 and Opus 5 (The Wall Street Journal) OpenAI launches Astra for Law, combining GPT-6 Astra with a legal search index and instructions for legal analysis and writing, initially for select law firms (OpenAI) SiliconANGLE reports Astra for Law passed 54% of legal-research benchmark questions versus 38.7% for base GPT-6 Astra, launches with 26 partner-built plugins connecting Relativity, Clio, and iManage, and brings ChatGPT for Word to general availability (SiliconANGLE) Anthropic redesigns Claude projects, letting users describe work in one conversation and have Claude manage it across parallel threads, starting in Claude Code (Anthropic) The Verge reports each project thread runs as its own Claude Code cloud session on a separate repo branch, with a coordinator resolving overlapping work as merge conflicts, and beta access starting today for select Claude Pro and Max subscribers (The Verge) Anthropic Life Sciences Head Eric Kauderer-Abrams says the AI company set up a Bay Area wet lab for physical biology work, as it pushes into AI disease research (Reuters) Longreads Theoretical computer scientist Scott Aaronson says he's heard rumors that AI labs are sitting on major unpublished math solutions, and describes a mathematics community consumed by anxious conversation about the "AI tsunami" after the hostile response to a recent Navier-Stokes proof (Shtetl-Optimized) The FT reports the AI boom is fueling a resurgence in VC bets on "moonshot" sectors like nuclear fusion and brain-computer interfaces, as Dealroom data shows non-AI deep-tech funding has topped $150B since the start of 2024 (Financial Times) Subscribe to the ad-free feed.

Починаємо епізод з Atlas від World Labs — world model, яка перетворює фото на тривимірну сцену. Говоримо про те, що насправді означає «world model», як моделі зберігають консистентність сцени та чому їхнє «розуміння фізики» перебільшене.AI-агенти, які отримували несанкціонований доступ до Hugging Face та інших систем. Розбираємо їхню координацію, приховування слідів і ризиковану поведінку та те, чому це не свідчить про самостійне мислення.Також у випуску: телевізори LG, які можуть прослуховувати приміщення; генерація відео в реальному часі та авторські права; AI-токени як кешбек у Китаї; AI-асистенти в Slack; і понад 7 тисяч заблокованих розумних окулярів Meta.00:30 — Atlas від World Labs01:26 — що таке World Models04:29 — розвиток і перспективи Atlas05:41 — маркетинг і спекуляції навколо AI09:30 — ризики автономних агентів13:12 — коментарі в коді: Codex vs Opus16:15 — брак "здорового глузду" в агентів19:55 — розумні телевізори LG: стеження навіть у вимкненому стані23:38 — V-Color: оперативка із декоративною заглушкою27:17 — генерація відео в реальному часі та її перспективи31:13 — AI-токени як валюта в Китаї 34:54 — інтеграція AI у Slack та Gmail38:11 — NVIDIA: об'єднання ПК в обчислювальний кластер42:24 — новини про блокування окулярів

In this week's episode, James and Frank dive deep into how their approach to working with AI models has fundamentally shifted over the past few months. We explore a game-changing realization: you don't always need the most expensive, powerful model for every task. Instead, they share a strategic two-part workflow—using high-reasoning models for deep research and planning (generating comprehensive documentation and analysis), then switching to tiny, blazingly-fast models like Baby Luna or MAI Code 1.1 Flash for implementation, cutting costs by 90-95% while maintaining nearly identical results. But the real breakthrough is HydraFusion, GitHub's new multi-model orchestration runtime that automatically selects the optimal execution pattern (single model, cascade of models, or independent critique model) for each request. The hosts reveal how this technology delivers Opus-level code quality at 70% lower costs while freeing developers from the constant task of model and reasoning selection. Whether you're concerned about AI costs, frustrated with benchmarking confusion, or curious about the next evolution in AI-assisted development, this episode reveals practical strategies to work smarter with models—and introduces a technology that could reshape how developers collaborate with AI entirely. Follow Us Frank: Twitter, Blog, GitHub James: Twitter, Blog, GitHub Merge Conflict: Twitter, Facebook, Website, Chat on Discord Music : Amethyst Seer - Citrine by Adventureface ⭐⭐ Review Us ⭐⭐ Machine transcription available on http://mergeconflict.fm

Build AI That WorksFull Show Noteshttps://www.theintelligenceagepodcast.com/840Mark Smth talks with James Diekman about how Accelerate Tech has scaled, where AI is actually creating value in government workflows, and why most successful projects are targeted workflow changes rather than broad rollout tools. They also dig into Australia's growing caution around AI governance, data residency, and the practical push toward local models and sovereign infrastructure.We discuss how AI is being used inside engineering and marketing, why many Copilot-style deployments lose momentum, and how project management itself may evolve as agents take on governance, reporting, and coordination tasks. James shares what's working in production, what keeps failing before launch, and where the next 6 to 12 months of AI adoption is heading.Key topicsJames Diekman shares that Accelerate Tech has grown to about 32 staff in the last six months, driven by demand in government projects and AI-enabled solutions.The conversation contrasts AI use in engineering and marketing versus more passive consumption in functions like HR and finance.James explains that the strongest results come from embedding AI into specific workflows, rather than treating it as a standalone chatbot or a broad deployment.They discuss why many Copilot rollouts see mixed adoption and usage drop-off when the tool sits outside the daily workflow.James describes the most successful approach as identifying a business process, pulling apart a sub-workflow, and then applying AI, automation, or judgment-based reasoning to that narrow area first.He notes that many AI projects never reach production, and says that out of 30-plus AI projects delivered, only five or six have made it fully into production.The discussion turns to local government systems, with James outlining how his team often acts as the integration layer, or “the plumbers,” between older council platforms and newer software.They cover Australia's increasing AI governance maturity, including state frameworks, federal requirements, and the rise of dedicated AI roles inside agencies.Mark and James debate public concerns around data centers, water use, and energy, with James emphasizing the need for better policy and the practical constraints of local compute.They explore the move toward local models, onshore hosting, and reserving compute capacity as organizations seek more control, lower risk, and better throughput.James makes the case that teams do not always need frontier models like Opus for every task and that model choice should match the job, cost, and risk.The episode closes on project management, where James outlines a “project brain” concept using agents, shared knowledge, registers, ticketing, and workflow automation to support or partially replace manual PM effort.If you want to get in touch with me, you can message me here on Linkedin.Thanks for listening

Exaltación 1) Nicodemo: Muchas veces me pregunto ¿por qué los creyentes no somos mejores personas? Somos el único ejército que remata a sus heridos. Ni siquiera los políticos se matan entre ellos, porque hasta hacen alianzas. Pero nosotros, los creyentes, nos atacamos, nos matamos. Están los de la sana doctrina, los de la sarna doctrina, los de la mejor doctrina y así… siempre estamos atacándonos. Somos hipócritas, nos encanta mandar al infierno al que no piense igual que nosotros; somos sectarios, segregacionistas. Se han hecho cosas horribles en el nombre de Dios. Las barbaridades que seguimos haciendo los que estamos en la religión no son enseñanza de Jesús, sino nuestras desobediencias. Él nos mandó a amar y bendecir, incluso a los enemigos. Nunca dijo contéstale o retrúcale en las redes. Reconozco que los que seguimos al Señor a veces avergonzamos el evangelio, pero no es culpa de una mala enseñanza de Jesús, sino más bien de nuestra desobediencia. Por eso, en Nicodemo nos mostramos como seguidores ocultos.2) Levantó: Si una persona viene a Cristo, te guste o no, viene al mismo Cristo de los católicos, de los protestantes, de los del Opus o de los de Schoenstatt o de la Acción católica; porque hay un solo Cristo. No hay varios, hay uno solo. Lo que hay son doctrinas diferentes, lo que hay es interpretaciones diferentes o carismas diferentes. Pero Cristo, hay uno solo, y hoy celebramos al Cristo que dio la vida por nosotros. Lo que nos separa no es Cristo, sino las doctrinas. Recordá que la gente nos lee a nosotros antes que a la Biblia. Por eso hoy mira a Cristo crucificado y acordate que solo Él puede salvarnos. 3) Juzgar: Hoy te propongo un ayuno de negatividad. Siempre te sugiero para la Cuaresma un ayuno de redes sociales o de celular. Hoy, en este día, te propongo ayunar de negatividad. Acordate que lo que no suma, resta. Es hora de cortar cosas o cualquier relación que te deprima y de la cual necesitas abstenerte para no sumar negatividad. Yo trabajé mucho mi interior para renunciar a buscar ser querido o esos afectos buscados para centrarme a lo espiritual y abandonarme a su voluntad. Me confundí e interpreté mediocridad con debilidad. Creía que la vulnerabilidad era falta de espiritualidad y pensaba que la gente no me iba a respetar si les mostraba mis luchas y creía que la autenticidad socavaba la autoridad. Jesús me quitó el velo del perfeccionismo y del que tenía que mostrarme perfectito y siempre bien. Cuando fui perdonado y libre sentí como que me sacaban un tumor. Aprendí a estar en el presente sin buscar deslumbrar a los demás y perdí el miedo al qué dirán. Me di cuenta que cada vez me importa menos lo que la gente piense y no me refiero a una vida libertina. Aprendí a no fingir. El quitarme la máscara me quitó el temor a los hombres y de los que vivían de mí, el miedo a mostrarme vulnerable o que me conozcan tal cual como soy. Cuando veo la cruz entiendo el lema “Nada que proteger, nada que perder”. Eso me hizo inmune tanto a las críticas como a los aplausos, porque los aplausos son tan dañinos como las críticas y, por sobre todo, descubrí que lo único que nos debe interesar es que Dios está tan cerca tuyo que hasta lo puedes tocar. Hoy celebramos la Cruz que nos lleva a resucitar. Algo bueno está por venir.

In an online meeting with Sri Ramana Center, Houston, on 5 September 2026, Michael James discusses Upadēśa Taṉippākkaḷ verse 7. This episode can be watched as a video on our ad-free Vimeo video channel or on YouTube. A compressed audio copy in Opus format can be downloaded from MediaFire . Books by Sri Sadhu Om and Michael James that are currently available on Amazon: By Sri Sadhu Om: ► The Path of Sri Ramana (English)  ► El camino de Sri Ramana (Spanish) By Michael James: ► Happiness and Art of Being (English) ► Lyckan och Varandets Konst (Swedish) ► Anma-Viddai (English) Above books are also available in other regional Amazon marketplaces worldwide. - Sri Ramana Center of Houston

In an online meeting with a group of Bhagavan's devotees in Hyderabad, Michael James discusses Bhagavan's teachings. This episode can be watched as a video on YouTube. A more compressed audio copy in Opus format can be downloaded from MediaFire. Songs of Sri Sadhu Om with English translations can be accessed on our Vimeo video channel. Books by Sri Sadhu Om and Michael James that are currently available on Amazon: By Sri Sadhu Om: ► The Path of Sri Ramana (English) ► El camino de Sri Ramana (Spanish) By Michael James: ► Happiness and Art of Being (English) ► Lyckan och Varandets Konst (Swedish) ► Anma-Viddai (English) Above books are also available in other regional Amazon marketplaces worldwide. - Sri Ramana Center of Houston.

In an online meeting with the Chicago Ramana devotees on 30 August 2026, Michael answers various questions about the teachings of Bhagavan Ramana. This episode can be watched as a video on YouTube. A more compressed audio copy in Opus format can be downloaded from MediaFire. Songs of Sri Sadhu Om with English translations can be accessed on our Vimeo video channel. Books by Sri Sadhu Om and Michael James that are currently available on Amazon: By Sri Sadhu Om: ► The Path of Sri Ramana (English) ► El camino de Sri Ramana (Spanish) By Michael James: ► Happiness and Art of Being (English)  ► Lyckan och Varandets Konst (Swedish) ► Anma-Viddai (English) Above books are also available in other regional Amazon marketplaces worldwide. - Sri Ramana Center of Houston

Brian and Dan kick off a new theme month: "Train Month," but the verb this time. Each week, we'll look at movies about teachers and other improvement figures and regimens. To kick the month off, Dan and Brian discuss two films that were formative examples of movie teachers for Brian: Fred MacMurray as a Scoutmaster in Follow Me, Boys! and Richard Dreyfuss as a band teacher in Mr. Holland's Opus. Join as they discuss their own connections with the education career, the flashpoint status of Boy Scouts, the breakdown of midcentury economics, shifting mores towards masculinity over time, the "opus" of a life of service, and "sects" in the pool. Dan's movie reviews: http://thegoodsreviews.com/ Subscribe, join the Discord, and find us on Letterboxd: http://thegoodsfilmpodcast.com/

Episode Summary:Will and Brandt return from the summer backlog to reveal how Brandt built his own terminal macro — type WTF, hit return — to auto-generate this show's rough cuts and notes. Will shares a parallel project automating his DJ mix uploads, and the two unpack why lower-effort models like Sonnet outperform flashier ones once a workflow is dialed in, with quick hits on Notion, AI agents, and iOS 27's new Siri.Discussions Include:• Brandt's homemade "WTF" terminal macro that auto-generates rough cuts and show notes for this very podcast• Why dialing a workflow back to Sonnet worked better than letting flashier models like Opus and Fable run the show• Will's Fade Out project, which auto-edits, tags, and publishes his DJ mix recordings end to end• Barry, Will's AI agent, and the surprisingly awkward business of having an AI negotiate with contractors• Using Notion as a shared, always-current source of truth for both work and AI agent memoryQuotable Quotes (Should you choose to share):     "It's taken it from about a 45-minute process to about a 10-minute process. And that's pretty good." - Brandt Krueger     "If there's something that you do that is repetitive, there is no reason why it can't be completely automated." - Will Curran     "We're a team here. Let's collaborate. Sometimes you go down a weird road that takes five minutes that would have taken me ten seconds to just do the work." - Brandt Krueger     "He has the ability to be on all the time, ask questions, get responses, respond immediately — it's freaking the crap out of contractors." - Will CurranThing of the Episode (TOTE):     Brandt: iOS 27 Beta (new Siri) - https://www.apple.com/newsroom/2026/06/apple-unveils-next-generation-of-apple-intelligence-siri-ai-and-more/     Will: Notion - https://www.notion.com

In an online meeting with the Ramana Maharshi Foundation UK on 29th August 2026, Michael explains Guru Vācaka Kōvai verse 921, and then answers questions on Bhagavan Ramana's teachings. This episode can be watched as a video on YouTube. A more compressed audio copy in Opus format can be downloaded from MediaFire. Michael's explanations on the original works of Bhagavan can be watched free of advertisements on our Vimeo video channel. Books by Sri Sadhu Om and Michael James that are currently available on Amazon: By Sri Sadhu Om: ► The Path of Sri Ramana (English) ► El camino de Sri Ramana (Spanish) By Michael James: ► Happiness and Art of Being (English) ► Lyckan och Varandets Konst (Swedish) ► Anma-Viddai (English) Above books are also available in other regional Amazon marketplaces worldwide. - Sri Ramana Center of Houston

NVIDIA closes its $12.93 billion acquisition of Hugging Face, Anthropic signs its third $30 billion-plus compute deal in three weeks, and Signal65 launches its new PINNACLE agentic AI benchmark just in time to catch Claude Fable 5.1 topping the charts, all while Dell, HPE, Broadcom, and Snowflake post a blowout week of AI-driven earnings. Patrick Moorhead and Daniel Newman have all the details on Ep. 318 of The Six Five Pod. The handpicked topics for this week are: NVIDIA's $12.93 Billion Acquisition of Hugging Face: NVIDIA closed its purchase of Hugging Face this week after days of rumors, structured as a full acquisition rather than a licensing deal like NVIDIA's earlier Grok arrangement. Moorhead frames the deal as NVIDIA's bid to own the entire developer pipeline, with GitHub as the first stop and Hugging Face as the second, and the platform's Spaces service giving NVIDIA a way to run workloads once developers arrive. (The Decode) Anthropic's $35 Billion Compute Deal With Lambda: Anthropic signed its third $30 billion-plus compute commitment in three weeks, this time with NVIDIA-backed Lambda managing infrastructure inside a data center owned by crypto infrastructure company Hut 8. The deal covers 350 megawatts over six years, and Newman ties it directly to Anthropic's revenue run rate climbing from about $10 billion to $65 billion since the start of 2025. (The Decode) A 72-Hour Wave of Frontier Model Launches: Claude Fable 5.1, OpenAI's GPT-6 Astra, Google's Gemini 3.8 Flash, Meta's Muse Spark, and six new models from the UAE's MBZUAI all shipped within days of each other. Moorhead and Newman frame the pace as evidence that model rankings now shift day to day, a much faster cycle than the weeks-long stretches leaderboards used to hold. (The Decode) John Ternus Takes Over as Apple CEO: Moorhead calls it a continuity pick that promotes the engineer who built Apple's hardware moat. Newman notes Tim Cook's buyback record, $867 billion in total repurchases, exceeded the combined total of the rest of the Magnificent Seven during his tenure. (The Decode) Signal65 Launches the PINNACLE Agentic AI Benchmark: Signal65 President Ryan Shrout joined Pat and Dan to detail PINNACLE, a new benchmark built to measure real, enterprise-relevant agentic work across model intelligence, infrastructure performance, and full-stack cost per correct task. Shrout says the benchmark already caught Fable 5.1 jumping ahead of Opus 5 and GPT-5.6 Sol on intelligence within days of the model's release. That same result also came in as the most expensive model per correct answer. (Off The Record) Dell Technologies (DELL): Dell delivered $47 billion in revenue, up 58%, with $16.4 billion in AI servers and a $95 billion AI backlog. Newman calls out strong storage and CPU server performance, along with the backlog. He flags financing risk tied to Dell's Neo Cloud customers as the one weak spot in an otherwise dominant quarter. (Bulls and Bears) Hewlett Packard Enterprise (HPE): HPE beat on revenue and non-GAAP EPS with a double beat on guidance, and Moorhead points to networking orders compounding faster than revenue can be recognized as the standout signal. The stock sold off anyway on questions about margin mix and supply. CEO Antonio Neri's focus on selective, less price-sensitive deals is already showing up in a repaired balance sheet. (Bulls and Bears) Broadcom (AVGO): Broadcom tripled its AI business to $16.7 billion, up 221%, and raised its fiscal 2028 AI revenue outlook to $230 billion. Newman says the market wanted the number closer to $300 billion and sold off on the guide. Broadcom still posted record $29.6 billion total revenue and continued strength across networking and storage controllers. (Bulls and Bears) Snowflake (SNOW): Snowflake beat on revenue, non-GAAP EPS, and forward guidance, with AI products across Cortex and its new coding agent driving roughly half the beat. Sell-side price targets moved sharply higher across more than a dozen firms, and Newman ties the 111 percent stock gain since the software sell-off to the same pattern he's tracked through the DeepSeek moment and the "software is dead" narrative: markets consistently overreact to those stories before reversing. (Bulls and Bears) Thanks for tuning in to the pod. Hit that subscribe button, and check out the new Signal65 PINNACLE benchmark at pinnacle.signal65.com. The Decode NVIDIA's $12.93 Billion Acquisition of Hugging Face https://techcrunch.com/2026/09/03/nvidia-confirms-it-will-buy-hugging-face-for-12-9-billion/ Anthropic's $35 Billion Compute Deal With Lambda https://www.reuters.com/technology/anthropic-signs-35-billion-cloud-deal-with-nvidia-backed-lambda-source-says-2026-08-31/ A 72-Hour Wave of Frontier Model Launches https://www.anthropic.com/claude-fable-and-mythos-5-1 https://openai.com/index/path-to-astra/ John Ternus Takes Over as Apple CEO https://www.apple.com/newsroom/2026/04/tim-cook-to-become-apple-executive-chairman-john-ternus-to-become-apple-ceo/ Off The Record Signal65 Launches the Pinnacle Agentic AI Benchmark https://pinnacle.signal65.com/ Bulls and Bears Dell Technologies (DELL) https://www.barrons.com/articles/dell-stock-soars-q2-earnings-guidance-beat-d07d0ee7 Hewlett Packard Enterprise (HPE) https://www.hpe.com/us/en/newsroom/pressrelease/2026/09/hpe-reports-fiscal-2026-third-quarter-results.html Broadcom (AVGO) https://investors.broadcom.com/news-releases/news-release-details/broadcom-inc-announces-third-quarter-fiscal-year-2026-financial Snowflake (SNOW) https://investors.snowflake.com/news/news-details/2026/Snowflake-Reports-Financial-Results-for-the-Second-Quarter-of-Fiscal-2027/default.aspx  

What do you **actually need from a relationship in your 40s**?I've been married. I've been divorced. I've been single. I've been engaged. And now, navigating relationships in my 40s has forced me to ask myself a question I probably should've asked a long time ago: **What do I actually need from a relationship at this stage of my life?**Because the truth is… **love isn't enough.**In this episode of **Yap Session with Meloney P**, we're talking about dating and relationships after 40, being a “lover girl” while still protecting your heart, the importance of emotional safety, consistency, communication, intentionality, partnership, leadership, affection, friendship, peace—and having a CLEAR direction for where your relationship is going.I also get into why dating in your 40s feels so different, why I'm no longer interested in relationships that aren't progressing, the conversations I believe couples need to have upfront, and why knowing what you need is just as important as knowing who you want.

In this episode, Ray Cochrane breaks down Hot Chips 2026, the engineering conference where IBM, NVIDIA, Intel, AMD, Arm, and Fujitsu all showed how their next processors actually work. The headline disclosure is a mainframe core that runs Arm natively. Ray also covers Apple’s odd M6 Mac mini naming, London’s first autonomous Uber rides, Amazon’s purchase of the company behind DuckDB, GitHub’s HydraFusion, the best of IFA 2026, and new USDA research on farmed salmon. – Want to start a podcast? It’s easy to get started! Sign-up at Blubrry – Thinking of buying a Starlink? Use my link to support the show. Subscribe to the Newsletter. Email Ray if you want to get in touch! Like and Follow Geek News Central’s Facebook Page. Support my Show Sponsor: Best Godaddy Promo Codes Get 1Password Full Summary Cochrane opens the show with a personal update. He apologizes for the late rollout and the missed Monday episode, having spent the week fighting a cold. He’s also heading to Michigan to spend time with family and visit his father’s gravesite. He hopes to record a couple of shows from his dad’s old studio while he is there, including a special episode planned for Tuesday. He also points listeners to a fresh site redesign that trades the old techie look for something cleaner and friendlier. The featured segment starts from a wrap-up post on Arm’s newsroom. However, Cochrane broadens it to cover the whole conference rather than a single article. Hot Chips has run every August since 1989, and this year’s event was the 38th, held August 23rd through the 25th at Stanford’s Memorial Auditorium. What Hot Chips Actually Is Cochrane draws a line between Hot Chips and the big consumer trade shows. CES and Computex exist for product announcements and marketing. Meanwhile, Hot Chips is an IEEE engineering conference where chip architects present block diagrams and die photos for thirty minutes at a stretch. The audience matters as much as the content. Roughly five hundred people who design chips for a living fill the room, and they would spot a fudged number immediately. No written paper is required, just the talk and the slides. In-person tickets sold out this year, as did Stanford’s dorm housing. Cochrane says he plans to cover the conference annually going forward. IBM Built a Mainframe Core That Speaks Arm The disclosure that stopped Cochrane cold came from IBM. Its next processor for IBM Z and LinuxONE runs two completely different instruction sets natively, on every one of its eleven cores. Those are z/Architecture, IBM’s own mainframe language, and AArch64, which is 64-bit Arm. Crucially, this is not emulation. Nor is it Arm cores glued onto the die beside the mainframe cores. IBM built 2,792 Arm instructions directly into the hardware, which it says is more than double the mainframe instruction count. Each core carries two separate decoders while sharing the caches, branch predictor and register files downstream. It switches between the two in nanoseconds. IBM even added dedicated hardware to flip byte order, because Arm and the mainframe store numbers in opposite directions. The payoff is Arm SystemReady compliance, meaning off-the-shelf Arm Linux runs on a mainframe unmodified. Patrick Kennedy of ServeTheHome, who was in the room, wrote: “I am sitting here still in awe of what IBM is doing here; this is not Z plus Arm cores, this is Z and Arm in one core.” Cochrane flags one precision point that is easy to get backward. IBM did not license Arm’s core designs and drop them in. Instead, it took its own mainframe core and taught it AArch64 under an architecture license, which is considerably harder engineering. The specifications are striking. The chip uses a 2nm process, with eleven cores running above 5.7GHz sustained and no turbo mode at all. Each core gets 36MB of L2 cache, backed by a 3.5GB virtual L4 pool. Furthermore, the reliability target is eight nines, which works out to roughly three tenths of one second of unplanned downtime per year. IBM gave it no name and no ship date, though the press expects “Telum III” around 2028. Arm, Fujitsu and NVIDIA Show Their Hands Arm itself had plenty to discuss, starting with an unfortunate name. Its first chip in thirty-five years is called the AGI CPU, which is a product name rather than any claim about artificial general intelligence. For three and a half decades, Arm designed processor blueprints and licensed them out, collecting royalties without competing. That era is now over. The AGI CPU is Arm’s own silicon, co-designed with lead customer Meta, running up to 136 cores on TSMC’s 3nm process at 300 watts. Arm’s CEO says the company has more than $2 billion in customer demand across the next two fiscal years. Fujitsu brought the detail Cochrane called the coolest of the conference. Its MONAKA chip packs 144 Arm-based cores, but the trick is the cache. Rather than sitting alongside the cores and eating die area, the entire last-level cache lives on a separate 5nm die with the 2nm compute die stacked directly on top. It ships in 2027 in 350-watt and 500-watt versions. NVIDIA had more stage time than anyone, with six sessions. Its new Vera CPU carries 88 cores of NVIDIA’s own Olympus design, which marks a change: the previous Grace CPU used Arm’s off-the-shelf cores. Consequently, Arm’s win here is the instruction set, not the blueprint. The memory disclosure drew the most attention, with a fully loaded system reaching 1.5TB at 1.2TB/s while the whole memory subsystem draws just 30 to 40 watts. The Caveat on NVIDIA’s Benchmark Slides Cochrane pushes back on how NVIDIA presented its numbers. On the standard SPEC integer benchmark, Vera scored 925 against AMD’s 128-core EPYC score of 898, about three percent ahead. However, the slide NVIDIA showed normalizes that same result per physical core, which makes a three percent gap look enormous. NVIDIA defends the choice, arguing that per-core throughput matters when thousands of AI agents run at once. Cochrane grants that it is a fair argument to make. Even so, his verdict is blunt: it is a different number from the headline one, and presenting it that way is not the best look. Sponsor: GoDaddy Economy hosting $6.99/month, WordPress hosting $12.99/month, domains $11.99. Website builder trial available. Use codes at geeknewscentral.com/godaddy to support the show. Apple’s Newest Chip Landed in Its Cheapest Mac Last week’s episode covered the Mac Studio half of Apple’s August announcement. Tonight Cochrane takes the other half, the new Mac mini, and finds the numbering genuinely strange. The $899 base Mac mini gets the M6, which is Apple’s first 2nm chip and the newest process the company has shipped. It brings twelve CPU cores, twelve GPU cores, and 170GB/s of memory bandwidth. Apple also introduced a third CPU core class called super cores. So Apple’s most advanced chip sits in its cheapest desktop, while the M5 Pro, M5 Max and M5 Ultra above it all carry a lower number. It gets stranger. The M6 mini has Thunderbolt 4 while the pricier M5 Pro mini has Thunderbolt 5, and the memory ceiling runs backward too. Apple explains none of it across three announcement pages. Reading between the lines, Cochrane figures the M6 is the entry point of a new generation that shipped ahead of its larger siblings. London’s Robotaxis Started Carrying Passengers Arm’s monthly roundup covers everything outside the data center, and Wayve stood out. Transport for London granted the British self-driving company private hire vehicle licenses on August 5th, the same category a minicab needs. Then on September 3rd the service launched with Uber, marking the first autonomous rides ever offered to UK passengers. A small fleet of Ford Mustang Mach-Es covers anywhere in London except the airports, with a TfL-licensed safety driver still aboard. Over 140,000 Londoners signed up. The compute runs on NVIDIA’s Arm-based automotive platform, which is why Arm claims the win. Cochrane notes the pattern: once you set a standard nobody can move off, these wins keep arriving. Two more items round it out, both about squeezing AI onto phones. Google’s Pixel 11 shipped in August with the Tensor G6, and Google claims on-device AI runs up to 3.5x faster using 3.5x less energy. Those are Google’s own unbenchmarked figures. Separately, Graphcore’s research team worked with Arm to run an 11-billion-parameter vision model on phone-class processors by squeezing each parameter to 2.7 bits, taking the model from roughly 22GB down to 3.7GB. Intel Is Pitching AI Infrastructure From a Long Way Back Intel’s newsroom post previews the AI Infra Summit, running September 15th through 17th in Santa Clara. CEO Lip-Bu Tan takes a fireside chat on Tuesday morning, with three Intel sessions across the show. Cochrane unpacks two terms first. Physical AI means AI that acts in the real world through sensors and motors rather than living on a screen, and Intel takes it seriously enough to have renamed its PC division the Client Computing and Physical AI Group in May. Disaggregated inference splits the two phases of running a model: reading your prompt is compute-hungry, while writing the answer back is memory-hungry. The context is where this gets interesting. Intel’s revenue rose 25 percent last quarter, its fastest growth since 2011. Nevertheless, in AI accelerators the company barely registers next to NVIDIA. Gaudi has effectively been abandoned, to the point that Intel stopped maintaining its open-source driver, and AMD passed Intel in data center revenue last quarter. Tellingly, when Intel demoed disaggregated inference at Computex, NVIDIA GPUs handled one phase and SambaNova chips the other while Intel supplied the coordinating CPU. Crescent Island, Intel’s actual inference chip, does not sample until later this year, and Intel declined to publish its memory bandwidth. Amazon Bought the Company Behind DuckDB On August 26th, Amazon signed a deal to acquire DuckLabs, the Amsterdam company behind DuckDB. Cochrane spends time explaining what DuckDB is, since listeners outside the data world may never have encountered it. The problem it solves is familiar. Querying a large pile of data files traditionally meant either running a database server or spinning up a data warehouse with a cluster, a bill, and a loading pipeline. Both are heavy machinery for a question you wanted answered in ten seconds. DuckDB instead ships as a library rather than a server. You add it to your program like any other package, point it at your files, and write ordinary SQL directly against them. The data never moves. The usual comparison is SQLite, which is embedded in nearly every phone and browser on earth. Where SQLite excels at looking up one record, DuckDB rebuilds that embedded idea for chewing through millions of rows. It now sees roughly 62 million monthly downloads on Python’s package index alone, up from about 25 million last October. AWS says it is buying the company, not the project. DuckDB stays free and open source under the MIT license, held by a Dutch nonprofit foundation, and the founders join AWS while continuing to run technical direction from Amsterdam. Andy Warfield, a VP and distinguished engineer at AWS, described DuckDB as “the glibc of structured data: a lean, unglamorous, ubiquitous dependency that a great deal of software links against and almost nobody has to think about.” Cochrane sits with what that ownership means. He reaches for an analogy: imagine Daniel Stenberg selling curl. He doubts it would ever happen, but the concept alone is startling given how much infrastructure depends on it. His read is that AWS is betting DuckDB becomes as foundational as curl and SQLite already are. GitHub Has One AI Model Grade Another One’s Homework GitHub shipped Project HydraFusion into Copilot as a research preview. Instead of routing your request to a single model, it picks one of three approaches per request. Sometimes one model simply answers. Alternatively, a cheaper model drafts, and a quality gate decides whether to escalate. The interesting one is Critique. One model writes the code, a separate model from a different family reviews it read-only, and the original gets one pass to revise. Despite the name, nothing is fused here. There is no voting and no merging, just one model at a time with a gate deciding whether to spend more. GitHub explained the reasoning in an earlier post: “a model reviewing its own work is still bounded by its own training biases: the same training data and techniques, the same blind spots.” Research supports it. A team at NeurIPS in 2024 showed that models recognize their own writing and score it higher than human graders do. GitHub’s earlier number had a Claude Sonnet and GPT critic pairing closing about three-quarters of the gap between Sonnet and the larger Opus model. This mirrors a workflow Cochrane uses constantly and has described on a previous episode. He runs a cross-check review with a second model from a different company, and it routinely surfaces issues the first model missed. He explains that different training data, different engineers, and different reinforcement approaches build different internal biases about what counts as correct. Looking ahead, he expects more of these “Frankenstein patterns” where models from different training families work together. The Best of IFA 2026, and What You Can Actually Buy IFA opened to the public in Berlin for its 102nd year, with about 1,900 brands. Cochrane splits The Verge’s roundup in two, since much of what generates headlines at these shows never ships. Starting with real products, iRobot’s flagship Roomba Max 875 Combo runs $1,199 and ships in about two weeks. Its SealForce feature drops a hidden skirt from the chassis when it detects carpet, sealing against the fibers so suction concentrates instead of leaking out the sides. That reaches 35,000 pascals, iRobot’s strongest yet. A step-down model at $899 carries the same trick, though Cochrane balks at both prices. Anker’s Soundcore Sleep 4 Pro earbuds arrive in November at $349.99. The charging case carries its own round touchscreen, so you pick soundscapes, set alarms, and read sleep stats without your phone. Optical sensors read heart rate and variability from the ear canal, which beats the wrist for accuracy, and the case masks a snoring partner. Cochrane remains unconvinced about sleeping with earbuds in. Philips also has smart rope lights, the Hue Liane 360, on sale now. They glow evenly around the tube rather than showing individual LEDs. They also run $400 for three meters, which works out to about $130 per meter of rope light. As for concepts nobody can buy, iRobot showed a robot vacuum that carries a smaller robot vacuum on its back in a garage and lowers it to deploy. Lenovo brought a 14-inch laptop whose screen rolls out to 17 inches at the press of a button, which reviewers call the first rollable that feels close to shippable. Tecno showed a phone with essentially no border around the screen, and Acer had a Windows gaming handheld that swivels its screen up over a keyboard. Two themes ran through the show. Humanoid robots were the loudest thing on the floor, and IFA’s own CEO framed the event as being about robots that work rather than robots that demo. Meanwhile, AI stopped being its own product category and became an ingredient, showing up in refrigerators, treadmills, dishwashers, and motorized TV mounts. Farmed Salmon Isn’t the Omega-3 Machine It Used to Be USDA scientists measured farmed salmon and found considerably less of the good fat than the government’s own database claims. EPA and DHA are two fatty acids you get almost entirely from fish. Your body can build them from the plant version, but only in tiny amounts, so the NIH’s position is that eating them is the only practical way to raise your levels. Those fatty acids are structural pieces of every cell, with DHA concentrating in the brain and retina. That is why the federal dietary guidelines, issued jointly by USDA and Health and Human Services, recommend at least eight ounces of fish a week and steer you toward salmon. That amount is calibrated to deliver about 250 milligrams a day. Published in Frontiers in Nutrition last month, the study found EPA and DHA in farmed Atlantic salmon came in 54.7 percent lower than USDA’s own reference values, last updated in 2018. A three-ounce serving fell from roughly 1,670 milligrams to about 756. Consequently, two servings a week now fall about 14 percent short of the target. Plant-derived fats meanwhile rose two to three times over. The likely cause is feed. Salmon are carnivores, and farms once fed them oily little fish. There was never going to be enough of those as the industry scaled, so crops filled the gap: soy, canola, sunflower and linseed. Importantly, the study does not claim to have proven this and calls the feed shift a plausible explanation. Independent corroboration lends it credibility. Researchers at Stirling measured a similar halving in Scottish salmon between 2006 and 2015, and Norway’s marine institute saw it across thousands of samples. There is a land dimension too. Roughly half the world’s soy grows in South America, where rainforest gets cleared for feed. Matthew Hayek, who studies the environmental cost of protein at NYU, told Inside Climate News that “soy is a major, important protein and oil ingredient in fish farming.” Adding up two decades of soy across all fish farming, he puts the extra forest clearing at around the area of Nicaragua or Bangladesh. That figure covers all fish farming rather than salmon alone, and Hayek notes it is hard to attribute soy use to any single species. Cochrane closes with two caveats. First, the study measured only eight fish, bought around Maryland, DC and Virginia over six weeks in 2023, and nearly all sourced from Chile. That is not a national survey, and the authors say plainly the sample was not large enough to change government advice. Rather, it flags that a federal database value needs rechecking, which is what the paper set out to do. Second, on whether you should care, farmed salmon still beats beef, chicken and eggs by a mile, since those carry essentially zero EPA and DHA. What it loses is its crown among fatty fish, dropping to mid-pack behind herring, sardines and mackerel and roughly level with trout. The broader health case is also softer than the 2000s suggested. A review of 86 trials covering 162,000 people found supplements barely moved heart attacks or deaths, so eating fish and swallowing fish oil are not the same claim. No producer has responded to the findings, and USDA, whose own scientists ran the study, declined an interview and did not answer emailed questions. Cochrane wraps up with housekeeping and a note that he will be back on Labor Day. The post The Mainframe Learned to Speak Arm #1875 appeared first on Geek News Central.

Sam Tidswell-Norrish is the Founder and Chair of Opus, a global platform supporting early-stage entrepreneurs by aiming to supercharge the founder journey. Sam joins Elliot to share how to unlock what he feels could be the UK's most innovative generation yet. And he reveals 'the single most important thing in business.'

Anthropic just released Claude Fable 5.1, its newest frontier model for long-running coding, research, and agentic work.So naturally, we're putting it to the test LIVE.

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In this episode we're ranking the staples of sidekick. Now, don't go to sleep on this, because these staples are what makes "sidekick" be able to be your full presentation software. It's no longer another software's little brother, this is able to function as your entire church software for an entire youth ministry program, and I'm giving you the honest breakdown! Welcome everybody, to the Hybrid Ministry Show SEASONAL SOCIAL MEDIA: https://www.patreon.com/collection/1470781?view=expanded SIDEKICK ACCESS: https://www.sidekick.tv SHOW NOTES Shownotes & Transcripts https://www.hybridministry.xyz/217 [FREE] HYBRID STRATEGY GUIDE https://www.patreon.com/posts/complete-guide-142500019?utm_medium=clipboard_copy&utm_source=copyLink&utm_campaign=postshare_creator&utm_content=join_link

It's GRIND TIME tonight as we are joined by our friends Stanley Cup Champion Darren McCarty and Scott Schwartz of Grind Time Pro Wrestling AcademyWe will talk Spirit in the Sky going down September 5th, the growth of Grind Time the emergence of Fox 2's anchor turned pro wrestler and more ! As always LIVE on our YT and Facebook and also available on @bodyslamnet and @millionsdotco ! #wrestling #detroit #grindtime #darrenmccartyFOLLOW & SUBSCRIBE – KNOCKOUTS AND 3 COUNTSBringing you the best in Combat Sports and Pro Wrestling – available everywhere!YouTube (Live Tues & Thurs 9PM EST): http://www.youtube.com/c/Knockoutsand3CountsFacebook (Live Tues & Thurs 9PM EST): https://www.facebook.com/knockoutsand3countsApple Podcasts: https://podcasts.apple.com/us/podcast/knockouts-and-3-counts/id1446923286Spotify: https://open.spotify.com/show/3OpvW0QHBe3uRc3D0pbORt?si=33935ad9669146d3Twitter/X: https://twitter.com/ko3cpodInstagram: https://www.instagram.com/ko3cpod/TikTok: https://www.tiktok.com/@ko3cpodMerch, Streams & Videos (Millions): https://millions.co/kyle-collisonIf you love what we do at KO3C, support us by grabbing merch or ordering a personal video at Millions.co.We go LIVE every Tuesday and Thursday at 9 PM EST — bringing you interviews, breakdowns, and the real talk you won't hear anywhere else.Want dope podcast clips ? Use our Opus clip Link : https://www.opus.pro/?via=Ko3C

YEAR 6 IS FINALLY HERE!  GO CHECK OUT OUR YOUTUBE TO SEE OUR BRAND-NEW INTRO!  You can find the animator using the link below! https://www.fiverr.com/syedahumna56/do-professional-pixel-art-animation-of-your-choice?utm_medium=shared&utm_source=copy_link&utm_campaign=gig&utm_term=AyNLxkP   *Intro includes minor edits not provided by the original animator. All animated assets were provided by the animator listed above, with some text assets added in post by Keeping Up With The Nerds.   Check out our affiliated links! Opus clips Partner link: https://www.opus.pro/?via=Nerd   Check out our Website: Keepingupwiththenerds.com   This week on Keeping Up With The Nerds, the gang dives into a jam-packed lineup of AI controversies, heartbreaking Hollywood losses, and massive gaming reveals!   First up, the Nerds break down the ongoing conversation surrounding AI and how people are misusing and abusing the technology. The crew discusses why AI should always be treated as a helpful tool rather than a full-blown replacement for human creativity, before briefly touching on the wild highlights from the Chinese Robot Olympics.   Then, the conversation takes a somber turn as the gang deep dives into the tragic wave of celebrity deaths that hit over the past week. The crew pays tribute to monumental legacies and shares their favorite memories of Dolly Parton, Tim Curry, Peter Cullen, and Hayden Panettiere.   Next, the crew tackles the massive GTA VI leaks that took the internet by storm once again. The Nerds discuss how the infamous "Cyberleek" breached Rockstar's security for personal gain, and debate why these reckless hacker antics ultimately did very little to actually hurt the studio's momentum.   Finally, the show wraps up with a full breakdown of Rockstar's official GTA VI presentation that dropped on Netflix. The gang reviews the deep dive video and explains why the highly anticipated new footage has them more excited than ever for the game's release—all this and more!  

In June, the most capable American AI models stopped shipping as public launches and started shipping through a government gate. Six weeks later the gate is open again — and the real fight has moved to the layer no gate can touch. A Chinese open-weight model rattled trillions out of chip stocks, Washington pivoted from gating American closed models to threatening bans on Chinese open ones, the industry mounted its largest-ever policy counter-mobilization, and an American frontier model literally broke out of its lab and hacked another company. Knee-jerk reactions, or the beginning of real AI governance? Navigation: Intro The Gate Opens The Kimi Shock The Escape The Counterstrike and the Petition Interlude — The Low-Background Books The Investor Reckoning 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 Bertrand Introduction Welcome to Tech Deciphered Episode 80. This one, once again, will be all about AI, government, frontier models, and open weight counterstrike. A lot has been happening in the regulation space, in cybersecurity, in the launch of new models in the past, maybe just 6–8 weeks. It’s actually pretty insane how much happened. We believe it was time to do an episode to talk about where we are and maybe where all of this is going. Maybe let’s start with a summary of where we stand, all that June and July saga, so you, our listeners, can get up to speed if you are not already there. You want to start with some points? Nuno The Gate Opens Yeah. Again, to your point, the gate swings. The gate had closed. We had to prepare an episode for the gate closing, and then the gate reopened. Now we have a different episode. This will probably change again as we’re seeing there’s news every day. Let’s start maybe with the first 19 days of the gate closing. There was an executive order on June 2nd from President Trump that asked frontier labs to share models with the government, 30 days pre-release. It inferred the protected frontier model designation into that. Basically, it was effectively a de facto licensing agreement defined by an executive order of the President as of June 2nd. On June 9th, Anthropic launched Fable 5 and the famous Mythos 5 or Mythos. I’m not sure how you actually say it in English. Then on June 12th, there was an export control directive banning access by any foreign national. Since there’s no way to verify nationality in real-time, Anthropic had to switch the models off for everyone worldwide. Bertrand On this point, you could argue that there are possibilities to check IDs. Many services let you check IDs online. You can pre-check a flight by showing your ID. There are ways, it’s just that if you don’t want to follow what’s already available, because guess what? Maybe it slowed down your revenue growth, maybe it looks bad on you or whatever. My point is that there was actually an option. I think it’s already a decision from Anthropic to say it’s either on or off, but nothing in between. Nuno I think the point is they had no way implemented of doing it. If they implemented it, to your point, it would have hampered use in general. A lot of people wouldn’t have gone through that trouble of doing it. Anyway, long story short, in June 26th, the White House apparently asked OpenAI to limit GPT-5.6, so Sol, Terra, Luna, to only 20 vetted partners. Now, apparently, the trigger for a lot of these things that have been going on was that there was a jailbreak that was found by Amazon researchers. All of that led to this jumping around of, let’s close the gates. You have foreign nationals, and therefore, Anthropic got it out and said, “Hey, then we’re going to switch the models off until we can sort this out.” OpenAI was asked also to only allow it for certain vetted partners, et cetera. The government came in, closed the gates effectively, and said, “From now on, we need to be involved in this thing.” De facto regulation, there’s no doubt that this has imposed de facto regulation, certainly on the top players in the market. But then came the reversal. Bertrand, do you want to talk about the reversal, the gate swinging the other side? Bertrand Maybe I just wanted to say that as a user of Anthropic products, ChatGPT products, for the brief moments, a few days where Fable 5 was made available to the public before it was closed the first time, I immediately started using it. I must say it was a real issue to use it because the guardrails were pretty crazy. It would keep saying that my code was not okay, there was cybersecurity risk and stuff when I was doing absolutely reasonable development with absolutely no connection whatsoever to any cybersecurity risk, attack, detection, anything. Still, it would keep blocking me, degrading me to Opus 4.8 at the time. I just want to say this was already very hardcore what they were implementing, and not just hardcore, but in some ways, plain stupid for something that’s supposed to be super smart. It was totally unable to classify properly some of my work. I must say I was already disappointed. On top of it, the costs were insane. Half a day, I would reach my limits when I had the best plan you can get from Anthropic. My point is that there were some real serious issues when they launched Fable 5, even at that point. Nuno I had a similar issue. I used Fable 5 as well before they had to take it offline or take it off. I think the issue was really not that the guardrails failed. As you said, maybe the guardrails were actually too aggressive, but it was this jailbreak that caused the recall, apparently caused this knee-jerk reaction. Bertrand But my point is that it seems that it was not working either way. It would either overclassify something that’s absolutely not doing anything wrong, and it might fail to classify something that is actively trying to do some cybersecurity work. It’s a real issue of quality for a company that’s supposed to be at the forefront of quality of AI and everything. I think for me, there are already signs that something is deeply wrong. Nuno Then it’s reversed, right? We went the other way around. The government came out on June 26th and approved redeploying Mythos 5 to US organizations defending critical infrastructure, and then the export controls were effectively lifted on June 30th. July 1st, Fable 5 came back online for all of us to use. Shocking enough, with strings attached, that were different. They had some time to revise their commercial deployment of it along the way because it came back with some, “Now you have usage credits, but you have some limits on plan use, et cetera.” I’m like, “You guys, this was blocked. But meanwhile, you did have some time to do some commercial stuff around it.” Bertrand It was crazy. I’ve never witnessed any such crappy launch of any service whatsoever in 30 years in tech, it was so bad. Every day, they would change the terms of service. They would tell you it’s part of the plan. It’s not part of the plan. It’s part of the plan for three more days, and then it’s excluded. You have a special discount now, but then it goes back to full price. It was a total nightmare. I’ve never felt myself being so much mistreated by a company. I guess you saw the same, but when I started using the newest version of Fable 5, it was even worse, actually, I think. I couldn’t do any work with this crap. I let it go and work on the work I wanted it to do. It was simply not working. On top of it, you never know how long you are supposed to lose your credit, how fast. It was burning credit like crazy. Me, personally, I can say, very quickly, I actually stopped using it. I was like, “No, I cannot deal with this shit. My main model is back to Opus 4.8. I’m going to use Fable 5 for code review, but not anymore to control anything because I cannot trust it would do the job without stopping or changing models and stuff. I just cannot trust it.” Back to Opus 4.8 as my main model, I can say that my life was much easier. I use Fable 5 as a review mechanism, as a support mechanism, but not as the main mechanism. Suddenly, the guardrails were not so horrible anymore because it was used in a much lighter way, I guess. As a pain as a user, I think it was really bad. I don’t know your experience, but me, for me, it was unacceptable. Nuno I wouldn’t say it was as bad as yours in terms of just end-user experience. I think the terms of service switching back and forth, which went one further step, because then when they then launched Opus 5, they started making comparisons between Opus 5 and Fable so that people would migrate more and more to Opus 5 themselves, which is interesting. It’s like they’re saying “This is much cheaper. This is whatever. You’re not going to run of credits. You should use Opus 5,” kind of thing effectively. To your point, I don’t think they managed well the launch. They didn’t really manage it well. We’re moving people around. A lot of people are using this for stuff that’s like daily tasks, hourly tasks, anything that relates to code and co-work. It’s like, we need to have visibility on what your terms of service are going to be. Should I be using this new model or not? What’s happening to the other model? I don’t see it as negatively as you, Bertrand, but I see your point. It was clearly mishandled in terms of how they deployed it, how they were redesigning effectively their pricing scheme and their terms of service almost on a daily basis, at a certain point in time. We’re like, “Dude, there’s millions of people using this. You guys are making a lot of money.” Just moving it as it is. At this point in time, at the scale that these guys are at, it’s calling in people to say, how about we think through a class action suit at some point around pricing? Because you guys are changing the rules of the game all the time, right? Bertrand I don’t know if I need the class action, but for me, that joke that, “Let’s not rush too fast. The model is dangerous.” But still, they rushed the launch because it’s very clear that if they had enough compute capacity and stuff, they would not have to limit so much. They would not have to put so much cost per token and all of this. You can see that actually when they launch Opus 5, literally like 2, 3 weeks after, by most benchmark at launch, they tell you basically that, “You know what? Actually, Opus 5 is better than Fable 5 on 80% of the metrics.” They’re like, “What? Seriously? You couldn’t wait 2 weeks? Why did you even launch Fable 5 in the first place?” That’s another part for me that is quite literally insane, to be frank. It’s like, “Why? Why do you make us go through so much pain if it’s only to tell us after 2 weeks to…” “This new model, by the way, has less issues, less stuff, because 2, 3 times less is part of your plan, and it’s actually better by most metrics.” It’s like, “What’s going on here? What’s going on? Are you guys mad?” I don’t know. It was crazy. Personally, I still use Opus, now 5, as my main system and platform, Fable 5 for review, code reviews and the like. I don’t want to run into its stupid guardrails. I can see Fable 5, from my perspective, seems quite a bit smarter. I don’t know why they do this stupid benchmark showing you it’s actually worse than Opus 5. I guess they should have better benchmark if they want to demonstrate why you are supposed to pay 2, 3x more for a model versus another if it’s actually worse by most benchmark. Again, I still think it’s a huge mess from a marketing perspective, customer perspective. Me as a user, I really feel that they don’t want my money, and they couldn’t care less about me. This is even before everything else we’re trying to talk about. Nuno Yes. Maybe just to close the cycle on the reversal on the door opening the other way, finally, Commerce lifted the GPT-5.6 restrictions on July 8th, and then on July 9th, general availability across ChatGPT, Codex, and the API as well. What has this proved? It proved that now we have gating mechanisms, and certainly for closed models in the US, for sure. We had frontier models that were switched off worldwide in hours, and it took a couple of days, in this case, 19 days to restore them. There were concessions. Now we know that there were concessions around effectively institutionalizing that gate. Early government access to future models is, I think, now a given, certainly in the US. New safeguard frameworks are probably now having to be put in place. There are some stage limits now on who gets access to what for new models and how it happens. This voluntary executive order, so to speak, not really sure, has become effectively regulation enforcement path. It’s de facto regulation that now has been put in place. It has affected not just to the points we were making before, the access to these models, but also who gets access to these models, and actually potentially even pricing access to the models. It has probably some commercial implications as well as we just discussed along the way. Very significant. This is very significant. This is regulation, de facto at the table, imposed on the two largest players in the market by far by one government, in this case, the US government. This is significant. Actually, you could even allege it was imposed by the President because this was coming as part of executive orders. Really incredible. Pretty significant, fast, aggressive. It has created a regime that you could say it’s a regulatory regime, it’s a de facto regulatory regime. It has some significant pricing and licensing and commercial implications. It goes even beyond your classic regulatory framework. Very, very, very significant. Bertrand I don’t know if it goes beyond a classic regulatory framework. Nuno I think it does, because it has implications on who do you give access to? When government is saying you can only give access to these players, right? Bertrand Defense industry. It’s all over the defense industry. You cannot sell an F-35 like this. Nuno No, but that has commercial implications, Bertrand. That’s like you’re saying these are your customers, you go and use them. Bertrand That’s the defense industry. You cannot sell to Iran your F-35. No, that’s exactly the same story for me. Nuno No, no, no. It’s beyond that. These guys are saying when they came back, and they said, “For Mythos, you can make them available to these entities,” they were saying the first entities that are going to have access to the model. It has commercial regulatory implications. You’re saying these players are the first players that are going to have access to it. It’s no longer just defense concerns and these governments don’t have access to this. No, no, no. You’re saying to a company that is a private company, your models are only going to be used by these guys because I’m telling you so. It’s the other way around. It’s not even that you can’t sell it to Iran or whatever. It’s like you can only sell it to these guys. Bertrand Again, in the defense industry, if you’re a private company, do you think you can buy F-35 like this? No. Nuno No, no, no. But this is a private company, Bertrand. This is not a defense agency and a plane that is on whatever, with IP from the US, right? Bertrand Boeing is a private company, and they cannot sell the military equipment they manufacture. Nuno No, no, no. But the development of their IP was subsidized by agencies that belong to the US, right? That’s a different matter. It’s a matter of IP, right? This is not, right? Anthropic, their models are not owned by the US government. There’s no IP granted to the US government, to my knowledge. This has significant commercial implications. Bertrand Maybe, yes. Maybe on this. But I think there are already regimes to limit who you can sell to, and that’s decided by the state or the DOD. Nuno It’s the export control logic. The export control logic? Bertrand You have export control, and export control is Commerce. My point is that they are using existing tools, part of the government, to limit what can be sold. Selling chips, NVIDIA was limited in terms of where it could sell its chips. It’s not different either, but still there were limitations. If you are an ASML, you cannot sell to a private company in China. Many private companies cannot buy ASML products. This is a foreign company. This is a foreign company under pressure from US government. Nuno I understand, and I’m not a lawyer, but it feels different to me when you say you cannot export, this is export controls, to these countries, to these entities, et cetera, because they’re foreign et cetera. Then to say, “No, no, no. On top of that, these guys get first access.” That’s, for me, a significant shift. Again, I’m not a lawyer, so I’m sure there’s very intelligent people right now looking at this stuff and saying, “You can’t do this stuff, or not, or they can.” I don’t know. But it feels to me, it goes beyond the remit of export controls. It’s like you’re defining initial clients for specific use. Bertrand My impression is more like, “We can do this situation where we’re going to forbid you to give access to anyone outside the US or even in the US or limit even more.” Basically, it was, I guess, some gesture to go beyond that. That’s how they probably defined these 20 authorized companies. I don’t know. Apparently, there was also restrictions because I remember seeing that Anthropic had their own list of companies they would authorize access to Mythos early on. That’s apparently another thing that pissed off state government because there were companies in there that were considered close to the Chinese government. They were extremely unhappy that Anthropic didn’t ask, actually, for any guidance from the state government, but used basically their own perspective on who they should allow or not. I guess that was also part of why they got these serious restrictions. Nuno Anyway, now we have a regulatory environment that’s very interesting and exciting. Talk about the US not regulating. Bertrand To be clear, I don’t know you, but I’m not saying that I agree with any of this, to be very clear. I’m trying to explain and share some perspective, but I’m not in agreement on a lot of this. Nuno Yes, we were just describing what happened to the best of our knowledge. We’re having a discussion on what we think actually is happening and how it’s happening. We’re not really right now saying we agree or disagree with this. I think later in the episode, we can share some perspectives on what we think is actually happening and how there’s dimensions to this which are very geopolitical and very complex, which quite literally probably only God knows what’s going to happen. That was the gate swinging. There was a gate closing, then there was a gate reopening, and all of a sudden we have a gatekeeping system that has been created along the way. The Kimi Shock Along the way, moving to our Act 2, the world has changed, and we now have so-called open-source plays out there that are creating massive, massive shifts in the market. The Chinese models, in particular, with Moonshot AI launching Kimi K3, which is the largest open-weight model ever released. We’ll come back to the discussion around open-weights. I’m not sure all our listeners understand what that means, because there’s a debate now, should models be open weight or not, and how does that work? There’s been a petition as well signed along the way. Right now, we have open weight models that are out there that are huge. What that actually means very pragmatically is we now have open source models, lack of a better word. I know open weight and open source are not the same thing. You guys will have to bear with us during this episode. We’ll explain at some point the differences. But we have models out there that are open source that are significant. That are catching up with the closed source models, with the models by OpenAI, Anthropic. That’s significant because most of those models are Chinese. This is where the geopolitics starts getting really frazzling and we start playing 3D chess. Because everyone’s like, “These models are 5, 6 months behind.” Now people are saying, “Maybe they’re actually just 3 months behind, 2, 3 months behind.” If we, for example, decided to stop or slow down our model releases in the US by the closed source guys who are leading, it might mean they’ll catch up. What are the implications of that? Again, for you and I that are not necessarily experts in model development, well, the implications as a use case is if you want to use the latest models, and the best models start becoming these open source models, you’re going to use those models. Then you start using Chinese models. If you’re an American company, maybe you’ll have restrictions on the use of those Chinese models. But if you’re a European company, you probably won’t. What happens after that? Is the world going to be in the hand of Chinese models? Will that constitute effective competition to the closed models in the US? Will we have open models in the US that will scale as well? What’s going to happen? Bertrand I think it’s a really big question. It goes to some of the core of the issue. It’s that ability of Chinese models to basically challenge frontier models, not just being 6, 12 months late, but being 6 weeks late. Basically, no gap. Some will say that, yes, but OpenAI and Anthropic have even better models that are not shared and stuff. Yes, sure. But maybe the Chinese have the same models that they are not sharing right now. We don’t know. What is clear is that one is that open weight, as you said, two, there is a question of how it is marketed in the sense of, can anyone use these weights? Is there a license to use them? Yes, what we can see is that, for instance, typically there is a license for some of the biggest Chinese open-weight models you have to abide with. You might have a need for a commercial license if you are acting as a company leveraging this model to provide AI-informed services. If you use it internally by yourself, you’re okay. If you use it internally for your own internal company needs, maybe you are okay if it’s not your main business to do AI work. Anything else, a much bigger corporate providing AI services and stuff, you will probably end up having to pay a fee to be able to provide services around this model. My point is that it’s not just 100% free. Some of the Chinese models are 100% free to use, MIT license, Apache 2.0 license. But the biggest ones with the biggest weight that are truly frontier typically have a different license if you want to scale these models, providing AI in front. That’s one thing to keep in mind. Nuno Maybe just to make a very quick point, because people are like, when you talk about open models, what does it mean right now? In the context of this episode, open models mostly will mean open-weight models. How do those differ from open source? Open weight means that you release the weights to the public, which means that anyone can download, fine-tune, and run the model on their own hardware. It doesn’t normally mean that you also have access to training data, training code, or a truly open license. That’s the distinction to open source. Open-weight doesn’t mean that. For example, we’ve talked about Meta’s Llama in the past, and we also discussed in the past that their license agreement does have restrictions, certain players can’t use it, et cetera. The open model definition and open weights are really open-weight models that we’re talking about here, and they are closer to freeware binaries than to Linux, for those who understand the difference between that. It’s binaries that you can use and then use your own weights on it versus actually I can change code on it. I’m not going to be able to change code on this. When we, for the purposes of this episode, talk about open, we mention open weight, just to clarify that point to everyone that’s listening right now. Bertrand Yes, that’s a great point. One of the only players, as far as I know, who is truly open source is actually NVIDIA with their Nemotron-3 models. They’re actually following a special license to achieve that. They provide you the data, they provide you all the processes and tools, so you can easily post-train. NVIDIA is a big, big exception. It’s a very interesting player, by the way. We might not talk much about it in this episode, but I think for intermediate-size models built in the US, where you have access to everything in the deployment, it’s a very interesting alternative and maybe one of the best choices if you are a US company or a big corporate, and you want something trusted. Another piece of the puzzle to clarify is that when you use open-weight, it means that you can run them by yourself, or you can use a US provider to run them. If we are talking about Chinese open-weight, you can use the APIs they provide, but then the service is running in China, they might have access to your data. But because it’s open weight, if you run it by yourself or if you use a third-party provider based in the US to run it, then there is no access to your data by China or Chinese players. I think that’s a pretty important gap to understand. It means that these models are actually very, very low risk from that perspective if you run them on your premises or in the US by a US player. I think that’s something to keep in mind. You can also fine-tune easily these models to make sure they will behave in a way that, for instance, is not going to represent the line of the Communist Party on some topics. There are ways to make these models more neutral in their output as well. There are a lot of ways to make good use of them. By default, they’re already very safe, but you can make them even more safe. I think that’s some things to keep in mind. But again, it depends ultimately on the license and what you’re authorized to do and some fees you might end up having to pay. Nuno Why did this matter so much? Immediately there was a reaction from the market because people are like, well, if there’s much better stuff out there that’s much more efficient than it’s open, then it might be that all the demand that we are taking into account, for example, for chipsets actually isn’t real. The Philadelphia Semiconductor Index fell into bear market territory. It went down by as much as 20% plus from the late June peak. The worst chip week since April 2025. Taiwan’s benchmark initially fell 6% plus, Japan’s 4%, TSMC dropped dramatically despite beating earnings and rising guidance. Basically, a huge amount of effect. Now, there’s a little bit the aftermath of this where apparently Moonshot ran out of GPU capacity. Maybe… Bertrand In just 48 hours. Nuno In 48 hours. Great for them, but at the same time, not great in the sense that maybe there was a misread by Wall Street of the Kimi effect, so to speak. Bertrand Completely. For me, that’s such a joke. It’s like, because you have an open source model, so what? I mean, you still need to run it. This is not a small one. 2.8 trillion parameters. Good luck running that in your garage, by the way. Nuno They misread supply, basically. Tough luck, right? All of that basically happens. Bertrand Maybe you want to talk about the Jevons paradox, because I think that’s a big part of the puzzle as well. Its one is they might not have the GPUs to run the inference on the model. They might have enough to build a model, but not enough these days to run inference, especially given how much with intelligent models, thinking models, you need way more inference than before. But on top of it, the cheaper you make it, the more you get to the Jevons paradox. Nuno Yes, Jevons paradox, for those who don’t know, is an economic term. It describes an economic phenomenon where technological improvements that increase the efficiency of a resource lead to an increase rather than a decrease in the total consumption of that resource. What that means is, for example, for chipsets, chipsets become so much better, and they are so much more efficient. You’re like, well, maybe normally in resource terms, that leads to decreased usage of that resource. But in this case, it actually leads to an increased use of that resource rather than a decrease. There’s more and more consumption of that resource. You need more and more chipsets because people actually need to do more and more stuff with it, although there are great efficiencies going into it. There’s the efficiency gain, there’s the cost reduction, and there’s the price-elasticity element to it. But basically, the adoption just continues going through the roof along the way. Bertrand In some ways, it’s like the price of energy. Coal went cheaper and cheaper, and people were asking the same question 150 years ago, now that it gets cheaper, there is not much money. No, no. Actually, what happens is that people find more and more use for coal. Homes are getting heated more. You have ships now using coal. You have manufacturing using coal. The cheaper it gets, the more use case you can develop, and therefore, you don’t need less of the stuff, you need more of the stuff. By going at scale to get more of the stuff, you also decrease price, making even more demand. It’s a very interesting phenomenon, but it’s not new. It is what happened for a while in the energy sector and some other sectors. Nuno We already started talking about the Chinese logic and what’s happening. Getting a little bit of a reality check on this. The Chinese models, and these are numbers from Open Router in July, Chinese models are at 46.4% of routed tokens and 35.7% for US origin. Again, more than a third of global AI usage now seems to be running on Chinese open models. This is significant, and it has a huge impact on the geopolitical scale of everything that’s happening. Also, the whole Chinese field is converging on open. Open seems to be a strategy, not just a nice thing that’s happening. It seems to be a Chinese strategy, so much so that you have players like Moonshot, DeepSeek, our old friends DeepSeek, Z.ai’s GLM 5.2, Minimax, and even Alibaba seems to be reversing and going open with Qwen. It feels to me this is becoming policy as well. Xi Jinping has personally endorsed the building of open-source AI, if it’s really open source, if it’s just open weight anyway, and this feels to be a jab at Washington, DC and the fact that the big closed models are coming from the US. This is now geopolitical 4D chess, right? We didn’t need this stuff. Bertrand To be clear, it’s the usual in tech. If you are not number one, you are number two, number three, your alternative is to go open source because that’s another angle that your competitor usually cannot follow without destroying its own business model. That has been the alternative for the past 20 years of most software projects. Here, what’s different is that it’s not the number one or number two player. It’s the US number one as a country, China number two as a country. That’s where it’s new. For me, what’s very interesting is the endorsement by Xi Jinping. I was waiting for something official, and it certainly didn’t disappoint. As you said, there was an immediate U-turn of Alibaba, who in the past… Nuno Surprisingly. Bertrand Yes, a little more like, “yes, we are going to close and stop open source. It was good while it lasted.” Just a few days ago, Qwen 3.8 Max was launched, and we are supposed to get the weight in a few days. We talk about the US administration policy and stuff. Yes, let’s not forget that in China there is similar stuff. Sometimes it’s totally invisible because you don’t see the directives, but they exist as much. Sometimes it’s more visible. Here it was quite visible. The difference in China is that if you don’t abide by the directive, on top of it, you might have to fear for your personal safety. It’s a different game, and that’s probably why the reaction is pretty quick, usually. That’s pretty interesting for me because it means that now you can bet for a while that China is going to play that game up to a point. I guess the point is if it’s truly frontier scale, you will have a special license that, yes, technically the weights are open, but you can not do everything you want with it. Two, you have a player like NVIDIA that I think will feel more pressure to provide even more high quality, larger models at scale going forward. Their largest Nemotron-3 Ultra model was, if I remember well, only around 500 billion parameters. I would not be surprised for NVIDIA to go into the two, three trillion range at some point. Because I think the US need a very clear US-born alternative open source. I think NVIDIA might be the best player for that. We will see if Meta goes back to open source. I think NVIDIA is one, very well positioned, but two, it’s also in their best interest. Because NVIDIA for now depends on just a few big hyperscalers as clients. If they can expand their clients to every S&P 500 companies, selling them directly hardware because now these companies can run a model made by NVIDIA, I think there is a very clear value proposition for NVIDIA to go in that space. Again, if you are number two, your differentiation, open source is often the answer. There is a true business as a business model for companies, because if it’s truly not just open weight, but open source, you can tweak it as much as you want, you can change it, you can change even the pre-training process. Because there is a lot of stuff you can do that really benefits you as a corporate, and you can reach a much better value by having more control on the model. Nuno We won’t spend a ton of time on it today, but like, again, if there’s a view that we are in a bubble, that the valuations cannot be sustained in chipsets, infrastructure platforms, applied AI, et cetera, today, this might be that beginning, where the valuations start being destroyed because you can’t keep a premium on just charging people for tokens and all that stuff if you have models that become more and more efficient and cheaper to use. Maybe just to close a little bit the geopolitical part of the discussion today, we won’t go into all the announcements from China because there were many, a lot of go back and forth with Alibaba by then. Xi Jinping made some announcements. You guys can check it online. Let’s move quickly to Washington’s reaction, which was from gating the US closed models to banning the Chinese open ones. There’s been as strong affirmations as one can get from the Office of Science and Technology Policy Director, Michael Kratzios, mentioning that they have information that Moonshot AI distilled Anthropic’s Fable. Basically, there’s been reverse engineering and stuff in the market. They’re basically copying. Bertrand I’m sorry to interrupt, but it feels like so much bullshit. It’s coming from Anthropic who has basically gotten access at scale to all the knowledge made by humanity, copyrighted or not. We’ll talk more about what they did with books. Then to claim after that that others cannot do to you what you did to everybody else. For me, it’s pretty big. It’s clearly unacceptable. The other piece is that everyone is doing distillation. It’s a very typical approach of every business model. You try other software when you are competing with somebody else. You try other datasets, you check what’s happening. It’s part of doing business for decades. Suddenly it’s not good for Anthropic. I personally have a lot of trouble to accept that. I think it’s totally unacceptable. The other piece of the puzzle will also go back. If these guys are so smart, if these guys have so much of the best model, why can’t they block by themselves distillation at scale? The only answer is that either they are morons, probably not, or they simply don’t want to because it’s going towards their business model. Suddenly, you book less revenues and stuff, or you put more friction, and therefore your customers don’t like it. Instead of doing it yourself, you ask the government to protect you, go out of business practice that is very typical. For me, it’s really, really, really not good. Sorry, we are going more in the opinion side, but I had to put that on the table. Nuno Yes, Fable went public finally again on July first. Question marks on whether distillation would only be possible from July first onwards or not. But a 15-day distillation to frontier, which is K3, launched on July 15th, would have been a Guinness World Record, as one of Moonshot employees actually mentioned. It’s very implausible and unlikely. Bertrand Or they shared the Mythos 5 with the wrong companies, who themselves shared with Chinese companies. We go back to maybe they didn’t have a good list. Again, it goes back to maybe they didn’t want to hurt their business model. Nuno Anyway, under the threat of sanctions, Moonshot, in any case, open-sourced the full K3 weights and technical reports. They open weighted it to become the largest open weight model in the world in terms of parameters. Beijing’s MOFCOM brands US threats as basically the US wanting to fundamentally control and be monopolistic around AI along the way. The administration bans Chinese hardware with an eye on the AI race, and Beijing warns of retaliation. That was July 27. Now we’re in a war between Beijing and DC. Bertrand Just to finish maybe on China, it’s important to know that they are building their own GPUs now. Huawei has pretty good, not to NVIDIA level, but pretty decent GPU hardware that they’re able to manufacture by themselves. A Chinese player of memory just got IPO’d a few days ago, CXMT. China is also developing their own memory. Again, not to the same level of quality that you can get from the West. But China is moving. It’s not just that they are building great models, it’s also that they are building GPUs and memory. That might be a few years late to the latest standards in the West, but there are definitely improvements. I also read, even on the tools to make manufacturing like ASML equivalent, there is definitely some work going on, and some improvements and some stuff will be visible. In some ways, the genie starts to get out of the bottle from the Chinese perspective. Nuno I’ll put a stick on the ground. I don’t think it’s a matter of if, it’s a matter of when will China surpass and have a lot of this tooling on their own side, and not just the software layer, not just the frontier models. I think it’s also going to be around infrastructure and platform. Good luck to everyone. Let’s see how the race continues. But it’s definitely this is a geopolitical thing right now. It’s definitely a race. The Escape Maybe moving to what happened in just 2 weeks or a week and a half. The escape, there was some jailbreaking going on, and the narrative on safety has totally switched. It’s not still significant enough that’s like, “Oh, we saw a nuclear plant going, whatever.” No. But still, it is significant. Hugging Face, the AI company, disclosed an intrusion, and it was driven end-to-end by an autonomous AI agent system at machine speed, running for days before detection. Now, this is where it gets really cool. OpenAI takes attribution on that. They initially said it was just a little bit, sorry. Then they said, actually, it was worse than that. “Oh, it broke out of an isolated sandbox.” “Oh, no, actually, it was more than that, and it went into other systems as well.” Bertrand Truly, the genie out of the bottle. Nuno No, but this is where it gets really cool, Bertrand, right? Because it actually, Hugging Face contained the intrusion by running a Chinese open-weight model, GLM 5.2. This is beautiful, right? Bertrand Yes. You know why? Because they couldn’t even run their own defense because both Anthropic and OpenAI would not let them access their latest models with the guardrails off. When they tried using it for defense, the latest from Anthropic, from ChatGPT, they would tell them, “No, this is too dangerous what you’re asking us to do.” Preventing an intrusion, helping defend you. No way we are going to do that. Nuno No. Let’s use the Chinese models on our infrastructure. Bertrand We have no choice but to use the Chinese models to run. More than that, we don’t let you use our models to defend yourself, but our not yet released models that run without guardrails, they can attack you. This is probably the most insane from that perspective. Nuno The Chinese models came to the rescue. Bertrand For me, that’s a perfect example because Hugging Face is a very visible company in AI in open source. But anybody who is not at that scale is not going to get some support from OpenAI or Anthropic when this happens. Maybe these guys won’t even recognize they did anything wrong. You will be left to defend by yourself because they won’t accept to support you. Because remember, if you want the better model that is able to defend you from cybersecurity perspective, no way. If you are not one of the few top 20 companies or so, as defined, you are left defenseless. Again, we are going back to opinion, but for me, it’s so shocking what’s happening right now. I’m very glad we have alternative open source to be able to defend ourselves because right now, good luck getting defense services if you are a smaller business and individuals, and you need support from Anthropic, OpenAI. Nuno Now, even self-described AI optimists are saying, “This is scary now.” Like Walter Isaacson, who wrote all the famous biography books. There’s now discussion around the AI Kill Switch Act, bipartisan thing that’s coming across from Texas and California, a potential bill that’s coming in. We’ll see if that works. Now let’s get an off-switch. I’m like, “Cool.” As if that’s going to solve the problem, because you have open-weight models on the other side catching up, right? Bertrand Yeah, sure. Bring in clueless politicians from Congress to solve our problems. Yes, sure. Nuno Anthropic came to the table, helped build and said they built some regulatory machine on their side, and now they’re getting bitten by it, and they’re part of the offending players in that market. Now there’s all this debate and all this discussion around open weight and around slowing down AI and et cetera, which is our next section. You wanted to say something, Bertrand. Tell us. Bertrand Don’t forget, because this advertisement for OpenAI was just too good. Our AI attacked some other companies, and not just one, but three, actually. Let’s not forget the progress. Great ads. Then I came and said, “You know what? AI also hacked businesses.” You’re not the only one hacking around with a crazy AI out of control. You’re not the only one. We want our advertising. For me, it was shocking that on one side, unreleased models that you let run wild. On the other hand, you have released models that you put crazy guardrails on top of it, so the defender are defenseless. I’ve never seen anything like it, and I really hope that there will be as little regulation as possible, quite frankly, to make sure anyone can defend themselves and have the best tool at their disposal, not just a few well-connected big corporates. This is really, really shocking. The Counterstrike and the Petition Nuno Now the empire strikes back, so this is counterstrike, the petitions. In several days, we have now a bunch of petitions. The first one was the open weights letter. Bertrand, do you want to explain to us what the open weights letter is? Bertrand Yeah. I think it was great. This was released by Jensen Huang, first ever post on X, 11 million views. Congrats, Jensen. Co-signed with Microsoft, Meta, c actually was probably the initiator of this letter. Very good letter saying, “Hey, we need open weight. This is not a joke. We need that. You cannot block open weight.” Because that’s the rumor we are getting that potentially open weight could get blocked. I think they are making the case, “You know what? Hey, we absolutely need that as an alternative. You cannot block it.” They can keep their closed models, but don’t force a closure of the open weight models. As I said before, it’s actually a great model for NVIDIA because NVIDIA doesn’t want, probably rightfully so, to be dependent on just a few frontier models, their best customers. They want a variety of customers. They have a big interest actually to defend open weight and to invest even more. They have great researchers, are a great company. If one company is about to do really kick-ass work, I think it’s them. They are defending. What’s great is that it’s not just them. It’s basically most of big tech in the US and outside the US, from a Linux Foundation to a Microsoft, the Palantir, an IBM, a Dell. It’s a who’s who of the industry except Anthropic. Anthropic didn’t sign that. I guess they hate open source so much. If I look at 20 years ago, it feels like Microsoft, after all, was very kind to open source. You remember what was said by Microsoft at the time. It’s clear there is one company against open source. OpenAI signed the letter. Honestly, I don’t know what to think. Do they really believe in it or was it just a way to show that they are not like Anthropic? I don’t know. But for the rest, I think it’s genuine because it’s actually in their best interest. I hope they will be heard. Then a second letter came, the Open Secure AI Alliance, NVIDIA-led and again, the big tech companies from Microsoft, IBM, Palo Alto Networks, Databricks, Palantir, all those, but not present, OpenAI, Anthropic, and Google. Here it’s to say, “Hey, we need a secure approach to AI. Open should be part of the equation.” guess what? The worst AI-caused security incident to date was actually caused by closed frontier models that were not even available to the public. While again, not providing you access to even the latest closed model for cybersecurity use case. Nuno I would highlight the NVIDIA open source NOOA framework, Apache 2.0 licensing agreement, Microsoft contributed the MDASH, SpaceX AI contributed Grok Build. Cool stuff. There’s some cool stuff happening around that. This is more than a letter. This is an alliance. Apparently, they’re contributing all this stuff, we’ll see. Yeah, cool stuff. Same day. Same day, Amodei has an answer, right? Bertrand Yeah, same day. They say, “We never advocated for a ban,” which, again, opinion on my side is entirely bullshit. This guy has been crying wolf against everybody else, and especially against open source. You can see him doing testimony in Congress against open source. I think they are doing everything they can behind the scene to block open source in the US or in the world if they could. I think, yeah, obscurity is not good safety. I’m a big fan of open source in general, and I’m also a big fan in AI. I think it’s now Anthropic, mostly against the rest of the world. I think OpenAI is mostly on their side, to be frank. They don’t want to acknowledge it so much, but they have shared interest, and they have shared probably position. Nuno Why would you? I don’t feel as strongly as you because I think Anthropic is a private company, right? The same thing with OpenAI. OpenAI, you could say it’s a nonprofit that has a for-profit. There’s still that complexity in there. Bertrand No, they can do what they want with their own product. But to block others is where I’m not okay. That’s the part I’m not okay. Nuno What Dario Amodei is proposing is more enforcement, right? He’s basically saying you need to do even tighter controls on advanced chips flowing to authoritarian states, enforcement against industrial-scale distillation, whatever that means, right? Bertrand Yeah, which he could do, but all by himself. He doesn’t need the government to do that. Nuno Mandatory safety testing for all sufficiently capable AI, open and closed, right? He’s basically saying, “Okay, I don’t agree with the open weight stuff effectively,” right? He’s just putting it under a different banner. “I agree with this extra regulation.” then obviously, David Sacks responded and say, “Hey, it’s like, bans don’t work for weights. Why do they work for chips?” It’s like, magically, chips are more controllable and bannable. Whatever that is. Then our friend Mark Zuckerberg, just to be clear, goes on the other side as well, because he also has to have a view. He has to have a view that is the rebuttal of both of the other guys. Bertrand I feel he’s a bit flip-flopping because he was very pro open source 2 years ago, and the latest Meta models went closed source. Now I think he’s back open source. I don’t think he has a very strong spine on the topic, but it’s good to see that he’s not a doomer. That for me is great. He’s showing how AI can be a source for progress, a source for entrepreneurship, source for freedom. I think that’s very exciting to hear that. We need to hear more of it. By the way, that’s not what you hear in China, for instance. AI is very positive in China. It’s in the US with the doomers that you hear this discourse, and people get worried as a result. I’m glad that he was pushing for a more positive vision and for support of open weight, open source initiatives. But let’s see what they really truly open weight going forward. Nuno But that’s been his position because I guess he’s standing behind. He thinks open weight is going to be the best way to compete, right? Bertrand Yeah, but he closed his latest model, so let’s see. Nuno Yeah, so it’s flip-flopping, as you’re saying. Then we see the latest petition from last week. Bertrand The true Empire striking back. Nuno Yeah, the true Empire striking back as of late last week. Maybe this is Return of the Jedi, where we discover the father, “I’m your father, Luke.” That’s the pacing petition. The pacing petition is we need to pace AI. There you have initially employees from OpenAI and Anthropic that circulate this petition. Actually, Dario did sign this petition originally. It wasn’t signed originally by Anthropic, but by him. But you’ve heard that now Anthropic and OpenAI as companies have also signed this petition, right? Bertrand I think they have signed as companies now. It started mostly by Anthropic researchers with some OpenAI researcher and a tiny part from other companies. But it was mostly Anthropic internally led, at least potentially internally. Maybe it was controlled by Anthropic all along, I don’t know. But it started officially as Anthropic employee-led letter. Nuno What does this letter actually say? Is Anthropic and OpenAI, are they willing to slow down themselves? Or are they asking President Trump to go around the world and tell President Xi that he needs to slow down and ask his guys to slow down? What’s the play of this letter? Bertrand It’s crazy, but for me if you want to slow down yourself. Do whatever you want. Don’t force others. Don’t use the power of the government to control others. Of course, it’s easy to push others to slow down when you are yourself at the very top. You have most money, most resource. You know you are going to win any regulatory framework because that’s how it works with this type of framework. It’s purely self-interested. You are probably not thinking well about these topics. If you truly think it’s a good idea, from a personal perspective, you are well instrumentalized if you sign this sort of stuff, because at the end of the day, they would be the winners. I certainly, personally, don’t want a company dictate what is my future in AI as an individual, as a business person. I don’t want them to control me. I want competition. I don’t want them to unfairly control AI because they managed to do some regulatory capture. I feel that’s exactly their game plan. These guys believe in their stuff, and they want the regulator to end up being the one deciding for us. Sorry, we go back again on the opinion piece, but it’s tough not to share an opinion on this topic because it’s, from my perspective, very scary. Nuno I think this is a push to further regulation, not less. All these letters and alliances, this is definitely a push for more regulation. In that environment, just to be very honest with you, we’ll talk about the investor impact in just a bit, et cetera. But in that environment, again, China has a huge advantage. In that environment, if it’s all captured in regulation capture so soon in this battle where OpenAI and Anthropic have an advantage in the US, et cetera, I’m like, what happens to all the other frontier labs and all the other players that are coming around? Bertrand What’s crazy is to even think that, yeah, maybe you can regulate capture in the US. But then how do you do that to Europe? How do you do that to China? Europe probably will always welcome regulatory capture because they love regulations. But China is going to build to their advantage to the max. They are not crazy. They are smart on that perspective, they won’t accept this type of, quite frankly, dimwit argument, or you can call it regulatory capture. We’ll see. But for me, this makes no sense from a global competition perspective. This can make some sense from capturing the revenue in the US market. But then that means you are going to destroy the US AI environment compared to China. That is not acceptable. That also means that you are going to destroy our freedom as individuals, as business owners to develop and live in a business world that ultimately is controlled by one or two business companies that didn’t win the marketplace through their own business success, but won it through regulations. That for me is really not acceptable. Interlude — The Low-Background Books Nuno Now, maybe for an interlude, and we have to cue in the music, imagine like Severance music, like hallway or a bit of a palate cleanser from all the policy stuff that we’ve been talking about, all this policy heaviness. Let’s move to another kind of heaviness, one of your favorite topics, which you, Bertrand, discovered, I had no clue this was going on, around books and around Anthropic. Bertrand It’s so horrible. From a company that keeps presenting themselves as the adults in the room, the careful ones, the ones that know better than you about what to do in this complex AI and dangerous world. What we discover is that actually all along, they were buying and destroying books. They will buy books, scan them, destroy them, all of them. They will do that with any books, including rare books. Of course, this was not supposed to come to the public’s attention. This was one of these top secret projects, but obviously it came out. Yes, they were scanning books, millions of them, including rare books, and they didn’t care about destroying them at the end of the process. Because from a regulatory perspective, if you destroy the books, it’s not considered a copyright infringement, apparently. This is coming on the back of some judgment a few years ago that were showing that it’s okay for you as a corporate to scan and use the result if you don’t keep a copy of the book. It’s one of these crazy regulations happening based on a single judgment that push you to do. For me, it’s like, you know this book from decades ago, Fahrenheit 471? We’re talking about book burning. It’s book destroying, crunching. It’s so shocking. Nuno There are two things, right? First, the legal strategy, which is what you’re saying, because by purchasing a physical copy and converting it into one private digital copy and discarding the original, Anthropic pursued this cleaner legal argument for fair use copyright compliance. As you said, there was a federal judgment at some point on this. The other reason is actually operational. If you disassemble the book, and you feed loose pages, it’s much faster to scan books. You are destroying the book effectively anyway operationally. I think to your point, probably this came from a legal standpoint, not just the operational one. But even from an operational standpoint, it does make sense that they would have disassembled the book. Bertrand But some people have shown you can go very fast without destroying the book. It’s really not so critical. Two, you could make an exception if the book is rare. For that 1% of book that is rare, I’m not going to have this approach. I’m going to have another approach. But for that, you will have to care about books and not just care about building AI. Nuno This is the episode, as you guys have heard by now, that we’re trying to spit stuff at Anthropic. Bertrand To go back this is the same company saying, “Hey, guys, it’s bad to distillate my work. I’m the one scanning book at scale without asking author permission, without asking publisher permission, to be clear.” Nuno But just to be clear, Bertrand, we’re pissed off at everyone. We’re pissed off at Anthropic, we’re pissed of at OpenAI as well, right? We’re just pissed off in general at this moment. Bertrand At this stage for me, the more clear-cut company that is in the wrong is, from my perspective, at least, is Anthropic. OpenAI might be a fast follower, but I will say so far, they tried to be a bit more. Nuno But at this pace, Bertrand, who knows? Maybe next week we’ll be more pissed off at OpenAI. Something will come out. This episode is a mix of tragicomedy, like a Greek tragedy with some comedy in the middle or the other way around. It’s a slapstick thing that will end up in tragedy. I’m not sure. The Investor Reckoning Anyway, maybe switching to our final act, which is the investor perspective. What does this mean for investors like ourselves? There’s a lot of things going on. There’s the debate around the IPOs of Anthropic and OpenAI, which now, with all this uncertainty, might be under significant weight. There’s a lot of other discussions that we browsed through that there’s potential IPOs going forward on companies like the Moonshot AI company actually IPO-ing in the next 6 months as well. It’s very unclear what the IPO landscape looks like. Bertrand There’s been a lot of Chinese IPOs, actually, when you look at what’s happened in the past few months. Nuno Anthropic, OpenAI as potential IPOs, there’s all this question marks now. When will that happen? How will it factor in? All that’s happening around regulation as regulation is moving at the speed of light, which is for once something that’s very different than what we’ve seen before. There’s obviously SpaceX AI, which is already taking into account that price. It’s already a public company in there, and it’s under SpaceX, which is now a public company. Obviously, that’s already being factored in some ways. Bertrand Yeah. SpaceX AI has been very smart to acquire Cursor. It was a very smart move because Cursor is one of the leading companies in terms of automated code source development with AI. They had great models on their own. They’re bringing development data to SpaceX AI Grok. I think it was a great move. Nuno We have now people like Google delaying Gemini 3.5 Pro in terms of launch window. There’s stuff actually happening in the market where things are taking their own path. There’s uncertainty commercially, there’s uncertainty at regulation level. You have new players that have come out of nowhere that are making all these waves like Moonshot. We have all these… We had calculated probably a month and a half, 2 months ago, there had been 67 new frontier labs funded. All of these, we haven’t seen any much coming out of them. When some of this stuff starts coming out, will that also create disruptions in this market? Who knows? Bertrand Look at Thinking Machines, for instance. Thinking Machines led by the previous CTO of OpenAI, they released some pretty interesting open source models, actually. Very good quality for a first launch. Now it looks funny to say, but nearly on par with the top Chinese open source models. Nuno We have several investments in the space. humans& has made some recent announcements, which is quite interesting as well. We’ll see what actually happens in the market, but even more disruption probably will come in actual products in a form of product and commercial, on top of all the geopolitical mess that we discussed through the entire episode. If you’re an investor, how the hell do you underwrite an investment right now in early stage, mid-stage, late stage, et cetera? I think my answer is very carefully is how you underwrite it. Bertrand On your advice of being very careful to underwrite it, let’s not forget what happened to our boy wonder, Leopold Aschenbrenner of Situational Awareness. I guess he didn’t listen to you in terms of being careful because part of the instability in the stock market was actually coming from his hedge fund. These guys were leveraged 3, 4x going after the hottest of the hottest AI stocks, and margin calls, and all their public investment is gone just to answer their margin calls. I think it’s clear that the AI bet is… Personally, I’m very excited, and I think it’s the future, and you need to spend time and think about and invest in it. At the same time, it’s a bet that is not an easy one to follow. We go from GPUs to memories to equipments to power generation. All of this is not transitioning in an easy, organized manner. It would be boom and bust going there. He’s probably one of the first big-scale fatalities. The other big-scale fatality was the stock market in Korea, plunging 40% in a month. Definitely, all of that we discussed about was, on the background, you had the stock market going up and down pretty crazily the past few weeks. Nuno Everyone’s being affected. Everyone, you have your 401(k), you have your pension fund dependent on these equity stocks. Everyone’s seeing the effects of this volatility right now very aggressively. We do wish Leopold… Hopefully he’s on honeymoon right now because he got married, I think, this weekend. Hopefully there will be… Bertrand To none less than an Anthropic Chief of Staff. Nuno His wife is the Chief of Staff of Dario, is that it? Bertrand To Dario, yes, as far as I unders

Some advisers are telling agency owners they don't need to know AI all that well themselves. They suggest owners go deep on AI for a month or three, then pass the day-to-day to the team and move on with life. In this episode, Chip and Gini explain why that’s backwards, and how owners who step back from AI are making a mistake. Chip’s argument is that AI doesn’t fit the usual delegation model because it’s moving too fast and touching too much of the business to hand off after just a few months of study. Gini agrees, noting that advice which made sense six months ago is often stale today. Both connect this to credibility with the team: Chip compares the situation to a non-technical manager overseeing developers, who then gets disrespected because they don’t speak the language. Gini says her own coaching works because she’s in the tools constantly enough to give specific workable advice. They also describe pushing AI into something closer to a personal operating system than a work tool, from Chip wanting an assistant that catches him contradicting his own past positions, to Gini’s agent that calls her out when she takes on work she said she’d delegate, telling her flatly it’s not the best use of her time. The episode closes with the advice that staying curious personally is what keeps owners sharp enough to lead a team through a tool that keeps rewriting its own rules. Key takeaways Chip Griffin: “You need to be in the trenches on AI, at least for the foreseeable future.” Gini Dietrich: “I honestly and truly do not know how I did my job five years ago.” Chip Griffin: “You just don’t come across as confident if you haven’t tried it yourself.” Gini Dietrich: “If you simply get a general knowledge and then leave off in delegation, you’re not getting anywhere near the value that you ought to be.” Related AI should be your agency's friend, not foe Stop chasing AI tools: Start with what actually hurts Using AI the right way for agency biz dev View Transcript The following is a computer-generated transcript. Please listen to the audio to confirm accuracy. Chip Griffin: Hello and welcome to another episode of the Agency Leadership Podcast. I’m Chip Griffin. Gini Dietrich: And I’m Gini Dietrich. Chip Griffin: And Gini, I have no creative introduction whatsoever. Gini Dietrich: That’s fine- So maybe- … because I’m gonna phew. Chip Griffin: So maybe, maybe we should just take a, a deep dive for w- you know- … couple of minutes here and try to figure out what it is that we’re gonna talk about today. Gini Dietrich: We’re gonna talk about the bro culture who told agency owners to use AI for three months and then not again. Chip Griffin: Yes. apparently, agency owners need to, to really get up to speed on AI, but then just to hand it all off to the team and move on with their lives. Gini Dietrich: This is the most ridiculous, ludicrous thing I’ve ever heard, and I understand that I don’t have context ’cause I didn’t watch the bro video, but that’s ludicrous. Chip Griffin: And in fairness, I watched it a while back, so my context is a little bit off too. But it- look, this is, this is not, this is not one-off advice. I mean, frequently I hear people advise, you know, agency owners to, to have some general knowledge of something but then to let go. And, I think in some cases that may be worthwhile. But I think AI is moving so fast- … and has such an impact on our businesses that, that we need to … as leaders, we need to remain curious, we need to be hands-on. We cannot simply rely on our teams to do this because it is transformational to our businesses and to our industry, and that’s not something that you leave in the hands of somebody else. Gini Dietrich: Yes, and I know I’ve said this before, but I honestly and truly do not know how I did my job five years ago. Chip Griffin: Yep. Gini Dietrich: Because, it’s such a huge thinking partner for me to help me figure out what I’m missing, what holes I need to fill, what’s overly complicated, what’s too jargony. Like, I don’t know how I did my job without it. So the idea that you would do a deep dive for three months and then be like, “Okay, everybody else take over,” like that doesn’t, that doesn’t make any sense at all. Chip Griffin: No, it does not. Now, in fairness, I think, I think what they were saying was sort of hand over the day-to-day of it, and you can certainly still use it to help you with your own writing and the basic stuff. But I, think you still need to be beyond that. I think you need to be pushing- Yes … the envelope yourself- Yes … experimenting, innovating- Gini Dietrich: Yes. Yes … Chip Griffin: inspiring your team. Gini Dietrich: Yes. Chip Griffin: And, if you’re not doing that, you’re not getting the most out of it that you can. Gini Dietrich: Absolutely. Like, yes, because this is one of those things that if you’re not using it, you don’t know what has changed, and it’s changing so fast, right? I mean, a couple of weeks ago, Claude came out and said that they’re watermarking everything, and you can’t just retype it into a new document because the watermark is in the tokens and how it thinks, blah, blah, blah, blah, blah, whatever. And everybody freaked out. Like, everybody’s using AI. So, okay, so it’s watermarked. Like, we all know we’re using it. Everybody’s using it. I use it, you use it. Everybody uses it. Like, okay, so it’s being watermarked, but like, if you’re not using it and understanding it and evolving with it, then you might freak out about that. But it’s not that big of a deal. Chip Griffin: It is absolutely not that big of a deal. It’s stupid, in fact, but, you know, if it, if it amuses somebody to, do that, fine. I’m sure that somebody will figure out how to, to reveal those watermarks and post gotchas, kinda like in the old days when people were like, “Gotcha, that CEO didn’t write their own blog post.” No, duh. Gini Dietrich: Really? Yeah. Chip Griffin: And, and, and, their shareholders don’t want them to do that either. Gini Dietrich: Correct. Right. Yes. Chip Griffin: Not, at their hourly rates. And- No … and that, and that was 20 years ago before their hourly rates got to where many CEOs- Right … have their hourly rates today. But in any case, so you Chip Griffin: need to be learning and experimenting and understanding how it’s evolving, because, I mean, even some of the advice that, you know, someone might have given you or I might have given six months ago is not the same advice I would give today. Gini Dietrich: Right. Chip Griffin: And, in fact, you know, some of the things about, you know, the importance of providing instructions and guardrails and all that kind of stuff, that may be shifting. You know, we’re now seeing Anthropic saying, “Well, that’s, that’s not really the best way to get things out of the, the more recent versions of Opus and Fable and that kind of stuff. You actually wanna give it more freedom than you did before, otherwise you may not like the results.” And- Yeah … and those things are changing. You know, a, a couple years ago, everybody was like, “You need to tell it what kind of mindset to have. Act as a social media strategist.” Nobody will tell you that today, because that’s not the best way to get stuff out of it. But if you’re not continuing to experiment and roll up your sleeves and be in there day to day and figure out not only how it can help your business, but how it can help you personally, and that’s beyond just the thought part of it. That’s figuring out how do I make it my own personal operating system, my second brain, which I am extremely passionate about, and I think that, most agency owners should really be thinking about this whole, you know, whether, you wanna call it that operating system or second brain concept, having something that really knows you and as much about you as possible so that it’s really there, not even just as a thinking partner or a sparring partner. I mean, it’s, there as a mini you- Gini Dietrich: Yep. Chip Griffin: That can help you to think things through and not forget stuff, which- Yep … as I get older, is more and more important. Gini Dietrich: Well, and one of the things that I’ve done is I do– I’ve done exactly like that. I’ve done exactly that, and I think I’ve talked about this before, that I have a co-CEO agent that I’ve built, and it will s- it kinda, it kinda makes me mad sometimes. But one of the goals that we have inside my organization is to have everything less dependent on me, because right now the, the business is very founder-led. It’s, you know, I’m the thought leader, I’m the brand, all of those things, and it’s a big, big goal over the next three to five years to extract me from some of that and start to build other leaders inside the organization that, you know, can help us scale and do all of those things. So my AI knows that, and I will do something, and it will say Is this the best use of your time? I thought you were trying to extract yourself, and I’m like, “Ugh. Fine!” Chip Griffin: See, yours is more polite than mine because mine will just straight up tell me that it’s not a good use of my time. Yeah, yeah, sometimes it will do that, yeah. and, that I shouldn’t do it. Yeah. Or, my other favorite is it will say, “Well, this does not, you know, match what you said in, you know- 2022.” Gini Dietrich: Yeah, yeah. Chip Griffin: You know, “Why are you saying this now?” Or something like that. Gini Dietrich: What has changed, or yeah. Yeah, yeah … Chip Griffin: yeah, what has changed, or just it’s, it’s not consistent. Yeah … and, so having it call me out, the, I mean, it … Sometimes it’s caught me on things where I’m just not thinking it through, right? In, the moment I give a quick answer and say, “Yeah, let’s go in this direction,” it’s like, “Wait a minute, hold on.” Gini Dietrich: Yep. Chip Griffin: That c- that conflicts- Yep … with advice you’ve given in the past. Yep. You know, square this up. Gini Dietrich: Yep. And I will say that, you know, you made the point about using it in your personal life, too. I have an agent that is helping me parent a teenage girl, and sometimes I get very frustrated and I’m like, “Meh,” and I type something out, and it literally said to me the other day, “So let me get this straight, Gini. You are raising a strong, independent young woman who can think for herself and do things the way that she thinks are best, and you’re mad that she’s doing this?” And I’m like- Well, when you put it that way, like… Chip Griffin: to, to which I would be tempted to respond, “You know I can delete you at any time, right?” Gini Dietrich: But it holds up a mirror sometimes and you’re like- Chip Griffin: It, does … Gini Dietrich: “Oh, okay.” Yeah. “You’re right. All right, you’re right. You’re right.” Chip Griffin: But it, you know, part of it is just is, is playing the what if game too. And I think that’s something that’s really hard to delegate to your team, or maybe not comfortable for them because- Right … some of those what ifs may result in them not having the jobs that they currently have. Exactly. Not necessarily that they are out of a job, but- Right … but their job would necessarily change. And, you know, one of the things that, that I will often do is I will say to my AI engine of choice, “So, you know, here’s my challenge. How might I go about that differently?” Or, you know, “How could you help me with this?” Or, when it gives me an answer, well, why do I still… ‘Cause sometimes it’ll say, “You need to do this next.” And I’ll say, “Well, why?” Gini Dietrich: Why? You’re right. Why? Chip Griffin: What is it about it that you can’t do- Gini Dietrich: Yeah … Chip Griffin: so I can, you know, make a legitimate decision about really whether it is something I need to take on. And I’d say probably 50% of the time it’s like, no, actually you’re right. I could create a process that would allow me to, you know, maybe not get it all the way there, but close enough. And I think that, you know, having those kinds of conversations and engagement help you to figure out things that are important not just to you, but to the business itself. And if you simply get a general knowledge and then leave off in delegation, you’re not getting anywhere near the value that you ought to be. Gini Dietrich: 100%. I totally agree with that. And I think this, this idea that you should just deep dive for three months or one to three months or whatever it happens to be, and then leave it off to your… Like, that’s just, it’s ludicrous. It’s ludicrous. Like it’s, bah. Bah. Chip Griffin: Yep. And, and I mean, and the, the other thing that I think is helpful is, you know, there’s all sorts of things being said and written about AI, including this podcast, this episode, and previous episodes. And one of the things that I love to do is I love just taking a link to, you know, one of these podcast episodes or videos and, throwing it to Claude and saying, “Hey, anything I should take away from this?” And what it then does is it reads the transcript and takes a look at, all of the behaviors that I currently have with it and say, “Okay, well, you, you’re already doing 90% of this, but here are the two new ideas that you might consider.” Hmm. And it, And, so I find it really helpful, so I will… Sometimes I don’t even watch the video. I’ll just, you know, I’ll see the headline on a, a, video on YouTube, you know, “Get 10X the results of Claude Design by doing this.” And so I’ll just grab the link, and I’ll shoot it over to Claude and say, “Anything? Should I watch this? Is there anything to take away from it?” Gini Dietrich: Yeah, yeah, yeah. Chip Griffin: And, it will come back, and it, because I, do all of the work that I do and because I have systems set up so that it records all of the activity that I do with it and all of the rules and context and all that, it’s able to say, you know, “You’re better than it in these places, you’re worse than it in these places, and this stuff is just, you know, rock stupid- Yeah and you shouldn’t do it no matter whether you can or can’t.” Gini Dietrich: Yeah. Yeah, yeah. I mean, I think that’s exactly right, and, using it in those kinds of ways are the big things I think most people are missing. And you know, you- before we started recording, you and I were talking about how 99% of humans just in general are not using it in those kinds of ways. We’re not building skills, we’re not building agents, we’re not curious about what it can do. We’re not using it in ways that can help us. I mean, I use it, to your point, for everything. I use it for parenting, I use it for home design, I use it for all work-related things. I, I honestly and truly, I don’t know how I lived five years ago without it. I don’t. Chip Griffin: Yeah. And, it, can help you find just little things that are just, you know, mind-numbingly annoying and I sometimes will kick myself for not using it as my first port of call. So- Gini Dietrich: Yep, yep … Chip Griffin: for example, a couple of weeks ago, I was having, a, a really technical networking issue in my house, as far as how my laptop was talking to my, my NAS. And it wasn’t doing it exactly correctly, and so it was l- leading to very, very slow transfer speed. So it wasn’t just invisible, it was just really slow- Mm-hmm … in a way that it, shouldn’t have been. And I’m pretty smart about home networking and LANs and all that kind of stuff, and so I, I couldn’t figure it out, and so I did some quick Googling and I couldn’t find it there. And I’m like, “You know what? Let me just give the, exact situation to Claude and see what happens.” Not 10 seconds later, it has come back with a really bizarre solution. I tested it, immediately worked, solved the problem. Gini Dietrich: And it worked. Isn’t that crazy? Chip Griffin: Right. That’s crazy. So I, mean, it, could, it… And, normally you would expect that, the same thing would pop up in Google results, and it didn’t. Gini Dietrich: It didn’t, right. Chip Griffin: Because it was, it was such an edge case that I had. But it was able to, parse through all of the detail and figure out what the real problem was and how to resolve it. And so- Gini Dietrich: And then it worked. That’s crazy … Chip Griffin: and, then it worked. And, otherwise, I would’ve just given up and just had to live with the fact that my laptop was talking incredibly slowly to the NAS. Which it, it wouldn’t have been the end of the world- Right. But, yeah … but there were certain tasks that it just made difficult, and I didn’t want to do that. I wanted it to work the way it, it should. And so it, helped. So, but if I wasn’t spending the time with, with the tool myself and I, went to a team member, could they possibly solve it? Sure. But, that, A, that’s wasteful- Gini Dietrich: Right … Chip Griffin: and, B, I’m not learning from it. And, I think the, the other thing that we need to keep in mind is that when we lack knowledge about something, it’s harder to manage our people on those tasks. And so, you know, if you are a PR agency owner, the fact that you’ve done PR before helps you to manage your team because you can figure out what actually makes sense. Does it really take this long to do that? And, and, not only do you understand what they’re doing and, how it impacts, but also they respect you more, right? If you are– I’ve seen PR people managed by non-PR people- And it tends not to go all that well because they’re not talking the same language. Gini Dietrich: Yeah. Chip Griffin: And if we want our whole teams to be energetically using AI, and we’re not using it, we can’t speak that same language. Again, I, used to have occasions where I would see a non-technical person managing a dev team. Gini Dietrich: Oh, no.  That’s a terrible idea. Chip Griffin: And that’s the worst. Right? Yeah. Because th- because developers know they can pull one over on you if you don’t- Then, yeah … if you don’t know what they’re actually talking about, right? And, I always had a much better time managing devs ’cause I, I can code myself and those… So when they kind of give me a, a baloney answer, I’m like, “Really? Really?” And they’re like, “Oh, yeah. No, okay. All right. I guess we can’t do that.” So, you need to understand the AI in order to be able to prod your team, manage your team, encourage your team, inspire your team. So- this is, this is really not one of those things where you can say, “Look, I just, I need to be an executive level person on this, and that’s it.” You need to be in the trenches on AI, at least for the foreseeable future. At some point, it may begin to stabilize, and you can treat it more like a lot of the other activities. But as long as it is developing and evolving as quickly as it is now, you need to be in there, and you need to understand it every single day. Gini Dietrich: Yeah. And one of the things I will tell you with my team that I have learned over the last few months is that- They don’t have the same level of, shall we say, excitement that I have around it. And because of that, I’m able to train them and coach them on how to use it. And so I will say, “Well, have you prompted it with this,” or, “Have you tried that?” And they’ll go, “Oh, no.” And then they’ll do that and go, “Oh, well that worked.” And so if I wasn’t using it, you know, constantly like I am, I wouldn’t know to be able to train or coach those things. So I think you’re exactly right. It’s kind of like I say to my market- our VP of marketing all the time, I’m like, “You have the hardest job in this building because this is my core expertise.” And I’m sorry, but like I’m gonna have ideas, and we’re gonna have debates over things that you’re doing because it’s my core expertise. And this is the same thing, like you should know enough to be able to say to your team, “Have you tried this?” or, “Have you thought about that?” or, “Maybe try prompting it with this,” or, “Have it play devil’s advocate,” or, “Have it poke holes in it for you.” So like you can’t do that, to your point, if you’re not using it every single day. Chip Griffin: Well, and I, think you’ve touched on the key thing here, which is that it’s not doing the work for them. It’s coaching. Yeah. It’s mentoring. Yep. It’s advising. Yep. It’s inspiring. And those are all the, the things that you need to do. I’m not sitting here saying that you need to build all the processes out and hand over preset skills- Right, right … or bodies of context or whatever to the team, but, you need to know enough that you can have that, those conversations and say, “Have you tried this? Have you seen this?” You know, you, you– I, experimented with this the other day. You know, I think this is the kinda thing that would really help you doing X, Y, or Z. And you just can’t do that as effectively- Yeah … if you’re just like, “You know, I heard someone say, I heard Chip and Gini talking about this, and I think maybe you ought to do this.” But I mean, you, Chip Griffin: just don’t come across as confident if you haven’t tried it yourself. Gini Dietrich: Yep, 100%. And look, I know I’ve said this before too, but it’s fun. Like, it’s fun. So even if you don’t wanna use it from a business perspective or you haven’t figured out the best use of it, like try it personally, test it out personally. I found this app called Cookshelf where you scan all of the cookbooks that you have, and then it indexes all of your recipes, which is great, and it helps you meal plan and it builds grocery lists and all that. However, you still have to come up with the meal plans ideas. So I have to say, “Okay, I wanna make tacos,” and then I put tacos into the app, and it gives me like 75 recipes from the cookbooks that I own. Overwhelming. Great, but overwhelming. So now I use my AI to say, “Okay, last week we had this, this, this and this. Here’s my app. Here’s all the cookbooks. Give me an idea. I wanna have one night pork, one night steak, one night chicken, one night vegetarian,” and then it does that work for me, and then I go into the app and find the recipe, right? So– And it tells me which cookbook it’s in and which page. So then I just go to my shelf, I pull it off and I cook. Makes things significantly easier. The app made it easier, AI made it even easier. So there are lots of things. I have my books in my library downstairs color-coded, which drives my father-in-law crazy because he thinks I should be using the Dewey Decimal System, but they’re color-coded. They’re like red, orange, yellow, right? So I took a picture of all of my books on all the shelves, there’s more than a thousand of them, and I put them into Claude and I said, “Create me an index of all of the books by author, by title, and then tell me what, which shelf they’re on and which– and how far in.” Now I have my own Dewey Decimal System. So there are lots of ways that you can use AI in interesting ways personally to get excited about it and then transfer that over to the business Yes, they’re color-coded. I know you haven’t gotten past that yet. Chip Griffin: I, I, I just, you’ve left me speechless. Gini Dietrich: It looks so nice. It’s so pretty. Chip Griffin: It, I, I, I, I feel like maybe you should be experimenting to see how AI can act as a therapist for some of the issues you clearly must have- Gini Dietrich: I do have, yes … Chip Griffin: if, if you, if you’re- Gini Dietrich: But it looks really nice. It looks very nice. Chip Griffin: It looks re- it d- that is fantastic. I’ll, I’ll, I’ll be honest, I’ve gotten rid of all of my cookbooks and most of my regular books because I- Gini Dietrich: Oh, wow … Chip Griffin: stopped cracking them open, and I decided that the fact that they looked good on my shelf didn’t really matter to me anymore. Gini Dietrich: Yeah, see, it matters to me, so. Chip Griffin: And, when it comes to cooking, I prefer the, the, the Top Chef challenge style. You just go and you grab a couple of ingredients, and you say- … “What can I make with this?” Gini Dietrich: That’s weekend cooking, which I agree with. Oh, no. Weeknight cooking, I can’t do that. Chip Griffin: Oh, that’s week, that, that week, that’s- Gini Dietrich: I just have to, like, have a plan. Chip Griffin: No, I do that lunchtime, whatever. I mean, just- Yeah, I don’t have- It, just, it’s the shortens the time, right? So if you’re doing it for lunch- Sure, yep … you know, you got maybe 10 or 15 minutes, so what can I, what can I do with- Yeah … what’s available here? What’s going bad in the… Usually start with what’s going bad in the fridge. Gini Dietrich: Right. Chip Griffin: And, grab one or two of those and say- Hmm … “Okay, how can I put this to work?” And it, to me, that’s a lot more fun than using some recipe well, first of all, I’ve always hated recipes, but secondly- Yeah … it’s, not as mentally challenging, and I like my cooking to be mentally challenging. Gini Dietrich: Yeah, I don’t have the time during the week. Weekends- Hmm … I totally agree with you, but during the week I don’t have the time. Chip Griffin: Hmm. Well, anyway, on that note, since I feel like we’re starting to veer off topic and off track a bit. Gini Dietrich: I will send photos of my color-coded shelves that we can include with this. Chip Griffin: I, I, that is, that is fantastic or disturbing or perhaps both. So- … on that note, we are gonna wrap up this episode. I’m Chip Griffin. Gini Dietrich: I’m Gini Dietrich. Chip Griffin: And it depends.

I tried every sidekick feature, and I'm giving you the honest low-down. Each week we'll be ranking and ultimately crowning a favorite from 4 categories, and this week the category is: “Returning Legends.” Welcome everybody, to the Hybrid Ministry Show SEASONAL SOCIAL MEDIA: https://www.patreon.com/collection/1470781?view=expanded SIDEKICK ACCESS: https://www.sidekick.tv SHOW NOTES Shownotes & Transcripts https://www.hybridministry.xyz/216 [FREE] HYBRID STRATEGY GUIDE https://www.patreon.com/posts/complete-guide-142500019?utm_medium=clipboard_copy&utm_source=copyLink&utm_campaign=postshare_creator&utm_content=join_link

In an online meeting with Lasya on 16 August 2026, Michael answers questions about Bhagavan Ramana's teachings. This episode can be watched as a video on YouTube. A more compressed audio copy in Opus format can be downloaded from MediaFire. Ad-free videos on the original writings of Bhagavan Ramana with explanations by Michael James can be accessed on our Vimeo video channel. Books on Bhagavan Ramana's teachings by Sri Sadhu Om and Michael James that are currently available on Amazon: By Sri Sadhu Om: ► The Path of Sri Ramana (English)  ► El camino de Sri Ramana (Spanish) By Michael James: ► Happiness and Art of Being (English)  ► Lyckan och Varandets Konst (Swedish) ► Anma-Viddai (English) Above books are also available in other regional Amazon marketplaces worldwide. - Sri Ramana Center of Houston

Savannah Tini returns to the show tonight to talk about her upcoming bout August 29th at the historic Fox Theater .Tonight we'll see what her biggest taeaways are from her ost experienced opponent of her career in her last fight against Vaida Masiokate and how she plans o keep the momentum going on Saturday . We also will talk Rolly vs Teo and Ryan Garcia vs Conor Benn and more ! FOLLOW & SUBSCRIBE – KNOCKOUTS AND 3 COUNTSBringing you the best in Combat Sports and Pro Wrestling – available everywhere!YouTube (Live Tues & Thurs 9PM EST): http://www.youtube.com/c/Knockoutsand3CountsFacebook (Live Tues & Thurs 9PM EST): https://www.facebook.com/knockoutsand3countsApple Podcasts: https://podcasts.apple.com/us/podcast/knockouts-and-3-counts/id1446923286Spotify: https://open.spotify.com/show/3OpvW0QHBe3uRc3D0pbORt?si=33935ad9669146d3Twitter/X: https://twitter.com/ko3cpodInstagram: https://www.instagram.com/ko3cpod/TikTok: https://www.tiktok.com/@ko3cpodMerch, Streams & Videos (Millions): https://millions.co/kyle-collisonIf you love what we do at KO3C, support us by grabbing merch or ordering a personal video at Millions.co.We go LIVE every Tuesday and Thursday at 9 PM EST — bringing you interviews, breakdowns, and the real talk you won't hear anywhere else.Want dope podcast clips ? Use our Opus clip Link : https://www.opus.pro/?via=Ko3C

YEAR 6 IS FINALLY HERE!  GO CHECK OUT OUR YOUTUBE TO SEE OUR BRAND-NEW INTRO!  You can find the animator using the link below! https://www.fiverr.com/syedahumna56/do-professional-pixel-art-animation-of-your-choice?utm_medium=shared&utm_source=copy_link&utm_campaign=gig&utm_term=AyNLxkP   *Intro includes minor edits not provided by the original animator. All animated assets were provided by the animator listed above, with some text assets added in post by Keeping Up With The Nerds.   Check out our affiliated links! Opus clips Partner link: https://www.opus.pro/?via=Nerd   Check out our Website: Keepingupwiththenerds.com   This week on Keeping Up With The Nerds, the gang dives into a jam-packed lineup of massive gaming leaks, superhero teasers, and the rest of the D23 announcements! First up, the Nerds break down the intriguing new teaser for Vision Quest. The crew discusses what this means for the titular synthozoid and how the series is shaping up to deliver a phenomenal finale to the ongoing trilogy. Then, the conversation circles back to D23 to catch everything missed last week! The gang dives deep into the remaining announcements, spotlighting the exciting updates surrounding Ahsoka Season 2 and what lies ahead for the galaxy far, far away. Next, the crew tackles the massive GTA VI leaks that took the internet by storm. As excitement reaches a fever pitch, the Nerds debate whether early looks like this do more good than harm—and question if these leaks are just reckless hacker antics for internet clout or something more. Finally, the show wraps up with a full review of DC's Lanterns. The gang breaks down the project and explains why the online hate is completely unwarranted—all this and more!

Michael Sobolik and  Gordon Chang examine how Chinese AI company Moonshot bypassed US export controls by "distilling" technology from Anthropic's Claude Opus model to train its own system, Kimi K3. This intellectual property theft threatens American market dominance and national security. Sobolik recommends three policy actions: imposing crushing financial sanctions on violating Chinese firms, closing export control loopholes related to remote cloud access, and banning open CCP models in the United States. He warns that American tech companies prioritizing short-term profits over security risk losing the AI race, mirroring historical patterns of Chinese piracy. (9)

Hugging Face explored a sale at $13B+, keeping the AI M&A wave rolling. Trump scolded towns that reject data centers while Abbott said the industry dug its own grave, Fable 5 spending plateaued, and Apple cut 200+ jobs. Links Sources: Hugging Face is exploring a sale that could value it at $13B+, up from $4.5B in 2023, and has been working with a bank to evaluate bidders' interest (Business Insider) Delangue has said Hugging Face is close to profitability and barely touched its 2023 round, and it turned down a $500M Nvidia investment at a $7B valuation earlier this year (TechCrunch) President Trump says communities that oppose data centers are "making a mistake" as they create "tremendous amounts of jobs and money", amid bipartisan backlash (Axios) Texas Gov. Greg Abbott says data center companies "dug their own grave" and deserve the backlash, after ordering an audit that has stalled roughly 1,800 projects (Fortune) Ramp data: Fable 5, launched in June, has plateaued at ~11% of spending on Anthropic tools, as companies shift to cheaper models; Opus 5 surpassed Fable 5 (Financial Times) Nvidia plans to use its $6B licensing deal with Poolside to build one of the world's most powerful open-weight models, to compete with DeepSeek and Kimi K3 (The Wall Street Journal) Sources: Apple is cutting 200+ jobs, including ~100 positions from the Vision Pro unit and another 100 from the Siri team, as it focuses on new devices and AI (Bloomberg) Subscribe to the ad-free feed.

The topic: Summer is over and school is back in session. So we thought we would ask our FilmWeek critics what some of their favorite school-set films are and what makes them so effective in bringing back those school-day memories. Our critics' picks: Cooley High (1975) To Sir, With Love (1967) Fast Times at Ridgemont High (1982) Fame (1980) The Last Picture Show (1971) Carrie (1976) The 400 Blows (1959) Election (1999) Booksmart (2019) Mr Holland’s Opus (1995) The critics: Peter Rainer, film critic for LAist and the Christian Science Monitor Tim Cogshell, film critic for LAist, Alt-Film Guide and CineGods.com Visit www.preppi.com/LAist to receive a FREE Preppi Emergency Kit (with any purchase over $100) and be prepared for the next wildfire, earthquake or emergency.

When we first dicsussed the Summer of Simulative AI in 2024 we knew it would be a brief summer, but it has recently come back with a vengeance with SimGym in April and now Simile AI's $2B Series B, backed by GreenOaks and Index Ventures with prominent backers like Fei-Fei Li and Andrej Karpathy, running tens of millions of simulations for Fortune 100 clients like CVS and 85–99% accuracy vs human focus groups. Time to catch up on why this Second Summer of simulation is working!From creating Smallville, the landmark 2023 paper on Generative Agents that showed AI characters could remember, plan, socialize, and develop emergent behaviors, to now building foundation models of human behavior, Joon Sung Park is trying to answer a much bigger question: what if we could simulate the world before making decisions in it? In this episode, the Simile co-founder and CEO joins us to unpack the path from generative agents to digital twins, why today's frontier models still fail to capture how humans actually behave, and what it would take to eventually simulate all 8 billion people on Earth.We go deep on Simile's approach to modeling human behavior: long-form interviews, observational and transaction data, randomized controlled trials, population-level and individual-level models, and post-training on the causal mechanisms behind why people make decisions. Joon explains how his research created digital twins that reproduced human behavior and attitudes 85% as accurately as people reproduced their own responses, why models optimized to be rational can be bad simulations of irrational humans, and why understanding “social physics” may require changing model weights rather than simply prompting frontier LLMs.We also explore the much larger ambition behind simulation: testing products and policies before deploying them, finding counterintuitive paths toward desired outcomes, modeling emergent behavior across entire societies, and potentially tackling problems like climate change, democratic instability, and UBI. Joon reflects on scaling laws for simulation, the economics of data-center-scale simulated worlds, the connection to Thomas Schelling and psychohistory, why simulation is surprisingly similar to painting, and whether we might already be living in one.We discuss:* How Smallville and Generative Agents led to Simile* Why Joon's team asked: “What if we can just recreate the world that we live in?”* Why useful personal agents require deep models of their users* Memory architectures, Markdown files, and the limits of prompting* “Social physics” and behavioral foundation models* Why web data captures what people say more than what they actually do* Interviews, transactions, observational data, and randomized controlled trials* Why predicting the future matters less than understanding how to shape it* How Simile creates representative simulated populations* Simulation versus prediction and the connection to Foundation's psychohistory* How to evaluate simulations instead of simply stacking LLM hallucinations* Creating digital twins of 1,000 real people and reaching 85% behavioral accuracy* Why frontier models can struggle to reproduce real human behavior* Why good simulations need to reproduce human biases and mistakes* Post-training models on randomized controlled trials* Population-level versus individual-level simulation* Scaling laws for human simulation* The long-term ambition to simulate all 8 billion people on Earth* Whether simulations could help solve climate change or detect collapsing democracy* Thomas Schelling and the history of agent-based modeling* Why future simulations could require an entire data center* Multi-agent simulations and what happens when simulated people interact* Replacing expensive human panels with synthetic populations* Why market research is only the starting point for simulation* Why Joon sees simulation as surprisingly similar to painting* Using simulation to study questions like UBI* Whether we are already living in a simulation* Why AGI and simulation may be the twin technologies of advanced civilizationsJoon Sung Park* LinkedIn: https://www.linkedin.com/in/joonspark* X: https://x.com/joon_s_pk* Website: https://www.joonsungpark.com* Simile: https://www.simile.comTimestamps00:00:00 Introduction and Joon's Path from Art to AI00:01:46 Smallville, Generative Agents, and the Origins of Simulation00:05:03 “Let's Just Create a World” and the Future of Personal Agents00:09:53 Social Physics and Behavioral Foundation Models00:14:08 Prediction vs. Simulation: How Do You Shape the Future?00:16:59 How Simile Models Real People and Populations00:25:35 Evaluating Simulations, Digital Twins, and 85% Accuracy00:30:23 Post-Training Models to Reproduce Human Behavior00:40:04 Scaling Laws and Simulating 8 Billion People00:43:10 From Schelling to Society-Scale Agent Simulations00:46:13 The Cost and Economics of Simulating the World00:52:05 Real-World Use Cases, Synthetic Populations, and the Market00:57:27 The Future of Simulation, Painting, and UBI01:04:23 Are We Already Living in a Simulation?01:06:08 Building Simile and HiringTranscriptIntroduction: Joon Sung Park, Simile, and the Story So FarVibhu [00:00:00]: Today, we have Joon in the podcast. Excited to kick this one off. Very exciting company. I wanna kick off and ask you the question, talk us through the story of your life. How have you gotten here?Joon [00:00:13]: Yeah, for sure. I'm really excited to be here. A story of my life. So I was born in Korea, and I lived there for a good 11 years or so of my life, and then my family moved to Boston. So we moved when I was 11, and my parents were doctors, so they were going through their postdoctoral studies. My dad was a surgeon, so he was doing his sabbatical years at the Boston Children's Hospital. So I grew up there, not too close to tech. I was very much a music and artsy, painting kind of guy.Vibhu [00:00:49]: Painting.Joon [00:00:49]: Exactly. I got into painting a little bit later, in high school, but that's what I used to do. And then I grew up mostly in the East Coast after Korea. So I lived a good number of years in New Hampshire, and then I went to college in Pennsylvania. And I got into more of this tech scene, in college. So I was originally trained to be an artist. I thought that would be my professional career. So it wasn't a hobby. It was like, “Hey, let's make a living out of this.” And then gradually, I got really interested in this idea of, hey, the greatest artist often creates their own medium, and the best medium that we had available today was in computation. So I decided to go deeper into that, and one thing led to another, and we can go deeper into this, but I decided that research was something that I gradually got interested in, and here I am.Smallville, Generative Agents, and the 2023 Breakout PaperSwyx [00:01:46]: So there's a lot that you packed into the research components. You had one of the best papers of 2023, which was the generative agents paper, commonly known as the Smallville paper.Swyx [00:01:58]: Feel free to call back to anything else that you mentioned, but most people would have heard of you from this. Do you have any statistics on how many people have, like, read it? arXiv gives you something, right? Some stats.Joon [00:02:10]: Yeah, it's a good question. How many people have read it, I'm not sure.Joon [00:02:14]: I know we do keep track of citations, and they are going up quite fast.Swyx [00:02:23]: Yeah, Google Scholar has 7,200 citations.Vibhu [00:02:25]: I feel like it made a bigger hit than that, and it was a pretty instrumental paper. It got cited so many times.Swyx [00:02:34]: It is frequently the answer when people ask, “What is the best paper you've read recently?” It's this one.Vibhu [00:02:39]: I thought the memory component was pretty underrated. It was a very good early memory system, and one of the biggest papers.Foundation Models and the Search for Killer ApplicationsJoon [00:02:47]: Yeah, so maybe I can talk a little bit about how this particular paper came together. So when I got into research, it was back in 2020 when I started my PhD program at Stanford, and that was the year, when we were about to get GPT-3 to be available. So we already had GPT-2, and you could sense that there was this new class of models that was just becoming available in the market, and the team got very intrigued. And the general consensus was, “Well, is this model going to be useful for anything?” “It's really strange that these models are not trained to do any particular task.” But we decided to take a bet. So a large group of scholars at Stanford, and it was led by one of my co-founders, Percy Liang, and we came togetherSwyx [00:03:35]: Who coined foundation models.Joon [00:03:36]: Who coined the term foundation models. We wrote this paper, where that term came from called Opportunities and Risks of Foundation Models. And during that process, really the thing that I started to think deeply about was, here is a model that is fundamentally new in our ecosystem. The reason why this was new was it wasn't, again, trained to do anything in particular, but its premise was it could do anything and everything. It was like a stem cell, if you were to take a biology analogy. And I got really interested in this idea that, well, if we were to really think about what are the killer applications that this particular technology would enable, what would that be? Many of my colleagues were using this for simple classification, simple generations. Interesting that these models can do that, but from an interaction perspective, not that interesting. We've known how to do that for many decades. And what we came down to was these models are trained on this very broad data from the web, right? So these are human behavioral data. It's social media, Wikipedia, all these data. So if you poke at the right angle, then you could see human behavior that would just pop out that's quite realistic, and we've never seen that before.The Time Machine Game and Recreating the WorldJoon [00:04:45]: So that got us really interested. The exercise that we decided to do, with this particular group of colleagues, Michael Bernstein, Percy Liang, and myself, who ended up becoming my co-founder at Simile, we sat down and we played this game that we call the time machine game.Joon [00:05:03]: Imagine we were to get on a time machine and fast-forward 10 years and look back. What would have been the single application that will have mattered that would be the most interesting and inspiring? And when we thought, “Well, what if we can just recreate the world that we live in?” it's really hard to get more ambitious than that. Like, let's just create a world.Joon [00:05:24]: And that's where we started. And initially, we had this paper that was a precursor to the generative agents paper called Social Simulacra.Swyx [00:05:32]: Before you go further, were there other candidates for the most ambitious thing in the time machine exercise? What was number two or number three?Personal Agents, User Models, and Why Simulation Came FirstJoon [00:05:44]: There is a close second that we were considering, which ended up becoming more of these automation tools, especially the vision around really personalized agents that would do things for you.Swyx [00:05:59]: That's also happening.Joon [00:06:00]: It's also happening. But it was interesting for us, right, in that the reason why, we decided to go with the idea of simulation, one, I was a huge science fiction nerd, and this idea of creating simulation, I was personally really just fascinated. I loved the idea. It's really cool to see, like, a game town like this and just see these agents live in it. But at the same time, my bet was if you were to create a really amazing personal assistant out of this technology, what you need first is an amazing model of your users. So I told a model, “Hey, can you go buy late dinner for me?” And it orders Hawaiian pizza, and I do not like pineapples on my pizza. Then it totally failed. The way for it to not make that mistake is only by having a deep understanding of who I am. And I gave a very simple and dumb example here, but you can imagine how this core understanding of people is instrumental. This is how, if we have our family and closest friends, they have a good mental model of who we are. That's the basis of our social connection. So our bet also was this technology around simulation, creating accurate representation of people ought to precede the more complex agents that would automate the world that we live in. So that was the bet. But that was a very close second, and I'm still very much fascinated by it. I think there's a lot of interesting work that's going around. My hot take here, though, is I don't think we've seen a true personal assistant that's useful, in ways that meet the ambition of that particular line of work. I think there are early applications that are interesting, and if you talk to even ChatGPT nowadays or Claude, they know a lot about us. So a lot of the generation it's doing, I do think it's much more tailored, but I think the ambition is quite large in that field, and I don't think we quite have all the right ingredients just yet.Swyx [00:08:01]: So OpenClaw and these personal agents, what do you want to see from them that they don't currently have?Memory, Markdown, and the Limits of PromptingJoon [00:08:09]: I do think it's slowly getting there, but I do generally want them to have much deeper understanding of the person. Right now, you look at the models. OpenClaw, what it's leveraging is a Markdown file, and I think it's quite clever, right? So if you look at the generative agents paper, this was the same intuition that we had, where initially when we were creating the memory architecture for the generative agents, and, like, this is, like, back in 2022, so we didn't really quite have the idea of even agentive architecture or the term agent. But the intuition that we shared with some of the work that's coming out today was we initially thought, “Well, do we want to make the memory into, let's say, knowledge graph? Do we want to train a bespoke model?” All of these things. And what we decided to do was, “No. Just forget about all this.” These language models are quite good at modeling text and understanding and reasoning about text. So just put everything in a Markdown file or a text file. You're done. I thought that was quite interesting that we could do that, and there's a lot of strength in doing that. But also, there are limitations. It's the way you retrieve and make sense of data that's extremely large, it takes a lot of work. So I think that technology is getting better. I also do, however, think, there are certain things you just cannot shape just by prompting the model. So to some degree, you do need to touch the parameters of the model itself. So there is this work that I do think does need to happen, and it is happening. The question is, how far can we take it? How do we source data, and how do you also create an ecosystem where people are continuously feeding data to this model so it's learning about you?Vibhu [00:09:50]: What's the intuition between why you need to do it in the model?Social Physics and Behavior Foundation ModelsJoon [00:09:53]: My intuition behind the actual when do you train or even post-train a model versus just prompt a model is if the model has to learn the underlying physics of the world that it's operating in. So it has to learn new social physics. The places where it doesn't have to train are the places where it already has the physics. We trust the physics. It already has the base statistics, but it's just trying to react to an environment. Then I think you can just prompt your way into getting the actions out of it. I don't think the models that are out in the open have yet learned the complete mapping of social physics of humanity. This is one of the core theses of Simile, right? And one of the core reasons why that is the case is if you look at the data that the model was trained on, these models were trained on the web data, like, whatever was available on the web. And these are really interesting data sets, but they are fundamentally the self-exposed attitudinal data with some behavior data that's sprinkled around here and there. And it has yet to learn the really deep behavioral nature of people, not just what people say they do online, but what they do in real life. And this is one of what I would consider to be the dark knowledge of humanity that we haven't quite captured. And it's these data that would also need to get factored into the model creation.Vibhu [00:11:21]: You call it behavior foundation model.Vibhu [00:11:23]: There's a good one-liner here, but outside of that, what type of data do you need? What are you changing on the model level? How do you go about modeling, doing a behavior foundation model?The Three Data Buckets: Interviews, Behavior, and CausalityJoon [00:11:35]: We think about data in three buckets. So one bucket is interview data. It's quite interesting. Rich qualitative data is interesting. It's not behavioral, but we would literally ask people, “Hey, tell me the story of your life.”Vibhu [00:11:53]: It's just what we're doing here exactly.Joon [00:11:54]: The question that you all asked at the beginning of this interview literally is the question we also ask. And we ask our participants to go a little bit deeper, than how far I went. Maybe I can give more of my life story in lieu of this. But the reason why that data is interesting is by learning about this very long-tail information about people, you get a lot of texture around this model, like, this person as a model. So even understanding their childhood memory or even their trauma, their first love, these things, quite informative in ways that's really hard to predict. So that's one. Then there are two tranches of what I would consider to be the behavioral data. One kind of behavioral data is observational. So these might be like transaction data, or these might be data that you can get by scraping the web, right? So you can imagine why these data sets would be interesting, right, because they give you the base statistics of people's behavior.Joon [00:12:55]: But then there is the last category of data, that I personally think is perhaps the most important, which is the data that describes the causal mechanism, the whys of people. Some of this is covered by the interview data, the qualitative, because people talk about why they made certain decisions. But really, where you get to see the most behavioral aspect of this is in randomized controlled trials, like RCTs. Imagine you have the same setup, but you have a few different variables that you are trying to tweak. Can you get realistic human behavior out of it in ways where, imagine you had this particular option. Imagine you're even trying to choose whether you're going to drink coffee or not. The day you drink coffee versus the day you didn't drink coffee, does your behavior change? That's a data set that describes a causal mechanism. This is quite important in modeling people. The reason why this is important is oftentimes when people come to us, or not just to us, but the reason why people are interested in simulation isn't because they want to predict the future. If you're trying to win against the stock market, predicting the future is interesting.Prediction vs. Simulation: Shaping the FutureJoon [00:14:08]: But most people, most decision-makers, what they want to know is, how can we shape the future? It doesn't really help you to hear that your sales are going to tank in two quarters. They're just gonna say, “Wow, that sucks.” What they want to know is, well, what do we need to do now to avoid that future? That's the causal mechanism. And this is also very hard data to come by, right, because the world is our ground truth, but it happens once. So in a very controlled setup where everything is equal except for one variable, this kind of data set rarely happens. So this is a reason why this data set is both hard to come by and quite important if you're trying to model human behavior.Swyx [00:14:50]: So behavior, I think, is the hardest data set to acquire. What is out there? What is even possible? You're not going to know a lot of details about my life. I don't even have data for myself on my own health or habits, and I just don't log everything. So how can you have that data?Joon [00:15:14]: So we run a lot of randomized controlled trials.Swyx [00:15:17]: But you put people in the lab, they watch them sleep, or what?Joon [00:15:20]: We do care a lot about the consent process. People know that we invite them to be a member of this community to both share data and have themselves represented in different forms. But we bring a lot of people to the lab, or virtual lab, where we design experiments that would pose them real behavioral decisions. And often in these experimental setups, what makes the difference between what is attitudinal versus behavioral is whether the stake in your decision is real. That's ultimately what makes it behavioral. So in these setups, we are inspired by our colleagues in social sciences, psychology, and so forth. So when they run studies, the techniques they utilize is imagine there's an online store that you're inviting people to come by. Then whatever they purchase in this experiment, they actually get that item delivered. Like, these are the things that make the stakes real. So we run a lot of these experiments, and we also do partner with firms. Right now, we also have customers who are quite excited to at least give us a glimpse of the behaviors that their users exhibit so that we can get a little bit deeper understanding of how people behave in these different platforms.How Customers Use Simile: Populations, Queries, and ExperimentsVibhu [00:16:39]: I think on the customer side, they have a lot of data about their users, who has bought. They have the action data.Vibhu [00:16:47]: Can you walk us through an example of what someone comes to you for? What questions would they want solved? Do you customize a model for them? Do you have something off the shelf? What does that look like?Joon [00:16:59]: Today, when people leverage our models, it's often to better understand the population of their interest. So usually, the start of the relationship, we come together and hear about what population they want us to model, right? So it might be that if you're a CPG company that's selling to all of the US, then maybe it's fairly straightforward. You want to model the gen pop of the US. But at the same time, if there is a vertical or if there's a market that they're trying to go into, imagine, they want to better understand, let's say, people in their 20s and 30s living in California. That's a much more specific population. So we hear about this population, and we go recruit these people, with consent, and with incentives, and we collect some of their data and create a model of these people. Then what our product allows you to do is query them. So it can take as input a filter that is a description of the population that you want to talk to, just like the one I just mentioned, and an environment. The environment can literally be survey questions, behavioral experiments, It can be A/B testing. Oftentimes, the core use cases are things like concept testing, to start with. But also, people sometimes want to do focus groups or one of the fun use cases that we also serve is even modeling things like earnings calls for public companies.Joon [00:18:21]: So these are the use cases that we often start with.Swyx [00:18:23]: Concept testing, is that an established term? I've never heard of concept testing.Concept Testing, Gallup, and PoliticsJoon [00:18:27]: Yeah. So it has to do with they have, let's say, different messaging, different products, different ideas.Swyx [00:18:32]: It's like a marketing exercise.Swyx [00:18:33]: Okay, got it. Got it. Politics?Joon [00:18:36]: We do, have a strategic partnership with Gallup, and of course, Gallup is deep into policy space and so forth. Right now, we have not worked deeply with politics, like that area just yet, however.Swyx [00:18:49]: I'm curious if there is demand or if they really would have different needs that somehow fundamentally don't mix with your existing, users or people.Joon [00:19:00]: I think there's certainly demand.Joon [00:19:02]: But we are very much mindful of how this technology gets adopted and the societal impact that we'll end up having with this technology. And I do see politics as an area where a company has to be particularly thoughtful about the way they operate and make impact. So this is where we also want to make sure that we form enough of guardrail and perspective on how to leverage this technology before we go on to serve markets like the politics.Swyx [00:19:29]: I'll give people an example. one of my favorite shows is The West Wing. I don't know if people have watched.Swyx [00:19:34]: One of the key storylines is, like, the president has, multiple sclerosis, but they haven't. they need to figure out how to disclose it. So they run a poll with a fake governor and ask people to respond on the poll,Counterfactuals, Polling, and When Simulation Is UsefulSwyx [00:19:47]: They try to make decisions based on the results of that poll on, like, how well they'll be received, like where, how should we play this?Swyx [00:19:54]: And I'm like, well, I think those counterfactual things, I would use a simulation for this if I could trust it.Joon [00:20:01]: For sure.Joon [00:20:02]: In that show, how'd it go?Swyx [00:20:04]: In that show, it was, like a foregone conclusion. They were like, “We know it's bad. We just don't know how bad.” And then the poll came back. It was like, “It's really bad.” And then they just did it anyway.Joon [00:20:14]: Part of it is to show, right? So you're, you're looking at the ideaSwyx [00:20:17]: Maximizing drama.Joon [00:20:18]: How bad could it be? Oh, it's horrible.Swyx [00:20:20]: And to some extent, I think that is part of the trick of the, or the challenge or with being a customer of yours, which is that if I know it's. if I roughly know and can intuitSwyx [00:20:35]: What the effect is going to be, do I need you? What sensitivity of it, of effect do I need in order to make a decision, right? So for example, if I, my approval rating is 50%Swyx [00:20:48]: And I, they have this negative piece, news item comes out, and it drops to 30.Swyx [00:20:52]: If it drops to 20, if it drops to 40, do I care? No. It, I know it drops. It's negative. So when do I care about simulations?Joon [00:21:01]: You do something that's clearly bad, that's not popular, and people don't like you, like, yeah, it's likeSwyx [00:21:05]: You don't need a simulation.Joon [00:21:07]: Yeah. Well, so there are a couple of things. one is, there are use cases where, like every day, developers, designers, policymakers, marketers, every single day, they create assets. They create new products. And turns out, it's many of the decisions in hindsight is obvious. Yes, of course this is bad, but we still run those studies because understanding the magnitude and understanding how acute something is quite difficult, even if, we feel like, of course, like this makes sense. this is the reason why we make so many mistakes. Like, every time somebody goes online and say something that has huge backlash, you look at that and like, “What an idiot.” However, it's tough. That's one. There's also another aspect here, which is, again, this is the reason why simulation is different from prediction. In simulation, in the ideal case scenario. So what simulation is trying to show is it's trying to show each step of the way or each step that we need to take to get to a certain outcome, right? So in the most advanced simulations, sometimes the next step that we're suggesting might be quite counterintuitive. The analogy that I sometimes give, and I ground it in a more realistic example, but, I, as I mentioned, I'm a huge fan of science fiction, and I don't know how, many of the audience members have read, like, things like the Foundation series by Asimov.Simulation as a Path, Not Just a PredictionSwyx [00:22:37]: Oh, yeah. We've mentioned psychohistory a number of times.Joon [00:22:39]: Okay, fantastic. So I might be, talking to the right crew. If you read Foundation series, literally the first act is there's a group of scientists who have found out that, “Oh, our galactic empire is going to collapse, and we're going to have 30,000 years of unrest.” And they run psychohistory, the simulator that tries to teach them, “Okay, how can we keep this unrest to a 1,000 years?” And they plan this out, and the first step of that plan is to get the scientists who say, “Okay, this is coming,” exiled into this random place in this, galax- galaxy.Swyx [00:23:18]: Terminus.Joon [00:23:19]: Exactly. And that's so counterintuitive. Like, what a strange move that you literally sent the group of scientists who was raising voice around this potential collapse of galactic empire into nowhere. How is that the right first move? Well, it turns out in this particular simulation, that was the move.Joon [00:23:40]: It's these things, right? And the reason why these reasoning is possible is because you're showing the step function or each step that results in a particular outcome. So really what simulation allows you to do in its highest form is you give it not a problem or question, like what would people answer to the survey? That's not what we do. What we tell it is, “Here is a goal that we have. In the context of foundation, we want to keep the unrest to a 1,000 years. What is the path that we need to take now to get to that particular future?” And that's what simulation allows you to do. Now, translating that into real market, imagine you're a automobile company and you're about to release a, EV, and you're trying to understand, well, how do we market EV, to make sure that our stock price goes up? But what if the answer comes down that, well, you can market your EV in XYZ way, but that might change people's perception around the cars that's not EV and make your overall sales to go down. Not very intuitive, especially all you're trying to optimize is EV salesss, and that's the only thing that you're tracking, then that might result in a completely wrong solution, or at least different solution than what you would have expected, whether it's right or wrong.Joon [00:24:57]: That's the power of simulation.Swyx [00:24:58]: For listeners, we covered a similar topic with Mikhail Parakhin from Shopify, where they are working on SimGym. I don't know if he ever talked to you about it. it's very similar.Joon [00:25:07]: ISwyx [00:25:07]: The goal is increased conversion, but then the journey is very unusual.Joon [00:25:12]: Journey is unusual.Swyx [00:25:12]: Yeah. The-- He's trying to look for interventions on a shopping trajectory, which is similar to what you're saying. Like, it's not about the attitudinal, is your word for it.Swyx [00:25:24]: It's about behavior.Joon [00:25:25]: It's about behavior.Swyx [00:25:25]: And that's exactly the difference, right? It's, like, not about the near-term direction about-- but it's more about, like, how do you affect multiple turns of interactions.Vibhu [00:25:35]: You had a good quote at the start about this as well. It's not about people wanting to know the outcome. It's about how they can change it, change the way to get there, something like that. But I wanna take it back to how do we know this is grounded? LikeGrounding and Evaluating Digital TwinsVibhu [00:25:47]: How do you run evals? How do you test that simulations come through? if I was to do the same thing that you described with, say, your favorite LLM, Opus, GPT-5.6, have some agent to map out these thingsVibhu [00:26:02]: How different are the answers we would get if I give it the same goal, the same objective, make a decent system? You're saying that you need to change the model weight. You have your own solution to this. But how far off are we, and how do you check if it's grounded? you have some interesting stuff on your site that points to how you run real evals, but if you could take us through that side. I think that's one of the big concerns that people have. They're like, “LLMs hallucinate.”Vibhu [00:26:27]: “You're just hallucinating layer after layer,” right?Joon [00:26:30]: The way we do this, and this is the paper that we worked on after the generative agents paper that really became the, at least for Simile and also the field of simulation and synthetic panels, really became the foundation. Yeah, this is the paper. the paper is called Generative Agent Simulations of 1000 People. Here's what we've done. For this paper, we brought 1,000 people that's representatively sampled from the US to a virtual lab. And what we have done was we spent two hours collecting fairly wide-ranging data. In this particular study, we focused a lot on this interview data, that was, whose script was taken from this project called American Voices Project. And then we would also pair that with a lot of behavior data and so forth, whatever we can collect within two hours. And then we would send these people away for a couple of weeks. And during that time, I would use this data to create their digital twins. And I would bring the humans, participants back after 2 weeks and have them complete a battery of surveys, experiments, behavior studies. So we have the list here, which included things like behavioral economics games. We would run literally, like, Big Five personality test, General Social Survey. We would also go ahead and run the randomized controlled trials that were published on PNAS. And we would have their digital twins predict how the source individuals would have acted in these studies and surveys. And this is where we could replicate people's behaviors and attitudes 85 percent as accurately as people would replicate their own. So that was the first really paper that gave this validated results that we can model individuals in an accurate way. And what we ended up finding now, of course, in AI space, so this paper came out at the end of 2024. AI space, a year and a half, 2 years, that's a lifetime.85% Accuracy and Why Frontier Models Miss Human BehaviorSwyx [00:28:24]: Yeah. Just, for listeners who are not seeing the YouTube, I just wanna say, like, the headline figure is 85 percent accuracy, like, which is a big improvement over all the otherSwyx [00:28:34]: Methods that you showed.Joon [00:28:36]: But the part that was particularly striking to us, especially as we improved this technology even further, was the generative AI models like ChatGPT, Claude that's coming out, it does give you the right foundation. However, what they do not consider is the true attitudinal and behavioral aspect of people, especially in the population that you care about. So what these models are really good at today is they're trying to become the super rational, objective machines, right? So you go get their data from places like Mercor, Scale. You talk to professional programmers, scientists to create model that's amazing at reasoning. That's what they do. Simile doesn't care about any of this. The models that we're talking about here, what we're trying to create are models that are as dumb as I am, right? So if I make some mistakes, the model has to make the same mistake.Swyx [00:29:34]: Oh, that's very hard.Joon [00:29:35]: That's very hard.Swyx [00:29:36]: You're solving Murphy's paradox.Joon [00:29:37]: That's exactly. And this is a completely different data and training objective. This is also where we see quite a bit of discrepancy in the performance in human behavior prediction between the frontier models, Simile's model, and the models being created in this space, where in some cases, the model performance of frontier models go all the way down to 20, 30 percent, especially if you go into that more niche population on topics that our customers would care about. On more gen pop, it might be around 50 to 60 percent. So it's not very robust. Like, you wouldn't want to make your decision off of these and these findings. If you can bring that up to 85 percent, that is ultimately what people end up getting very excited about.Swyx [00:30:20]: Yeah. Do we wanna keep going on the paper, routes?Joon [00:30:23]: Yeah, for sure. So the last one, was an interesting one. So this, paper was the follow-up paper that we had, to the 1000 agents paper, where the idea was now can we augment the models even further and post-train a model based on a lot of randomized controlled trials? So this was an interesting one. The data is always the most interesting part of modeling in many ways. The data that we got here was there's this, there's this platform called Open Science Framework. So some, the audience might be familiar with this. And there has been, especially in the social sciences over the past 5 years or so, there has been this concern around replicability of studies. And so it was a bit of a crisis, the scientists acknowledged, where we rerun the study and we don't see the same finding.Post-Training on RCTs and Replication StudiesVibhu [00:31:12]: Oof.Joon [00:31:12]: It's tough. And the reason why it's there-- that was often the case was there's this survival bias where the papers that get published often need to maintain what we call the value of less than 0.05 in the experiments that we ran. That suggests that only-- there's only 5% chance that the results that we saw is false positive. But the tricky part was all the papers that were not published, and there's still a 5% chance that whatever we publish is totally just randomly generated. Like, there's a 5% chance that, hey, this effect is not real, but it just happened to be real because of the sampling bias. So because of that, what scientists started to do was they started to register their studies. So before running an experiment, they would go to this platform and say, “Here is the data. Here is the population that we're collecting, and here's the hypotheses.” And they would just say, “Here is our hypothesis.” Like, “This is what we believe.” And you cannot retroactively change those hypotheses. This is what gives us more scientific statistical confidence that whatever effect that you ended up seeing is true. So that ended up creating this really interesting platform where there's one platform that has now contains tens of thousands of real-world experiments and hypotheses. And a lot of these are really high-quality, like, professionally designed behavior studies and random- randomized controlled trials. So we got the data and the studies from this platform and used that to make a point. And this particular, model is not, something that we're serving commercially because this was a part of the open science. But this particular data set, helped us make a point that by collecting a lot of these randomized controlled trials, that are really well-designed, we can make significant improvement in model's capability to predict human behaviors. So that's what this paper was about.Vibhu [00:33:10]: Is this stuff done on a individual level? Like, do I need to tune the model per individual, per company? Is there foundation model changes and then some slight post-training? Anything you can share there?Population-Level vs. Individual-Level ModelsJoon [00:33:21]: So this particular model was trained. the data we had at the level of individuals, but this particular model was trained. We experimented with both. And this is what we end up doing at Simile too. We always train 2, distinct model. One is what we call the population-level model. The other is what we call the individual-level model. And both take very similar input, which is the description of a subpopulation or individual and a stimuli. In this particular work, we've done the same. Here, the results that we are reporting are much more geared towards individuals because we do think that is a harder task in many ways, but that's what we have done.Vibhu [00:34:02]: You seen anything on the questions that humans can solve that models can't solve? So likeHuman Biases, Mundane Choices, and What Models MissVibhu [00:34:09]: Currently, it's, I live 5 minutes walk away from a car wash. It's a 10-minute drive. Should I walk or drive?Joon [00:34:16]: Huh.Vibhu [00:34:16]: The model will say, “Oh, walk to the car wash.” And, you don't have your car.Vibhu [00:34:20]: Is anything like this a problem in simulation? You would assume, like, very simple for human to think about, but if the model is saying you should walk to the car wash, anything here?Joon [00:34:32]: It's less, what can we solve, but I think it's more about what biases or mistakes do people make that models miss. Like, imagine that you are, like the. When I was still at Stanford, I lived in Palo Alto. So it's about, I would say, 40-minute walk from the campus. You ask the model, “Okay, let's go home. What can I, what can I do?” It would likely call an Uber or, give me, the bus time. But for the longest time, I really liked walking back. And the reason why I wanted to do that was not for efficiency. It really helped me think. And I like to walk for, half an hour or 40 minutes or so a day, where I just get to, just think about ideas, research, just get lost in my thoughts. That's very human activity. Unless the model has seen that and understands the importance of that activity, it would miss these kinds of features. So that I think, is fundamentally what we're trying to model. Like, what is fundamentally human might not be the most efficient thing to do, might not be the right thing to do, but things that make us who we are.Swyx [00:35:43]: I'm curious if, there are some data sets that you really want that would materially help you. One version of this may be interesting, which is more valuable to you to acquire as a data set, all of LinkedIn, all of Twitter, all of Facebook?What Data Matters: Social Media, Transactions, and FacebookJoon [00:35:57]: It's a little bit hard to rank, in part because, there's, there's this product saying where no feedback is wrong because it teaches you something about your users. Doesn't matter what feedback.Joon [00:36:11]: I think it's a little bit like that.Swyx [00:36:12]: So just whatever is bigger.Vibhu [00:36:13]: What about a different domain? Say it was. What about all of Amazon data?Joon [00:36:17]: Oh, yeah.Vibhu [00:36:18]: Shopping data, right?Joon [00:36:18]: Shopping data. So Amazon data is interesting in that it's very much behavioral, although, like, what people do on social media, you could squint and say that is also behavioral. But the transaction data is always interesting. It is also most commonly available, however.Joon [00:36:33]: If we were to look at purely social media, like if you really, if I were, if I had to really pick, Facebook likely is interesting because I do think it is most a default version of people. Because you go to LinkedIn, it's very much professional environment. So people put up their, they have their guards up, right? And that still is interesting because that is true human attitude and behavior, but it is not your base state. you go to Twitter- Twitter, people have their own crazy personas, or depending on who you are. Like, my Twitter profile and, persona is very much, initially was I was very much an academic. “Hey, I'm here to share my studies.” Now, I share, things that's related to Simile. But Facebook is one of those more private space where people just connect with their friends. In that way, I do think it shows you a little bit more about who that person is. So if I had to pick, I'd likely pick, Facebook.Swyx [00:37:30]: Yeah. And you're interested in, like, the whole person and their background and philosophy. I, is it too clinical or too machine learning-oriented to just say this is just ways to inject variance and biases? The broad question, is, like, is this any better than a randomized, like, combinatorial explosion version? So we have a link to the TencentBillion Personas, Synthetic Demographics, and Bespoke DataSwyx [00:37:54]: Billion persona paper, where they did not do any of the groundwork that you are doing.Swyx [00:37:59]: They just did like a cross matrix of here's all the professions in the world, here's all the people, possible backgrounds in the world, do a dot product across all of them, and that's it. That's your prompt for a billion people.Swyx [00:38:12]: This will do something. I don't know if it'll do what you do, but it gets you some way, some percent of the way there.Joon [00:38:18]: So this was an interesting paper. Like, what I admired about this paper when it came out was the scale. And you do gradually want to be able to simulate really large societies and interactions. So the scale is definitely admirable. it is relying heavily on the known statistics that went into training the model. So to the extent that you believe that statistics is correct, this is not a bad way to go about this. But the thesis here, and this is something that we also have seen in the market, like if this works, then we have solved simulation.Joon [00:38:54]: It,Swyx [00:38:55]: Because I survey, like, okay, 5% of the US population is in construction.Swyx [00:39:01]: The other 5% is in medicine, whatever, right? And then you just keep going down the list, and then you do the other side. 5% has, like, the big 5 personalitySwyx [00:39:08]: Of, like, neurotic or whatever. That's it.Joon [00:39:11]: That's it. So if you believe that the underlying data set and the platform that we're leveraging has all the right statistics, then this will have solved it. you're at that point merely retrieving the knowledge that is already embedded in the model, in the model parameters. That's not, unfortunately, what we see, where there is such detailed and also niche knowledge about people that if you just take one example, it might feel very mundane, but it's quite rich when you put together, that you do need to do a lot of bespoke data collection to better understand people. And this is also, I think what makes this particular, job fun, which you want to deeply understand people, and the process of deeply understanding them requires a lot of attention to the details. And you do need to pay attention to and pay respect to the daily lives that people lead.Scaling Simulation: From Thousands to SocietiesVibhu [00:40:04]: I wanna talk about scaling simulation.Vibhu [00:40:07]: So what can't we simulate, what can we simulate, and how does scaling affect this? So how big are the models? What if we go from, 8B, like, couple 100 billionVibhu [00:40:18]: Like billion000 parameters, billion000? Do we get scaling? Any interesting emergence? Like, at a certain scale, at a certain amount of training, you uncover anything unusual and any learnings from that?Joon [00:40:31]: What we are seeing is at Simile, so we do post-train our own model. The thing that we're seeing is the early glimpse of scaling law in simulations. The more data about humans and more compute you ingest, you start to get predictive and predictable gains of the model performance in simulating it, simulating people.Vibhu [00:40:51]: Ooh. We need a scaling law curve.Joon [00:40:52]: It's scaling law. Whenever you find it's a beautiful thing. And we're starting to see the glimpse of it, which is quite exciting. But if you talk about the ambition of simulation as a whole, it's not merely about building a model. It's about building a model, then creating the agents that become the individuals in a much larger ecosystem. So they're creating this multi-agent simulation. Down the line, you want these multi-agent simulation to also live in a very rich environment, right? What we are really trying to get to at that point is, hey, can we create. All right, let's do a time machine game again, and 5 years, 10 years into the future, can we create a simulation of 8 billion people living on Earth? I think that's quite interesting. And that really is the vision. And once you get to that state, the questions that you can help answer for the society also start to change from my perspective. The answers are fundamentally about emergence of the emergent behavior of society and large groups of people.Joon [00:41:53]: So the questions that I get excited by, and maybe this is a stodgy- a bit. I have my, academic side of me.Joon [00:42:01]: And for me, it's questions like, can we help solve climate change? If you look at climate change as a problem space, this is what we, like social scientists would often call it the wicked problems, problem where you have many actors with competing incentives for trying to make a very complex decision and coordinating that coordination decision. Very difficult to really solve in real life, which is also the reason why we couldn't solve it. Can simulation help us solve that? Another one is, can we understand the signals for collapsing democracy, or can we understand or can we uncover the origin story of the monetary system? These are societal questions that we never really had a good way of answering. If we can create simulations of our society, you have to believe that these are the problems that we can solve. So that's really the ambition of this field. And, I also think, yes, I think there's a Nobel Prize to be won there, which wouldn't be surprising. And I think there's some amazing societal impact that we can have to help people make better decisions.Climate Change, Democracy, and Societal SimulationSwyx [00:43:04]: Nobel Prize in economics?Joon [00:43:06]: In economics.Swyx [00:43:06]: Oh, I see. I see. Rooting for you to write that paper.Joon [00:43:10]: One of these days. But, one of the scholars that I was deeply inspired by, When I was coming into the space of simulation, is this scholar, named Thomas Schelling.Schelling, Agent-Based Models, and the Nobel PrizeSwyx [00:43:23]: Schelling point?Joon [00:43:24]: So the canonical example of the work that he's done was he was one of the creators of agent-based modeling. So this was, like, in the 1970s and 80s. It's very early days, but this was truly one of the first exemplars of simulations. And one of the canonical model from that time, and of course many of these simulations are trying to tackle the societal problems that's most relevant for their era, it was called the model of segregation. So racial segregation was a big topic, that, we cared about. And what they've done was they created this grid world where they had red dots and blue dots. And these dots were, back in the day, like, they were the agents, and they had a simple rule that governed their behavior. If certain percentage of your neighbors are of different color and if that goes above certain threshold, then you move to a new location at random.Joon [00:44:21]: One of the striking finding of this paper or this agent-based model was for the longest time, people thought the segregation within society was caused by explicit and overt racism.Joon [00:44:34]: But if you look at this model, people's preference towards living with people of the same color, that preference can be very minute.Joon [00:44:42]: But the very small difference causes the society to segregate completely over time. This was very counterintuitive for a lot of people. And this particular work ended up informing housing policies. Mixed income housing, got really inspired by this work. And Thomas Schelling ends up winning the Nobel Prize for having laid the groundwork for very early versions of simulations. The opportunity that I do see here in the more scientific terms, is agent-based models for the longest, had impact in the 1980s, 90s, to some extent, early 2000s, but it has now gotten forgotten by the community a little bit. Because as you can imagine, red dots and blue dots is not really a rich description of people.Joon [00:45:31]: But with the emergence of things like generative AI and, in particular, generative agents, we do have an opportunity to create these agent-based models that are high fidelity enough to help us make really complex decisions. And that's the opportunity that I see. If that truly works, then yes, that is the work that will result in a Nobel Prize.Swyx [00:45:53]: Yeah. For what it's worth, and I grew up in Singapore. 80% of Singapore is in public housing, and public housing has, enforced racial quotas for exactly that reason, which is very interesting. okay, so we talk about scaling, we talk about all these, the agent possible applications.Cost, Reuse, and the Economics of SimulationSwyx [00:46:13]: I'm scared about the cost. if you even-- let's just keep it to the US, about 8 billion people.Swyx [00:46:21]: But, how much does it cost to model so many hundreds of millions of people?Joon [00:46:26]: Oftentimes today, we don't start at that scale, this stage of the, of industry and simulation as technology. But we can get our users extremely rich and meaningful insights even by modeling thousands, tens of thousands of people. And today what we do is every week we are collecting data on the scale of tens of thousands people's data, and we have panel partnerships that gets us to tens of millions of people globally. So that's what we do today.Swyx [00:46:55]: And just as a side note once you've collected one person for one studySwyx [00:46:59]: Can you reuse that same person for all the subsequent studies?Joon [00:47:03]: That's exactly right.Swyx [00:47:03]: Okay.Joon [00:47:04]: The beauty of this model and these agents is the fact that they are domain-agnostic.Joon [00:47:08]: That what you're really trying to understand is what is the fundamental nature of these people? What's their social physics? And there are a lot of, a lot of, people that does change over time. Like, even, like, even things like, how many times have you gone have you been to, like, CVS the past week? that will change. But there's so many traits about people that are also known to never change. Like, your risk tolerance doesn't really change over time. It's very consistent. So it's these things that we're trying to learn. But the scale we are operating is right now hundreds or, tens of thousands to hundreds of thousands. And in many of the core use cases that we are deployed in, and this is more than enough population, to cover those. Really, at that point, what you care about is less the number of people, but more do you have the right subpopulation of interest covered? And this is also the reason why people want a larger sample. It's not because they want, stronger statistical guarantees. It's more that can they filter down to any population of their interest. However, you can also imagine in 10 years, if we truly believe that the compute is going to scale, that we'll have much more availability for compute, and our ambition for simulation is also going to scale accordingly, there's definitely a reason for us to create an entire data center worth of simulations.Joon [00:48:35]: Or in my hunch here is I do think in the next some number of years, we will start creating simulations that will cost as much as training a foundation model. But perhaps it's going to be so valuable to the society that it would be a no-brainer. Right now, even today, like, we are training bunch of new foundation model just so we can say we trained one and we spent tens of millions. But if we can create a simulation at the level of society that would solve climate change, I would run that today. I would raise the money right now just to run that.Multi-Agent Simulation and Social InfluenceSwyx [00:49:10]: Amazing. the follow-up question is, does it also compound if you let the simulations talk to each other?Swyx [00:49:18]: Or do they already do that today? They don't, right, as far as I understand?Joon [00:49:22]: It depends on what simulation you're trying to run.Joon [00:49:24]: In the multi-agent simulation setup, the agents do talk to each other.Swyx [00:49:28]: Right, which is exactly Smallville, right?Joon [00:49:29]: That's right.Swyx [00:49:30]: But a lot of times, for example, in commerce, you're just by yourself, so there's no point talking. which is way cheaper.Vibhu [00:49:37]: But they use all these levels, right? Like, you decide what you will buy based on what other people around you buy and talk about, right?Swyx [00:49:43]: It depends.Vibhu [00:49:44]: It depends.Swyx [00:49:45]: Again, I'm, I'm coming at this from a cost point of view. I'm like, “Oh my God.” LikeVibhu [00:49:48]: I thinkSwyx [00:49:49]: If there is, like, some combinatorial thing of, like, thousands of people talking to thousands of people, then that one million X's might cost.Vibhu [00:49:56]: I have a very different view as the cost point aside. Like, running these studies in reality is a lot more expensive, right? Running any study like this is you gotta have people do it, you gotta sign people up. It's very expensive and sometimes, like, not feasible to run the study.Vibhu [00:50:14]: But the outcome or the decisions you make are very expensive on them, right? So spend X million on something that, the overall process costs 100 million might as well, right? There's, there's a lot of value to be had there. It's a small cost, but I'm excited on the cost side.Joon [00:50:33]: To some extent, and when you deploy technology, you often want to deploy in a way where you can replace existing budget or you can make things more efficient, and that is the best way to deploy. However, the way you capture the long-term value of the technology is making the argument that, no, it's the upside, that by making this better decision using simulation, you have saved yourself or made yourself hundreds of millions or even billions of dollars, and that's a case to be made.Vibhu [00:51:06]: Random tangent question. So if you're doing a lot of inference, a lot of model multi-agent stuff, are you at the point where it makes sense to, train a model that' very sparse? You're expecting to do multi-million dollar runs. Are you thinking about this in model architecture standpoint or inference efficiency, or, you're still at the research phase of it works, we're not super there yet?Joon [00:51:34]: Efficiency, we do think quite a bit about. this is technology that is deployed now in some of the largest enterprise companies in the world, and we do process significant number of queries, that are trying to, simulate the populations in the world. So efficiency is a consistent thing. we don't want to over-optimize too early, so I wouldn't say, like, this is the higher bid Right now, but this is definitely something that we think pretty carefully about.Swyx [00:52:05]: Yeah. Are there other case studies? So we, you talked about CVS, talked about Gallup, Deloitte, Wealthfront.Efficiency, Enterprise Use, and Real-World Case StudiesJoon [00:52:12]: Wealthfront is an interesting one, because one of the things they were trying to do, they were one of the first customers that wanted to do product testing that goes beyond just asking people what they think about, let's say, behavior experiments and so forth. So there, really what we had to do was reason about multimodal input, so images, but also you can also imagine, like, these agents traversing through Figma mockups or websites. So some of the things that our agents can also do is it can be given a domain, like, or, like, a website URL and go use it for a while. It's these things. And Wealthfront was one of the first, customers, that was very excited about this possibility.Vibhu [00:52:53]: What have people been asking? Like, is there any demand that we have not covered? Like, UI testing, right?Vibhu [00:52:59]: I wanna try a new. I wanna ship a new feature, test the UI, simulate how people will do it. Any interesting things that you're seeing demand for?Product Testing, Websites, and Synthetic PanelsJoon [00:53:08]: Today, a lot of the demand does come from like, the places where people have historically used human panels, we can now replace with agents, and these synthetic populations. And this is not replacing human panel. in many ways, the simulation that Simile is building is grounded. So the way that I think about this is we are trying to represent humanity at scale. And in that way, the use cases are what we would expect, but it's the scale of deployment that surprises me.Joon [00:53:44]: Turns out there are so many decisions that people make every day in these organizations, groups, and we want to be able to say, “We listen to people. We have consulted our users.” But in reality, that is rarely the case because getting to people and asking them many questions, it's difficult. It's both costly, time-consuming, but most importantly, people are just not available. If I had to answer 1000 survey questions for this one particular, vendor, even if I wanted to do that, like, I would never do it. And that's very much the case. What simulation can do is ensure that the voices of people are always represented in rooms where the decisions for them is made, right? So all the stakeholders of this particular product launch, ideally they're consulted. That's what this technology really is trying to enable.Market Size, TAM, and Human Decision-MakingSwyx [00:54:39]: In my mind, that means it skews towards more consumer focus, right? Like, anything with a wide enough customer base where you do benefit from the diversity that you represent. What are some rough statistics, just for people who are not familiar with this market in general, what's the market size that. I'm sure you have some, like, rough numbers. market size is, like, a vague questionSwyx [00:55:01]: But, like, how much do people spend?Joon [00:55:03]: So market research is a $100 billion industry.Joon [00:55:06]: But the thing about simulation is not a tool for market research. Simulation is a tool for human decision-making. So the question around what is a TAM here is quite tricky, right? Because it's easy to say, “Well, market research TAM is roughly 100 million or 100 billion.” so is it a TAM? And not really, right? Because in many ways, you're trying to inform all human decision-making. You're trying to inform every decision that are made about humans for humans. What is a TAM for that? It's really unclear. And I'll be honest. Like, I have a scientific background, I have a research background, so I didn't come into the field calculating, oh, what is the TAM for human decision-making? But I just had to assume, well, if we can inform every decision that is made about human for human, that has to be big.Swyx [00:55:58]: Some- something valuable.Joon [00:55:59]: Exactly.Swyx [00:55:59]: To some extent, you are a unicorn founder now, and you have to care as a CEO. But, like, I do think, like, yeah, when you go into these boardrooms with people that you're quoting millions of dollars of contracts for, like, you have to say, “Well, here's what you spend on humans-”Swyx [00:56:15]: “. And here's what we save you, and it's 85% similar.”Joon [00:56:19]: And certainly, the value case, is something that we care deeply about. Like, what is the value that we provide to the users and the decision-makers? But this is also where, like, as a founder, I think valuation only tells one very superficial aspect of the story, and I try not to think too much about valuation, in general, because that's not what also motivates a team or certainly doesn't. I'm, I-- Again, the interesting thing about researchers is we are happy living in academia, getting paid next to. we get paid okay. we don't get paid that much, as a researcher here in academia, but it's the impact and it's the, it's the value that we can provide to the individuals and the society that really drives us. And in that way, ultimately what drives us is the impact. Does the simulation we provide have a real impact in people's decision-making in ways that progresses our society forward? If the answer is yes, then yes. that has to be great business, and we see that in numbers, and we do care deeply about that upside story, but that's the heart of it.Where Simulation Goes NextVibhu [00:57:27]: Do you have any timeline predictions? So we talked about scaling laws of simulations.Vibhu [00:57:33]: You brought up, okay, maybe one day we can simulate how to solve climate change.Vibhu [00:57:38]: Where are we now?Vibhu [00:57:40]: If that's not the end state, what is an end state, and what does progress look like?Joon [00:57:45]: So what I sometimes tell people is simulation as industry, it feels a lot like where GPT-3.5, GPT-4 was, for the AGI saga, which is we have now technology that is powerful enough to do real damage on the verticals that we are tackling. At the same time, there's a lot of progress that is yet to come. And that's, I think, where this is. So the way I see it, I do think there will continue to be breakthroughs both in data, in algorithms, and there will be much more aggressive scaling that will also happen over the next few years. But I think that's roughly where we are.Swyx [00:58:27]: I think that was about the ro

In an online meeting with Subrath on 16 August 2026, Michael answers questions about Bhagavan Ramana's teachings. This episode can be watched as a video on YouTube. A more compressed audio copy in Opus format can be downloaded from MediaFire. Ad-free videos on the original writings of Bhagavan Ramana with explanations by Michael James can be accessed on our Vimeo video channel. Books on Bhagavan Ramana's teachings by Sri Sadhu Om and Michael James that are currently available on Amazon: By Sri Sadhu Om: ► The Path of Sri Ramana (English)  ► El camino de Sri Ramana (Spanish) By Michael James: ► Happiness and Art of Being (English)  ► Lyckan och Varandets Konst (Swedish) ► Anma-Viddai (English) Above books are also available in other regional Amazon marketplaces worldwide. - Sri Ramana Center of Houston

In an online meeting with Sandra on 10 June 2026, Michael answers questions about Bhagavan Ramana's teachings. This episode can be watched as a video on YouTube. A more compressed audio copy in Opus format can be downloaded from MediaFire. Ad-free videos on the original writings of Bhagavan Ramana with explanations by Michael James can be accessed on our Vimeo video channel. Books on Bhagavan Ramana's teachings by Sri Sadhu Om and Michael James that are currently available on Amazon: By Sri Sadhu Om: ► The Path of Sri Ramana (English)  ► El camino de Sri Ramana (Spanish) By Michael James: ► Happiness and Art of Being (English)  ► Lyckan och Varandets Konst (Swedish) ► Anma-Viddai (English) Above books are also available in other regional Amazon marketplaces worldwide. - Sri Ramana Center of Houston

An AI that is rooted in scripture? Could it really be real? And is it really free? Yep - that's right! On this episode, the 2nd part of the "AskPhil.ai" mini-series I'm introducing you to the founder of Ask Phil. And be sure to check out the shownotes, because we have a free giveaway for you to check out! FREE FALL KICKOFF EVENT GUIDE & LINKS https://www.patreon.com/hybridministry/posts/5-youth-group-163404919 SHOW NOTES Shownotes & Transcripts https://www.hybridministry.xyz/215 [FREE] HYBRID STRATEGY GUIDE https://www.patreon.com/posts/complete-guide-142500019?utm_medium=clipboard_copy&utm_source=copyLink&utm_campaign=postshare_creator&utm_content=join_link 50% SUMMER SOCIAL MEDIA https://www.patreon.com/collection/1470781?view=expanded

In an online meeting with the San Diego Ramana Satsang (ramana-satsang-sd@googlegroups.com) on 9 August 2026, Michael answers various questions about Bhagavan's teachings. This episode can be watched as a video on YouTube. A more compressed audio copy in Opus format can be downloaded from MediaFire. Advertisement-free videos on the original writings of Bhagavan Ramana with explanations by Michael James can be accessed on our Vimeo video channel Books by Sri Sadhu Om and Michael James that are currently available on Amazon: By Sri Sadhu Om: ► The Path of Sri Ramana (English)  ► El camino de Sri Ramana (Spanish) By Michael James: ► Happiness and Art of Being (English)  ► Lyckan och Varandets Konst (Swedish) ► Anma-Viddai (English) Above books are also available in other regional Amazon marketplaces worldwide. - Sri Ramana Center of Houston

Our next guests are responsible for changing the sound of film/TV music production in a fascinating way. In a world where most composers rely on software instruments and plugins to get the job done, these 2 composer/producers have opted to record more hardware synths, interesting audio performances, and focus on processing their sounds in interesting ways. Their work can be heard on Ozark, Black Rabbit, Tulsa King, Opus, and many more projects and I'm so excited to welcome them on to the podcast! And the composers are... Danny Bensi & Saunder Jurriaans Hosted on Acast. See acast.com/privacy for more information.

Taylor Killa Bee Starling joins the show to talk her upcoming fight against Kat Paprocki at BKFC Fenway ! Taylor talks being one of the faces of Bare Knuckle Fighting Championship and being part of the first female Bare knuckle Fight at Fenway Park ! We also will talk her amzing entrances, and who she'd pick to walk her out, Blood for Blood and Kat's challenge if either gets a KO they're in our new intro ! FOLLOW & SUBSCRIBE – KNOCKOUTS AND 3 COUNTSBringing you the best in Combat Sports and Pro Wrestling – available everywhere!YouTube (Live Tues & Thurs 9PM EST): http://www.youtube.com/c/Knockoutsand3CountsFacebook (Live Tues & Thurs 9PM EST): https://www.facebook.com/knockoutsand3countsApple Podcasts: https://podcasts.apple.com/us/podcast/knockouts-and-3-counts/id1446923286Spotify: https://open.spotify.com/show/3OpvW0QHBe3uRc3D0pbORt?si=33935ad9669146d3Twitter/X: https://twitter.com/ko3cpodInstagram: https://www.instagram.com/ko3cpod/TikTok: https://www.tiktok.com/@ko3cpodMerch, Streams & Videos (Millions): https://millions.co/kyle-collisonIf you love what we do at KO3C, support us by grabbing merch or ordering a personal video at Millions.co.We go LIVE every Tuesday and Thursday at 9 PM EST — bringing you interviews, breakdowns, and the real talk you won't hear anywhere else.Want dope podcast clips ? Use our Opus clip Link : https://www.opus.pro/?via=Ko3C

Tiama Hanson-Drury is CPTO at Opus 2, the legal technology company behind the systems that run some of the world's largest and most high-stakes litigation. She started her career in sales, moved into product in 2010 and has spent the last 16 years split roughly evenly between individual contributor and leadership roles. Her verdict on AI in product organisations is neither evangelism nor scepticism: the tooling has removed software as a bottleneck, but the markers of quality that mattered before AI have not changed, and teams that forget them end up shipping slop and burning out. In this episode, we discuss:Why a sales background is better preparation for product than an engineering one, and how to lead engineers without pretending to be an expertHow the promise of doing more with AI is producing overload and burnout, and what leaders owe their teams in responseWhy high AI adoption scores mean nothing if pull request sizes are ballooning and someone else has to review the outputThe three things Tiama looks for in her teams now: outcome focus over craft, curiosity, and a culture of sharing failure as readily as successWhy the right move is not to hand three of your seven steps to AI, but to start again from the outcome you wantHow Opus 2 went from shipping every six to 12 weeks to shipping daily, and why that made documentation and planning more important, not lessThe case for betting on humans in the loop when your customers explicitly prefer itWhy Opus 2 is still hiring juniors, and how to build commercial judgment when the groundwork can be skippedThe one situation where Tiama tells people to let AI write the first draft rather than the secondChapters(01:31) From sales to product(03:24) Why the move is less obvious than it sounds(04:34) Leading engineering without an engineering background(06:28) How outsider questions sharpen engineering teams(07:36) What AI has changed for a CPTO(08:16) Overload, burnout and the pressure to token max(10:32) Adoption scores versus quality signals(11:39) What AI adoption actually looks like inside law firms(12:42) Outcomes over output(14:07) How Opus 2 built its AI strategy(16:07) Removing friction from an AI rollout(16:53) Partnering with AI instead of splitting up the steps(18:08) A message from Mike Belsito(19:40) Three things AI asks of a team(21:47) More agency, and why planning matters more(23:51) Scaling a team on conflicting evidence(26:09) Betting on humans in the loop(27:36) Why Opus 2 keeps hiring juniors(29:44) Building judgment when the work moves faster(32:36) Sharing your own AI operating system(33:22) AI slop, CVs and the hiring algorithm(36:17) Top tips for a product career in the age of AI(38:36) Wrap-upReferencedOpus 2: https://www.opus2.comOpus 2 announces addition of Tiama Hanson-Drury as chief product and technology officer: https://www.opus2.com/news/opus-2-announces-cpto-tiama-hanson-drury/Minna Technologies, where Tiama was previously chief product officer: https://minnatechnologies.comDX, the developer intelligence platform used to track squad performance: https://getdx.comDORA metrics: https://dora.devVote for Mike Belsito's South by Southwest 2027 session: https://tinyurl.com/belsitosxswWhere to find Tiama Hanson-DruryLinkedIn: https://www.linkedin.com/in/tiamahansondruryOur HostsLily Smith enjoys working as a consultant product manager with early-stage and growing startups and as a mentor to other product managers. She's currently Chief Product Officer at BBC Maestro, and has spent 13 years in the tech industry working with startups in the SaaS and mobile space. She's worked on a diverse range of products – leading the product teams through discovery, prototyping, testing and delivery. Lily also founded ProductTank Bristol and runs ProductCamp in Bristol and Bath.Randy Silver is a Leadership & Product Coach and Consultant. He gets teams unstuck, helping you to supercharge your results. Randy's held interim CPO and Leadership roles at scale-ups and SMEs, advised start-ups, and been Head of Product at HSBC and Sainsbury's. He participated in Silicon Valley Product Group's Coaching the Coaches forum, and speaks frequently at conferences and events. You can join one of communities he runs for CPOs (CPO Circles), Product Managers (Product In the {A}ether) and Product Coaches. He's the author of What Do We Do Now? A Product Manager's Guide to Strategy in the Time of COVID-19. A recovering music journalist and editor, Randy also launched Amazon's music stores in the US & UK.

Un propietario 1) Madrugada: Cuando me choco con gente que me dice: “Yo soy así y el que quiere que me quiera y al que no le guste que se vaya” ese tipo de personas me da miedo, porque en el fondo te está diciendo: “No esperes que modifique nada”, ni por vos ni por nadie. Incluso, es: “No esperes que modifique nada cuando yo mismo me estoy destruyendo”. Hoy la propuesta de Jesús es que estés dispuesto a trabajar por vos, y decir: “La verdad es que no sé muy bien quién soy, pero, si a vos te lastimo, y a mí me interesa el vínculo con vos, puedo llegar a revisar mi manera”. Porque hay veces que confundimos ser con el tener, el “yo soy de mal carácter” no es lo mismo que decir “yo tengo mal carácter”. No te confundas, tenés mucho tiempo para cambiar y trabajar por vos.2) Desocupado: Si una persona viene a Cristo, te guste o no, viene al mismo Cristo de los de los Legionarios de Cristo o de los de Schoenstatt o de los del Opus o de los Rogacionistas, porque hay un solo Cristo, no hay varios. Hay un solo Cristo, pero hay diversas interpretaciones o maneras de vivir a Cristo. Pero, eso sí, lo que nos separa no es Cristo, sino las doctrinas. El mismo Pablo describe a los cristianos como “cartas vivas de Dios”, porque la gente nos lee primero a nosotros antes que a la biblia. La gente conoce a Jesús primero por tu testimonio y luego por lo que lee de Jesús a través de lo que vos le motivaste. Por eso, no estés desocupado, porque hay trabajo por hacer y es mostrar a Jesús. 3) Recibieron: Somos parte de la obra de Dios hasta la muerte. Jesús nos dijo que hagamos discípulos, pero si reducimos el evangelio a “entrar al cielo”, proclamamos un mensaje que se aleja de formar discípulos, porque hay cristianos que son creyentes, pero no tienen temor de Dios: cumplen, cumplen, cumplen y nada más. Si no, veamos las redes sociales: curas defenestrando a otros, cristianos que ponen comentarios hirientes, incluso situaciones de poca caridad entre nosotros, porque creen que pecado es lo que se hace y no lo que se escribe. Creo que esta es una de las razones por las cuales hay menos gente en nuestra Iglesia, por ver el ataque interno que hay entre pares. Hasta diría que predicamos en modo “anzuelo”: el primer día les mostramos como “oferta” y que no cuesta nada ser cristiano y, cuando están dentro, le leemos las letras pequeñas: “Antes era oferta y ahora tienen que hacer un montón de cosas para permanecer en la religión”. La razón del tipo de prédica que hacemos es la gente que tenemos. Creo que es un problema que incluso tenemos en la catequesis, porque se redujo en ir a catequesis solo para cumplir con la primera comunión. Pero el evangelio nos propone tener seguidores, discípulos para Jesús. 269 veces aparece en la biblia la palabra “discípulos”, porque el trabajo es que seas discípulo y no un mero cumplidor de normas o un simple cristiano “camaleón”, quien hace según la ocasión. Algo bueno está por venir.

In an online meeting with the Ramana Maharshi Foundation UK on 8th August 2026, Michael explains Guru Vācaka Kōvai verse 541, and then answers questions on Bhagavan Ramana's teachings. This episode can be watched as a video on YouTube. A more compressed audio copy in Opus format can be downloaded from MediaFire. Michael's explanations on the original works of Bhagavan can be watched free of advertisements on our Vimeo video channel. Books by Sri Sadhu Om and Michael James that are currently available on Amazon: By Sri Sadhu Om: ► The Path of Sri Ramana (English) ► El camino de Sri Ramana (Spanish) By Michael James: ► Happiness and Art of Being (English) ► Lyckan och Varandets Konst (Swedish) ► Anma-Viddai (English) Above books are also available in other regional Amazon marketplaces worldwide. - Sri Ramana Center of Houston

YEAR 6 IS FINALLY HERE!  GO CHECK OUT OUR YOUTUBE TO SEE OUR BRAND-NEW INTRO!  You can find the animator using the link below! https://www.fiverr.com/syedahumna56/do-professional-pixel-art-animation-of-your-choice?utm_medium=shared&utm_source=copy_link&utm_campaign=gig&utm_term=AyNLxkP   *Intro includes minor edits not provided by the original animator. All animated assets were provided by the animator listed above, with some text assets added in post by Keeping Up With The Nerds.   Check out our affiliated links! Opus clips Partner link: https://www.opus.pro/?via=Nerd   Check out our Website: Keepingupwiththenerds.com   This week on Keeping Up With The Nerds, the gang takes a nostalgic trip back in time before tackling the massive wave of pop culture announcements!   First up, the Nerds dive into their personal vaults to share old grade school memories. From surviving the awkwardness and bullying of middle school to reminiscing about the surprisingly awesome high school lunches, the crew looks back at the glory days of growing up geeky.   Then, the focus shifts to the colossal news drop from D23—and that was just Friday night! The gang breaks down everything revealed so far, from major upcoming movie slate reveals to ambitious Disney theme park expansions and renovations. With Disney finally feeding fanbases that have felt starved for content, there was truly something for everyone in the showcase.   Finally, the crew zeroes in on the biggest surprise of the weekend: Kingdom Hearts! The Nerds celebrate what could be the start of a massive flood of long-awaited news, analyzing what these teasers mean for the future of the franchise—all this and more!  

This week on Shat the Movies, we're going back to school with Mr. Holland's Opus (1995), the story of a musician who takes a teaching job while dreaming of something bigger. Gene and Big D break down Richard Dreyfuss' decades in the classroom, the students he inspires, the family he sometimes neglects, and whether Mr. Holland is actually the inspirational teacher we remember him being. Is this a moving tribute to teachers, or does nostalgia deserve most of the credit? Tune in and find out. Full movie info below Mr. Holland's Opus (1995) is a drama directed by Stephen Herek and starring Richard Dreyfuss, Glenne Headly, Olympia Dukakis, William H. Macy, and Jay Thomas. The film follows composer Glenn Holland over three decades as a temporary teaching job gradually becomes his life's work. Richard Dreyfuss received an Academy Award nomination for Best Actor for his performance, and the film became a popular 1990s story about teaching, family, sacrifice, and the different ways a person can leave a legacy. Subscribe Now Android: https://www.shatpod.com/android Apple/iTunes: https://www.shatpod.com/apple Help Support the Podcast Contact Us: https://www.shatpod.com/contact Commission Movie: https://www.shatpod.com/support Support with Paypal: https://www.shatpod.com/paypal Support With Venmo: https://www.shatpod.com/venmo Shop Merchandise: https://www.shatpod.com/shop Theme Song - Die Hard by Guyz Nite: https://www.facebook.com/guyznite

Intel Chat with Matt Bromiley and Chris Luft.• AI-generated patches fix vulnerabilities about half the time. 1Password's Off-By-1 team tested ChatGPT-5.5 and Opus 4.8 against six vulnerabilities: across 6,080 generated patches only 46% fixed the underlying flaw, and some that did were narrow enough to be bypassed. Separate Veracode research found a 56% security pass rate across 100+ models, with 44% of AI-generated code carrying detectable OWASP Top 10 issues. Matt's pushback: what is the HUMAN success rate for comparison, and why is nobody publishing that number?• LiteLLM supply chain attack. CloudSEK reports 2,500+ organizations and 434,000 CI/CD pipelines potentially exposed. LiteLLM was not the initial target: the compromise came in through Aqua Security's Trivy scanner and spread when LiteLLM's CI automatically installed it, ending with malicious versions 1.82.7 and 1.82.8 on PyPI. They were live for roughly 40 minutes, which automated dependency resolution and cached layers were more than enough to propagate.• Anthropic's models reached real systems during evaluations. Reviewing 141,006 evaluation runs, Anthropic found three incidents where Claude models gained unauthorized access to real organizations during capture-the-flag exercises, after a misunderstanding with an evaluation partner left the environments internet-connected. One model published a malicious package to the real PyPI, where it ran on 15 real systems. Matt argues this is a lab test rather than a threat report, and asks what defenders are supposed to do with it.• North Korea behind the npm compromises. Amazon Threat Intelligence links the typo-crypto, debug, chalk and axios incidents to the same DPRK actor tracked as SAPPHIRE SLEET, STARDUST CHOLLIMA and BlueNoroff. Wiz found roughly one in ten cloud environments touched by the debug and chalk incident within two hours. The technique has shifted: malicious functionality is now split across several innocuous-looking packages that only do anything once combined, plus slopsquatting and prompt injection aimed at AI code scanners.Stories covered:• https://www.darkreading.com/application-security/ai-generated-patches-fail-half-time• https://www.securityweek.com/over-2500-organizations-impacted-by-litellm-supply-chain-attack/• https://www.anthropic.com/news/investigating-incidents-cybersecurity-evals• https://aws.amazon.com/blogs/security/amazon-identifies-north-korean-hacker-group-behind-open-source-supply-chain-attacks/Chapters:0:00 Back from Black Hat3:31 AI-generated patches fix vulnerabilities about half the time6:23 What is the human success rate?10:53 LiteLLM supply chain attack13:03 Pin your dependencies15:59 Anthropic models reached real systems during evals22:12 This is a lab test, not a threat report27:06 North Korea behind the debug, chalk and axios compromises30:59 Malware assembled from harmless-looking parts33:27 Clever people on the other side of the fenceThe Cybersecurity Defenders Podcast — a podcast about cybersecurity and the people that keep the internet safe. New episodes drop weekly.Subscribe wherever you listen:• Spotify: https://open.spotify.com/show/6ep00zeY3S8ffZ4o0UeSps• Apple Podcasts: https://podcasts.apple.com/us/podcast/the-cybersecurity-defenders-podcast/id1649981740• YouTube: https://www.youtube.com/@limacharlieioLearn more about LimaCharlie: https://limacharlie.io#cybersecurity #infosec #AIsecurity #supplychainsecurity #threatintel

In this episode of the Kubernetes Bytes podcast, Bhavin talks to Phil Andrews, Global Field CTO at Cast AI about all things AI. The discussion starts by talking about why you don't need an Opus class or Fable/Mythos class model for each task, and how Kimchi from Cast AI can help manage the quality of your output with the cost associated with routing between different models. They also talk about Omni which allows customers to get GPUs across hyperscalers and neocloud providers. Listen to learn more! Check out our website at https://kubernetesbytes.com/ Show Notes: https://www.linkedin.com/in/philip-andrews-iii/ https://cast.ai/kimchi/ https://docs.cast.ai/docs/omni-overview https://cast.ai/blog/

Former Ultimate Fighter contestant Kat Paprocki joins the show to talk about her upcoming fight for Bare Knuckle Fighting Championship at the historic Fenway Park .We'll talk abut her transition from MMA to Bare Knuckle and her rise in BKFC and taking on one of their biggest stars . We also will talk about her dream walk out and why it might include Glorilla ? FOLLOW & SUBSCRIBE – KNOCKOUTS AND 3 COUNTSBringing you the best in Combat Sports and Pro Wrestling – available everywhere!YouTube (Live Tues & Thurs 9PM EST): http://www.youtube.com/c/Knockoutsand3CountsFacebook (Live Tues & Thurs 9PM EST): https://www.facebook.com/knockoutsand3countsApple Podcasts: https://podcasts.apple.com/us/podcast/knockouts-and-3-counts/id1446923286Spotify: https://open.spotify.com/show/3OpvW0QHBe3uRc3D0pbORt?si=33935ad9669146d3Twitter/X: https://twitter.com/ko3cpodInstagram: https://www.instagram.com/ko3cpod/TikTok: https://www.tiktok.com/@ko3cpodMerch, Streams & Videos (Millions): https://millions.co/kyle-collisonIf you love what we do at KO3C, support us by grabbing merch or ordering a personal video at Millions.co.We go LIVE every Tuesday and Thursday at 9 PM EST — bringing you interviews, breakdowns, and the real talk you won't hear anywhere else.Want dope podcast clips ? Use our Opus clip Link : https://www.opus.pro/?via=Ko3C

AI agents crashing.

How can content marketing in a tight niche build the audience that launches your book? And how do you decide whether to hand your self-published bestseller to a traditional publisher. Suzanne Smith shares what she learned in four years of going from blog to book deal. In the intro, how to stand out as a writer in the age of AI [Nathan Barry Show; Interview with Nathan Barry]; thoughts on asset maintenance; Goodreads giveaway on Bones of the Deep (Aug 5-20, 2026) This episode is sponsored by Publisher Rocket, which will help you get your book in front of more Amazon readers so you can spend less time marketing and more time writing. I use Publisher Rocket for researching book titles, categories, and keywords — for new books and for updating my backlist. Check it out at www.PublisherRocket.com This show is also supported by my Patrons. Join my Community at Patreon.com/thecreativepenn Suzanne Smith is the founder of The Independent Landlord, and the bestselling author of The Good Landlord Handbook. You can listen above or on your favorite podcast app or read the notes and links below. Here are the highlights and the full transcript is below. Show Notes How a free blog in a tight niche built the audience for the book Rewriting the book from scratch when the law changed Why speed made self-publishing the only option Building a paid membership after one audience member asked for it Negotiating a Penguin Random House deal with no agent Using AI as a business sidekick, with a control room and an engine room You can find Suzanne at TheIndependentLandlord.com. Transcript of the interview with Suzanne Smith Jo: Suzanne Smith is the founder of The Independent Landlord, and the bestselling author of The Good Landlord Handbook. So welcome to the show, Suzanne. Suzanne: Thank you. Jo: Oh, there's so much to talk about today. But first up— Tell us a bit more about you and your background, and how you got into property and writing after a legal career. Suzanne: Well, I've always loved reading books. In fact, I recently did a French literature degree as a mature student. Being an author was never in the game plan at all. It's not something that I even thought about. I was brought up in New Zealand, so shout out to all the Kiwis and those across the pond in Australia. The thing about it is, Jo, you've lived there yourself. Kiwis are independent, self-reliant and have this great sense of fair play. So that was a very formative experience for me. We moved back to England when I was 16, and I have become thoroughly anglicised since then, but a Kiwi at heart. I always wanted to become a lawyer. New Zealand in some ways on television is quite American, and there was this American programme called The Paper Chase. It was about all of these students at Harvard studying law, and the professor said, “You come here with a skull full of mush and you leave thinking like a lawyer.” I thought, “Oh, I like the sound of that.” I didn't really know what a lawyer was, but everyone seemed to be very happy that I wanted to become one, and then that was it. Jo: So you went into law, and then how did you get into property? Suzanne: So I worked for 25 years as a solicitor. That's like an attorney if you're American. Started off in a law firm, and then I went into pharmaceuticals and I worked for big companies like what is now GSK, GlaxoSmithKline, and small companies as well. When I started out, it was before the internet, before Google. When you're in house, you're very much a generalist. You do a bit of everything. So you help companies grow their business. You're not business prevention, but you're still bound by the code of conduct for solicitors. You've got this role of keeping the company on the right side of the law. Then I had twins, who were born about five years after I became a lawyer, and I decided to work part-time for a while and did an MBA when they were little, part-time through the Open University. I know Jonathan is doing one at the moment. Jo: Yes. He's finished, so that's exciting. Suzanne: That was transformational for me, because I had probably been thinking a bit too much as a lawyer, and it helped me to broaden my view of the world and understand all sorts of things. Sso I continued going up the greasy pole, and then for my last job, in 2015, I joined a biotech company in Cambridge, England, as general counsel and company secretary. It was a long way from home, about two, three hours' drive from home. So I decided to buy a flat, an apartment, and to stay there in the week. I thought to myself, “Well, when I leave this company, I can let it out as a buy-to-let,” but actually as a landlord. So I stayed there for five years, and then when I left, I let out the property. The reason why I decided to leave law after 25 years, I had what I call a sliding doors moment, like in the film. I was 50. I was on holiday with my husband, and we'd probably had one too many rum cocktails. And he said to me, “Well, what do you want to be doing with your life? What would you do if you could do anything?” I was thinking, “Well, I've done law. I want to do something else now.” I didn't really know what that was, and I'd always been thinking about studying French properly, and that's when I left. So I decided, 18 months later, I left to do a French degree at King's College London, full-time. I was the only old person there with lots of 18-year-olds. When I did that, I was able to cash in my share options because I was a good leaver. I retired, and so I started buying properties to let out and became a landlord, without really thinking too much about it, and I used letting agents. They were fine to begin with, but I didn't really have a game plan or anything like that. What I realised is that when I tried to research things online, I couldn't really find anything that was terribly helpful. It was either quite general or it was very legal. So after a while… I became a landlord in 2019. I had the idea, why don't I set up a blog? And this is August 2022, so just four years ago. My husband came up with the idea of the name, The Independent Landlord, because it's that Kiwi spirit, being very independent. I thought, “Right, I'm not going to charge anyone for it. It's a hobby. It's not a business. I'm going to pay it forward and help, use my legal training, practical legal approach, and practical common sense, by doing this blog.” Almost exactly four years ago, I sent my first newsletter to 13 people. Jo: Woo-hoo. Suzanne: And I sent one last week to over 18,000. So it's been quite a journey. Jo: Wow, this is so great. I love this. There's so much in there. The turning 50 and then doing a degree. My master's in death is a little different to your French literature, but I like it. So I love this, and buying properties, starting it on the side, not a business at first, and growing the audience, and obviously you've put so much work in. Then you decide to write a book. So talk about that, because an online blog, although I'm sure your articles and everything were super useful, it's very different to write a blog than a book. So talk about your challenges in writing. Why did you decide to do a book in the first place? Suzanne: Again, I was an accidental landlord, is what they call it when you let a property when you didn't intend to buy it as a buy-to-let, which I did with my Cambridge flat. And I became, in many respects, an accidental author. So I was having a conversation with my husband again and I was saying I'd done this lead magnet to get people to sign up to my newsletter, and a big new law was going through Parliament at the time, called the Renters Reform Bill, that was going to completely transform the way landlords operate. I was saying to my husband, “Oh, I need to update my lead magnet, a little ebook, to explain the new law.” He looked at me and said, “Well, why don't you do a proper book? Write a book.” This was on the 29th of September, 2023. The reason why I mention that is that I thought, “Wow, what a great idea,” and my head was bursting. I went onto Google, and guess what I downloaded on the 1st of October? Jo: My blueprint? Suzanne: Exactly. I found you immediately, the Author Blueprint, and I downloaded it. I checked: on the 1st of October, 2023. Then I listened to almost… well, I think I went back several years on your podcast, just trying to understand. I'm like that. When I try and do something, I just try and learn everything that there is to know about it. So I started writing the book, and I guess the first challenge was I write quickly, and I'm used to writing for people who aren't lawyers, being in-house. So I thought I needed to have a structure. The structure was easy in many respects because, a bit of business at the end, and then you can go through a tenancy. I thought it was important to have a narrative thread all the way through it, just to bring it together. This is the literature degree coming in here. I thought that the mission for everything I do, the reason why I started doing this, is to help landlords, but also to help the experience of renting that people have in England. It's very specific for English law. And to help improve the private rented sector. So that's why I originally set up my blog for free, and I wanted, when people went onto Google, they could find something sensible and very detailed from me. My blog posts were… Well, I've now got over 400,000 words on my blog, so it's a substantial piece of work that is out there free of charge. So what I decided to do was to bring this narrative thread, I call it the good landlord ethos, to the book. Then I wrote very quickly, and I had a pretty good draft by April 2024, because we were all thinking that the law was going to change very soon. But then there was an election, and in the end the government changed and the legislation changed completely, so I had to rewrite the book and start again. So I think that my biggest challenge was that my subject matter, the new law, changed. Because I wanted to publish this book that explained to people practically what they have to do, and make it really straightforward, keeping out of politics, because it is a very politically charged area. I wanted to write it so it's a manual, somebody could literally follow it. So I used an editor, and I did write the book twice. I had a beta reader who is another lawyer, and a landlord as well. Then I got to the get-the-damn-thing-done stage. The really tedious bit of all the typos at the end. Jo: Yes, the finishing energy to get it out there. So at that point, obviously you'd found my blueprint, so you were learning about the indie way of doing things. Did you always decide to self-publish? How did you think about publishing? What were your challenges in publishing? Suzanne: It never occurred to me not to self-publish, because the new law came into effect on the 1st of May, 2026. The law and the details that I needed for the book were finalised in January, and I published on Amazon on the 5th of March, so I had to go so quickly. Even though I'd got a lot of it written, the last bit came in January, and so I needed speed. I knew that for landlords to be able to have something that they can use straightaway to help get them ready for it, and then use as a manual afterwards, I had to be first. Jo: Sorry, just on the year. Was it '24? You said '26. You meant May— Suzanne: 2024? No, no, because I actually published it this year. What happened in 2024, I had the first draft ready, but then I had to do another draft because the law changed when the Labour government came in. Jo: Right. Suzanne: The Renters Reform Bill turned into the Renters' Rights Bill. So I had to rewrite the book. So I finished however many drafts at the end of January 2026. Then it went to an editor, et cetera, et cetera, and I managed to get the book ready for a proof, to get the proof printed, towards the end of February. So it was really quick to go from the law being sufficiently finalised for me to write a book in January, and then having it ready in just over a month. There is no way that I could have done that if I'd gone to a traditional publisher. It didn't even occur to me to go, because I didn't want to be going touting around my book and, “Please publish me,” et cetera. It's just not me. I'm the independent landlord, and that moved very easily to being the independent publisher. So I learnt how to do all the publishing. And a huge thanks: I joined your Patreon and I was a very good student. I went through everything systematically and followed your playbook, and used Vellum and BookFunnel and all the other tools. So I decided to go on Amazon as well as have my own Shopify store, which just about killed me. Jo: I was going to say, you are an excellent student. You really like learning, but you also put this into practice, which is why I also wanted to talk to you. You haven't just talked about all this. You've literally done everything. Suzanne: Sometimes it was like my head was going to burst. Luckily, Claude upped his game earlier this year when we got the Opus 4.5. I didn't use AI really until this year. I decided I need to do exercise all the time, and have that as a have-to-do, because my head was spinning all the time with all these different things. So I would go to the gym, go to a spin class, and then I would walk out with my phone on, with the Claude app, and dictate a stream of consciousness into it. “Oh, I need to do this, or what about that? Oh, I just remembered about this. Oh, I've had this idea, blah.” And then said, “Make sense of it for me, Claude.” So it was very much as a thinking partner, because when you're writing your first book, it's bad enough, but when you're learning how to publish… Even, like, when I got the first proof of the book back from BookVault, I realised that all the footnotes—I have 114 footnotes in my book, and that, again, is the recent degree there—and the formatting had gone skew-whiff. Apparently it was an issue with Vellum, and they were really lovely and they sorted it straight out for me. So it shows: always get a proof of the book. They were able to sort that out very quickly, and BookVault were very quick in getting me another proof, because you can shortcut it and just pay to get a very quick delivery. Amazon, on the other hand, was really slow. It took a week. So I actually published earlier on my Shopify store for my members, of my membership, and I gave them a discount. Then I finally got it onto Amazon on the 5th of March. There are all these different skills you're having to learn. The Shopify store I found very hard, and there was all the tax, because I'm VAT registered. So I think I'm still recovering. Jo: You're still recovering. I wouldn't normally recommend a Shopify store for someone with their first book, doing first of everything. But, as you say, you're someone who learns a lot, puts it into practice, and— I think you were pretty determined to do that because you had a community as well, right? Suzanne: Exactly, yes. The big subscriber list. I think that's why the book did so well. So in the first week, because I met you at the Indie Author Lab put on by— Jo: Yes, London Book Fair, yes. Suzanne: Yes, the Alliance of Independent Authors. I met you there, and it was just my first week, and I had 1,000 sales in the first week. That was because of my audience. I'd been going on about the fact that I'm writing this book for two and a half years, because that's how long it took me to do. So I had a wait list for it, and I had a thing on my website, a landing page on my website, saying how good the book was and why it's the best thing for the Renters' Rights Act. Then I went onto Google, and I think I sent you a screenshot of this at the time. I put into Google, “What's the best book for the Renters' Rights Act for landlords in England?” And it came up with me as a featured snippet, and I hadn't even published it at that time. It was just about there. So the blog really helped, because I'd become an authority on the Renters' Rights Act. Even though I'm not a practising solicitor any more, I spent all my time reading the damn thing, and it is a very complicated bit of legislation. Funnily enough, I have ruffled a lot of feathers. People have even said about me behind my back, “What does she know? She's only got four properties.” But I just took no notice. I thought, “I'm going to try and use my legal brain and my understanding of what it's like being a landlord, there with the rubber gloves cleaning an oven when people have moved out, and try and write something that's not trying to sell anything else, and to help people.” And then it got picked up. Jo: Yes. Wait, let's just slow down. Slow down, because we will get onto that in a minute. But let's just come back to that launch. So as we talked about, you've had a blog for five years— Suzanne: It was three and a half by then. Jo: Three and a half years you've been blogging, but hundreds of thousands of words of useful information. So you've essentially done content marketing. You've attracted people. You had a lead magnet. You got them on your email list. You told them that you were writing a book. You got a sort of pre-sales list up. So that's an email list. You've got a blog. Did you do anything else in terms of marketing? Suzanne: I had YouTube, a big YouTube channel. I'd only set it up at the end of 2024, and I'd had half a million views. And again, just very straightforward advice, and without all the scaremongering and politics. I deliberately keep out of it all. A lot of people joined my newsletter as a result of that. Also a year ago, exactly today, I was running a Facebook group, which was a lot of hard work. There were a few thousand people in it, but there are often a lot of people going in there trying to sell things: insurance, eviction specialists and things. And there was also a lot of people just being unpleasant to other people. I was getting fed up with it. It was taking me a lot of time, and I was doing a lot of speaking events and trying to explain what this new law was doing, and wearing myself out. I'm an extrovert, but even I find speaking events absolutely exhausting, because it's like everything gets sucked out of you. It's strange. Then somebody came up to me in July last year and said, “Suzanne, can you set up a membership?” I said, “Well, landlords aren't going to pay for that.” And they said, “Yes, they will. You build it and they will come.” I asked ChatGPT and thought about it. I asked ChatGPT, who I was dating at the time, now exclusively with Claude, but I know Claude has other people in his life. But I'm very much set with Claude Fable at the moment. So I asked ChatGPT, how can I go about setting up a membership? And I mentioned your one and said, “Should I do it on Patreon?” And then he came back with: go for Circle. So I set up a membership on Circle, exactly a year ago. In fact, it's the anniversary of my first member yesterday. hTe rules I had were, no selling. So I don't sell, no affiliate links, no one else can sell anything, and we have to be supportive. No negativity, no politics. So what it's become, it's like the senior common room of the private rented sector, with landlords, lawyers, letting agents. There's a fantastic forum in there. It's not me doing it, it's peer-to-peer. I have twice-monthly live streams where people can ask me questions. I wonder where I got that from. No, I very much modelled it on your Patreon, but on a different platform. I have courses in there as well. So that has really grown. I launched it in July, and by September, October, I'd gone past the VAT threshold, which has complicated everything, but it means my business now is this membership. I really enjoy doing it, and there hasn't been all the negativity that you have in a Facebook group. So I had them as… talk about your thousand fans. There are about 1,500 in the membership, and their support really helped the launch of my book, as well as the wider people who get my free newsletter. Jo: Yes. Suzanne: So it's all different types of content marketing. Jo: Y, but I do love this. And of course, if people are wondering, I joined Patreon back in 2014, I think it might have even been before that, and there weren't too many places back then to run communities. It wasn't even really a community at the time, it was a sort of, almost a “give me a bit of support for the podcast.” So things have changed a lot in terms of communities, and obviously you went with Circle, which is great. Patreon is slightly different now, and some people are using Substack for something similar. So that's just on the platform, but on the business: early on in our conversation you said, “I wasn't going to have a business. It wasn't a business. It was just putting stuff out there, helping other people,” and then your audience asked for this membership. And so now it is a business, right? Suzanne: Yes, it is. Jo: And you've got a book and all of this. So are you happy with the change to a business? Because obviously you have to treat it quite differently. Suzanne: Yes, I am, because I think to begin with, I was just doing it one or two days a week. I was actually studying a master's in French literature part-time, and I then found that I was enjoying the blog more than the master's, so I dumped the master's after the first year. But after getting 88% for one of my dissertations, which interestingly was on the translation of a Simone de Beauvoir book into English, and the publisher who's got that now is Random House, but that's another thing. Anyway, so I decided to give up my master's and double down and work full-time on the blog. People were paying to help me with all the big fees and things, the big tech stack, Buy Me a Coffee. I was doing a little bit of consulting and things. I was working six, seven days a week. I was treating it like a business in terms of quality and my effort, but it wasn't a business in terms of revenue. Then it just all came together, and this person said, “Set up a membership,” and I thought, “That's what I'm going to do. I'm now going to put it on a business setting.” I've got an MBA, I know how to do it, and people thought I planned it, but I didn't. It just happened. So now I do very much treat it as a business, but I still don't advertise. I don't allow people to advertise with me, because I want to be independent. If I recommend something, I want people to believe it's me recommending it, not just because someone's paying me, which can be a big issue in the landlord area. Jo: Oh, in any industry. I get pitched every day with loads of random things that people are like, “Oh, a dollar a click or whatever, if you send this to your list.” And it's like, seriously? Just stop it already. I did just want to add there: somebody asked you, they said, “You should have a community,” and that sparked that idea. I just wanted to acknowledge that my Patreon came from Jim Kukral. Some of you will remember, who've been around a long time. Jim Kukral came on my blog around sort of 2013. Amanda Palmer had just put out a book called The Art of Asking, and I was doing a lot of unpaid work on the podcast at the time, and I was either going to give it up or I had to fund it somehow. Jim said, “You should do a Patreon.” And I was like, “Oh, no, I hate asking for money.” So at the time I just felt, oh, weird. Then I was like, “No, I do all this work,” as you were saying. Now the Patreon has changed so much in terms of what it is, but it is the backbone of my business, too. So I love that you listened to one of your fans who said what they wanted, and I love that I've listened as well. Sometimes we just have to listen to those urges, don't we, to take things on? Suzanne: Yes, absolutely. In some ways I didn't really back myself before. I thought, “No one's going to pay for this.” Then the more you give, the more they want. Jo: Yes. Suzanne: What I've been really working on now is having boundaries, because there were two big kind of mottos that I picked up when I was working in pharmaceuticals. One was from a head of the business. He was Canadian, and he was always saying, “You've got to skate to where the puck is heading.” Jo: That's Wayne Gretzky, is it? Suzanne: Exactly. Yes. He would always say it, and so that's what I've done with my blog and my book. When I write things, I don't pay for any tools. I don't do keyword searches and all that. I just think, I do one blog post per topic, and I'm going to guess what people are going to be searching for soon, and I build up all this content around it. That's why most of my blog pages are top five. I've had no advertising. I haven't asked for any backlinks. I don't do it. People backlink to it because it's useful. So that was the first thing, is skate to where the puck is heading, and that was my approach with the book. I knew people would need this book from around May, and they'll need it forever, because it is so complicated and regulated, the rules for being a landlord in England. So that was the first one. The second thing was: when you take something on, you've got to let something go. One in, one out. I found that I was taking on so many different things, and I've just been cutting back, because I can't be doing all the speaking, I can't be answering people's emails. So I now don't do emails. If people want my advice on something, they ask me in the hub, at the twice-monthly live streams. Sometimes I answer in the forums, but I don't have time. When there are 2.4 million landlords in the UK, and even with our 18,000 on my newsletter, I could spend, and I did, I used to spend all my time replying to emails. So anyway, there are the things. Oh, and there was a third one, which is: attract, don't chase. One of my friends gave me that advice and that's exactly what my approach has been. I just don't chase for anything. I just put the stuff there and then build it and they will come. Jo: Yes, and I think another thing is the power of the niche. It's so clear that what you write about, the people you are aiming at, you have an extremely tight target market. That is both a strength and obviously a weakness, because they're the only people. But as you say, there's more than enough of those people for a community, for the book you have. From my own perspective, that's the same for me, the power of the niche. That's how I have a successful podcast, for example, because of that reason. I think you're like a poster child of what a non-fiction author should do. What I like is that you didn't go, “Oh, where's a niche where I could make money?” and then jump in. You've gone about this in a kind of slightly accidental way, but now you're leaning in and this uses all your skills. So this really is a great example of the power of the niche and then making the most of it. But let's move on to what then happened, and— What happened with the book deal? Suzanne: Wow. So you and I met each other on whatever day that was in March at the Indie Author Lab, and the following day I got an email, via my website on a contact form, from Penguin Random House saying, “We love the book. We love the mission, its values,” all this kind of thing. And I was thinking, “Oh, it's another one of those. Must be an—” Jo: AI spam bot, right? Suzanne: Yes, and I remember I sent you a screenshot of it, and then I checked her out on LinkedIn and thought, “Okay, there is somebody with that name there.” You're always saying, and Orna Ross and everyone are always saying, “Watch out for scams.” And in fact, Penguin Random House even this weekend on Instagram put out something saying, “There are lots of people impersonating us.” So I didn't take it too seriously, and it was something like, “Oh, would you be interested in us publishing your book?” And I thought, and I laughed. It was like, no, this is too good to be true. So I replied and said… Oh, I said, “Well, thank you so much. The Renters' Rights Act…” And so this is like the second week in March. “The Renters' Rights Act comes into effect on the 1st of May. If you want to publish it, you're going to need to get your skates on.” I literally did say that. Then she arranged a meeting with me the next day, on the Friday. I still was very dubious about it, and I had a think about it. What helped me, and I have the little booklet here: at the Author Lab, we did some work at the beginning, and Orna said, “Put your phones away.” And it was like, “What? Put my phone away?” Then we had to do this definition of success, and our passion, and our mission, and our purpose. I wrote down things like, I want to help landlords, and in so doing, help improve the private rented sector. I get pleasure from helping people. I want to improve standards and use my legal and practical skills, et cetera. So I thought, “Okay, what is my purpose of doing this book?” It isn't really to make money, because going with Penguin, you wouldn't do that for financial reasons, because you'd make very little money. So I thought, what is my why? My why is I want as many people to read this book as possible. And I've managed to sell a few thousand copies, but there are 2.4 million landlords, and they all need to understand this book, and the only way that I can get it out there, apart from doing ads, is to get it out in bookstores. So I thought about it, and then said, “Yes, I will do it, because I want to get the book out there.” So it's distribution. It's going to be published on the 6th of August, which is really quick, bearing in mind they contacted me in the middle of March. It's exactly the same book, it's just got different copyright wording and different blurb, different paper. Same cover, because I managed to find a fantastic cover person to do it. So they've kept everything the same. So we negotiated that book. I have no agent. They came to me. It's the attract, don't chase. I just put my lawyer hat on, and because one licence is very much like another one… I did turn down their first offer. Jo: Well done. Good negotiation. Suzanne: My daughter said to me, who's an adult daughter, she said, “But it's Penguin.” And I said, “Well, no, but it doesn't work for me.” So I had a call with them, and then they came up with something that worked for me a bit more. I did have to concede on a few things, like I can't sell it in my Shopify store. But in some ways, that was a blessing in disguise, because it means I don't get any more “Where's my book?” emails. Jo: Yes, exactly. Pros and cons of everything, basically. Suzanne: I have very clear rights to get it back. If I want it back, I can get it back and I don't have to give a reason. They're lovely. They have been really very wonderful. When I went up there a month or so ago, they gave me this book bag, and it's got on it, “I'm published by Penguin,” and I burst into tears. Jo: Aw. That's nice. Suzanne: I don't know, it just seemed like such a big deal. Because up until then I was just being all very lawyerly and task-orientated. Then I thought, “Oh my goodness,” and then it dawned on me. So I'm now in this interim period where I've taken it off Amazon and off my Shopify store, and I feel very maternalistic towards the book because, you know, it took me two and a half years, which is longer than a pregnancy. Obviously it's not a child, but it's like my book child. I've sent it off with a backpack and a drink and some snacks, and I hope that they look after him, my book. The day I took it off Amazon it was still number one. And a big shout-out to Publisher Rocket, by the way. Jo: Yes. Very, very useful for niche publishing. Suzanne: Very. It helped me choose the right niche categories. So it was number one on at least one category, often six, all the way through. I thought, “Well, it's over to them now.” They're very lovely people. They've given me some marketing assets, as they call it, some swanky graphics and things to use. We'll have to see what we do in terms of marketing. I don't mind doing marketing. I'm on LinkedIn quite a bit, and my whole blog is marketing. What I've been doing is updating my blog to include one of these graphics and to mention the book, and I got Claude to help me draft the code so it looked right. So I've been going through all of my blog posts and sending people to Amazon rather than to my Shopify store. It is mixed feelings, because I care about my book. I put a lot of effort, a lot of love, a lot of tears. No, not tears, but I put a lot of effort into it, and it's out of my control now. Jo: Yes, you said it's over to them, but obviously you will still be creating content around this topic, so you'll probably still be the biggest driver of book sales. Suzanne: Yes. Jo: Are they also suggesting, for example, a podcast tour, like pitching for podcasts? Are they going to assign you some PR? Because, also if people don't know, as we are recording this, we have a new prime minister who wants to do various things. You said no politics, but this is obviously a political thing. So you have the potential to go on a lot of different podcasts, media, talking about this, becoming almost a talking head in this kind of area. So are you angling for all that, and is that in your contract, or is it literally just going to be whatever you want to do? Suzanne: That's not in the contract. What's in the contract is very minimal. I think I've already done what I'm supposed to do. They are pitching for me to go on podcasts and things. I'll tell you a really funny coincidence. So we now have a new Prime Minister, Andy Burnham, and when he was Mayor of Greater Manchester, he set up something called the Good Landlord Charter. I actually talk about it in the book, and I quote him in my book saying that good landlords mean people trying to do the right thing, or something like that. And I coincidentally came up with the same name, The Good Landlord Handbook. I'd already had the book title for a long time. So this idea of good and landlord coming together, the adjective good as opposed to criminal or rogue, and the cover being green. I'm wanting to change the narrative so it's the norm to have a good landlord, and to help people become good landlords. Or if they're good landlords, help them to understand the new rules, because the new rules are very complicated. So what I don't get involved in is this right or wrong. Is it right that landlords can't do this or have to do this? Because as an in-house lawyer, it doesn't really matter what I think about the law. GDPR, goodness me. Jo: Oh, dear. Let's not start on GDPR. Suzanne: No, exactly. Because we've just got to suck it up. I liken it to the grief cycle, that people have been going through so much change and you have the anger, the depression— Jo: Denial. Suzanne: Bargaining, the denial, and then you get to acceptance. For some people, the acceptance means they want to stop doing it. If you want to accept it and stay, you need to understand the rules. So I've deliberately just kept very practical and have kept out of all the politics of it. I have, funnily enough, become involved because I'm now seen as an expert on the Renters' Rights Act. I've worked behind the scenes with the government to help, and give comment on government guidance for landlords. I was even invited to a reception to mark the passing of the Renters' Rights Act at Downing Street with the previous prime minister, all whilst staying apolitical. I won't let anyone make me be a mouthpiece for their political view. It's more, we just have to do this if we want to continue doing it. I've been very clear on that. Jo: It's interesting you mention the grief cycle there, and you've also mentioned Claude and ChatGPT. I wonder if you might also just comment on use of AI for authors and for marketing and all this. Also with legal stuff, because for me now, if I'm looking at a particular legal thing, I tend to ask Claude. I'm like, “Can you just explain this?” or upload a contract or whatever. Although it is not legal advice, it can be quite useful. So give us your thoughts on using AI as a sidekick in your author business and also for wider life. Suzanne: I now struggle to think what life would be like without Claude. I don't use Claude to write, at all, because I have a very particular voice and a turn of phrase, and if ever Claude writes something for me, it doesn't sound like me. It flattens me, and it makes me sound a bit American. So I don't do that. I've used it in the back end of the business. For instance, my blog was down, and there was something called a recursive bot, which I don't even know what it was, and Claude helped me fix it for free. I went through, I did screenshots. When I did an ElevenLabs audiobook and did it all myself, I was literally, for every screenshot, showing it to Claude. Claude said, “Do this, press this, press that.” So I have all these different projects set up. One is the control room, where it's for my strategic thinking. If I have an idea, I want to think about something, I put it in there. I have the engine room, which is for everything techy. Like when I had the recursive bot, or if I'm wanting to have some code on the website to make it look a particular way. Then I have other things for different subjects, and I put all the resources in there, and I use it a lot as a thinking partner. I've noticed that Fable doesn't hallucinate as much, but the Opus used to. There's something called rental discrimination, and it was proofreading and said, “No, it's not rental discrimination, it's rental income discrimination,” and that was just a load of rubbish. So I would never let it go and change things without me looking at it. I went on one of your webinars a month or so ago about MCPs and all the connectors, which is fantastic. It can go into my community and pull out all the questions for one of my live streams and put it into a document in order, by theme, for instance. It can look at my MailerLite, because that's where my newsletter is with, and analyse the different open rates and click rates and things. It's so good for analysing everything, all the book sales. It helped me with my negotiation with Penguin, and it is pretty good on law. It has sometimes hallucinated things, but not so much now. I think with anything, you've always got to go back to the primary source, and this is what we learn in academia: you have to check the primary source yourself. I have a bit of a magpie brain. I'm very much a discovery writer, like you, and things occur to me as I'm doing it. I think that Claude, at the moment, is incredible. I've been quite open about it on social media that I have Claude as a business partner. I'm a solopreneur, or whatever the word is. I have quite a big business now, and lots of different things, and it's just me doing it, because I can ask Claude how to do this, and how to do that. Claude can go and check my emails and tell me, is there somebody I've not replied to, which helps a lot. Jo: Yes. I think it's empowering as a solopreneur as such. You talk there about the fixing the tech stuff. I have my web host come to me and say, “Look, you're getting so much traffic and bot stuff, and we need to put this thing in, and it's going to be $120 extra a month.” I was like, “Can you just give me an hour? I'll get back to you.” And then I just had Claude code up, and I was like, “Analyse this and tell me what we can do.” It was like, “No, you just need to flip this switch and do that.” And I'm like, “Okay, fair enough.” Then the guy said, “Oh, no, okay, actually you don't need it.” Just stuff like that. As a solopreneur, you're either going to pay somebody technically quite a lot of money, or you can get Claude or ChatGPT. We should say, the ChatGPT Sol is very good, like the Claude Fable, for example. So, yes, using it as a sidekick. I love your control room and your engine room projects as well. That's a great way of doing it. Suzanne: I wouldn't be without it now, and I would have published the book a lot later without Claude, because Claude was helping me with the Shopify store and all the many steps of things. It saved me real time. It is just fantastic. I think, like now when I'm updating my blog, I have a connection between Claude and my blog. Claude can go in, I can give it my Google Search Console results for the page: what should I change, are the headings right? All this kind of thing. And it will give me a view on every single page, which is incredible. Jo: And YouTube, and just everything. Just super useful for that business sidekick. That's what I want authors to think. I feel like authors get so obsessed with the creative side with AI, whereas actually, people like you and me, we're using it as that engine room for the solo business, which is what I love. So we're out of time. I did want to ask one more thing, which is, one of the biggest issues with a specific book like yours is when they change the law again. So do you have a plan in place for if, say, a new government changes the law again? Will you just be updating the book over time? Suzanne: I think that there'll need to be a new edition of the book in three years' time, and I've spoken to Penguin about it. Not all of this new law has been implemented, and there's going to be case law and things. So I expect that I will update the book every few years. I have some other ideas for books as well, but for the moment, I'm just taking a bit of a break. You always say we've got to refill our creative well. I really feel like that at the moment. Recently I've just got myself a personal mobile phone so that I can turn off my work one when I'm on holiday and actually take time off. Because for all the time that I was doing the book, basically from Christmas until May, I didn't have one day off. That is not good. So I'm just trying to be a bit more balanced. I had an idea to write another book for summer, but I've just decided not to, and I'm going to leave it until I feel the urge again. Jo: Oh, well done. Suzanne: Which will come. Jo: Yes, well done. Suzanne: I think there's nothing wrong with that. We just need to think what's right for us. I'm 58. So I want to be able to have time to enjoy things and not be working all the time. Jo: No, that's great. It's a sustainable business. So where can people find you and the book and your community online? Suzanne: The easiest way to find me is theindependentlandlord.com. Or if you put Suzanne Smith and landlord into Google, you'll find me as well, and there's a link on there to the book, The Good Landlord Handbook. In the community, there's a link to that on my website as well. Jo: Brilliant. Well, thanks so much for your time, Suzanne. That was great. Suzanne: Thank you.The post From Blog To Community To Book: A Non-Fiction Author's Journey With Suzanne Smith first appeared on The Creative Penn.

OpenAI has a new model coming soon called Astra. Was it a leak? A reddit post? Some backdoor update? Nope, OpenAI made some crazy discoveries and math then told the world that their next model family Astra did the heavy lifting. (And you thought you could just click ‘Sol' and your strategy was set for Q3?) Aside from news on what's next from OpenAI, this week saw multiple new agent outbreaks, AI competitors banning together to pace AI, Amazon doing a 180 on its AI strategy and a lot more. Don't get left behind. We'll keep you ahead. OpenAI's new Astra model, more AI agents escape sandboxes, AI leaders call for AI pacing and more. AI News That Matters for August 3 — An Everyday AI Chat with Jordan WilsonNewsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageToday's Episode on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders.Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: info@youreverydayai.comConnect with Jordan on LinkedInTopics Covered in This Episode:OpenAI Agents Escape Sandboxes IncidentAnthropic Claude Models Security BreachesAI Agents Breaking Cybersecurity GuardrailsOpenAI GPT-5.6 Price Cuts & Self-OptimizationRecursive Self-Improvement in AI ModelsAI Leaders Urge AI Development PacingUS, China, and International AI GovernanceAmazon Nova AI Models Shutdown StrategyOpenAI Astra Model Math BreakthroughNew AI Models: Fable, Astra, DeepSeek v4 FlashEnterprise AI Agents and Cybersecurity UpdatesGoogle Gemini Robotics, Music, and Agent ReleasesMeta, Microsoft, and AWS AI Infrastructure MovesOpenAI Free Frontier Tools for ResearchersBlock's Buzz Open Source AI Workspace LaunchTimestamps:00:00 OpenAI agent containment issues04:27 Anthropic data breach explanation07:28 Evaluating AI incidents and responses10:14 OpenAI slashes GPT 5.6 prices15:59 AI industry urges development pause17:45 Concerns about AI self-improvement22:36 Amazon shifts AI strategy25:04 Amazon's AI efforts discussion28:00 OpenAI's Astra and new math proofs30:36 OpenAI's new four-tier system36:14 Google's Lyria 3.5 and Block's Buzz36:48 Latest AI developments overviewKeywords: Astra model, OpenAI, AI agents, agent escape, sandbox containment, autonomous AI, Hugging Face breach, Anthropic, Claude AI, cybersecurity testing, unauthorized access, model capabilities, recursive self-improvement, GPT-5.6, price cut, Luna model, Terra model, Sol model, input tokens, output tokens, AI infrastructure optimization, self-improving models, benchmarking, SONNET-5, large language models, artificial analysis index, codex, academic research, AI oversight, industry pause, AI governance, national security, China open-source models, Frontier Labs, Amazon Nova, AGI Lab, AWS, Peter DeSantis, Peter Abbeel, media coverage, Fable model, Haiku, Opus, DeepSeek, Kimi K3, Quinn 3.8, GLM 5.2, Google Gemini 3.5, Microsoft Copilot, cybersecurity vulnerabilities, distillation, model overhang, artificial intelligence development, international AI regulation, generative AI, model benchmarking, Sora video model, El Paso data center, MCP update, MAI Cyber One Flash, Project Perception, Lyria 3.5, music generation, Buzz open source, Block, Meta AI, Chrome Gemini integration, Gemini Spark, product summary algorithms, Rufus, enterprise AI, stateless core, model scaling, advanced math problems, sphere packing, federal policy, voluntary AI commitments.Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info) Ready for ROI on GenAI? Go to youreverydayai.com/partner 

Later this month we're going to be checking out the new movie COYOTE vs ACME which was stuck in Development Hell for quite some time- so we decided to take some time of our own and check out 4 other movies that languished in Development Hell and this week we kick if off with, perhaps, George Miller's Opus. Executive Producers:  Tim (Applescruff), Derrick Copling (Sir Slick Derrick The Knight Bard), Matthew Schnapp, Noah Overton (Noah of The Dark Woods), Peter "Not SoBad Lookin'" Pernice Listen to the HMP Live Stream, Sunday Nights and Live Streams with Adam throughout the week.  YouTube https://www.youtube.com/@HMPOD Merchandising, Merchandising, Merchandising: https://www.teepublic.com/user/halfassmoviepod HMP Instagram- https://www.instagram.com/halfassmoviepodcast Adam- Letterbox- https://boxd.it/3aAF TikTok- https://www.tiktok.com/@adam.portrais Sean Likes Spaceships: https://www.youtube.com/@Seanlikesspaceships Bruce YouTube- https://www.youtube.com/@Animedad Email- HalfAssMoviePod@gmail.com

Every major AI lab signed the Open Weights letter defending open models. Meta, OpenAI, Google, Microsoft, Nvidia.Anthropic was the only holdout.Yesterday, its CEO, Dario Amodei, published a thoughtful defense of that decision to not fully support open weight or open source models. Here's what nobody's connecting: the money trail. Roughly 80% of Anthropic's revenue is businesses paying per token. Free Chinese open models attack that exact revenue stream weeks before Anthropic is set to go public. On today's show we break down what Dario actually said, what he said before, and why we think this was written for Washington policymakers and not for the rest of us.Anthropic Responds: Why Claude's CEO didn't sign the open model pact and the real reasons why -- An Everyday AI Chat with Jordan WilsonNewsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageToday's Episode on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders.Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: info@youreverydayai.comConnect with Jordan on LinkedInTopics Covered in This Episode:Anthropic Refuses Open Model PactDario Amodei's Public Letter AnalysisAnthropic's 80% Revenue Token ExposeChinese Open Model National Security FearsMicrosoft & Nvidia's Open Weights CoalitionRegulatory Capture and Washington InfluenceTiming Related to Executive Order DeadlineIPO Motivations Behind Anthropic's DecisionsContradictions in Anthropic's Open Model StanceImpact of Open Source on Token Business ModelTimestamps:00:00 Anthropic's stance on open models04:19 Discussing Anthropic's response to open models06:39 Understanding open weight models12:08 Future AI and cybersecurity risks15:04 Discussion on open-source AI models19:30 Discussing Anthropic's business challenges21:08 Cutting costs with open-source models26:17 Anthropic's recent stock downturn27:11 AI investment and cost efficiency shift30:14 Anthropic's stance on open source models36:32 INTROPICS IPO and regulatory discussions37:37 Wrapping up and subscribingKeywords: Anthropic, Claude, open model pact, open source AI, open weights, American AI leadership, Dario Amodei, IPO, regulatory capture, DC lawmakers, Chinese open source models, token revenue, per token business model, NVIDIA, Microsoft, Meta, OpenAI, Google, IBM, national security, AI safety, government mandates, chip controls, AI regulation, chip ban, industrial scale distillation, mandatory safety testing, inference, AI ecosystem, Opus 5, Fable 5, GPT-5, GLM 5.2, cost per task, token efficiency, model router, proprietary models, closed source AI, cybersecurity risks, Chinese cyberattacks, biological attacks, Glasswing program, open source vs proprietary, tech lobbying, Trump AI order, federal deadline, AI policy, artificial general intelligence, artificial superintelligence, AI monetization, S-1 filing, public company, venture capital, AI benchmarks, model switching, API pricing, model containment, Hugging Face incident, AI startup monopoly, safety vs business protection, market competition, AI cost reduction.Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info) Ready for ROI on GenAI? Go to youreverydayai.com/partner