Gemini 3 Hype, GPT 5.1 Updates, & The Future of Custom GPTs

Brian and Andy opened the show reacting to Gemini 3’s release, noting how quickly Google pushed it out after weeks of leaks. They framed the episode around three big storylines: Gemini 3 going live, the Prometheus project finally confirmed, and a wave of world model announcements across the industry.Key Points DiscussedGemini 3 officially launches with big jumps in reasoning, vision, and real time grounding.Google positions Gemini 3 as a direct competitor to GPT 5.1 and Claude 3.7.Early tests show major improvements in planning and tool use, but hallucinations still appear in edge cases.Jeff Bezos backs the Prometheus physical AI project, aiming to merge robotics, sensors, and world models.Elon Musk announces Grok 4.1, claiming large upgrades in memory and multi step reasoning.Nvidia reveals Apollo, a physics aligned world model intended for robotics and simulation.Debate over whether world models will replace Transformers or merge into hybrid systems.Anthropic updates Claude to improve tool calling and reduce slowdowns seen over the last week.New research shows world model agents may outperform LLM agents in long horizon tasks.Discussion on AI ecosystems pulling away from single model usage and toward fully integrated systems.Timestamps & Topics00:00:00 💡 Intro and Gemini 3 launch00:04:22 🤖 First reactions to Gemini 3 performance00:09:48 ⚙️ Tool use improvements and early benchmark noise00:13:40 🔍 Comparing Gemini 3 to GPT 5.1 and Claude00:17:22 🚀 Prometheus project confirmed with Bezos backing00:21:11 🤝 Robotics, sensors, and world model integration00:26:34 🔧 Grok 4.1 announcement and memory upgrades00:30:18 🧠 Nvidia Apollo and physics aligned world models00:35:42 🔬 World model agents vs LLM agents00:41:00 📉 Claude slowdown issues and Anthropic fixes00:47:29 🌐 Shift from single models to integrated ecosystems00:54:10 🏁 Wrap up and preview of midweek topicsThe Daily AI Show Co-Hosts: Andy Halliday, Beth Lyons, Brian Maucere, Eran Malloch, Jyunmi Hatcher, and Karl Yeh

Brian and Beth opened the week talking about post-travel exhaustion, holiday timing, and the usual Monday scramble before diving into the fast-moving AI news cycle. They framed the episode around two big topics: Gemini 3 and GPT 5.1, both expected to shape the competitive landscape going into the end of the year.Key Points DiscussedGemini 3 hype grows as leaks point to a major leap over 2.5 Pro.Nate Jones claims Google may take the top spot for model quality for the first time.Benchmark saturation makes performance harder to judge, so real workflow testing now matters more.Concerns rise about switching costs as models continue to leapfrog each other.Discussion on Kimi, DeepSeek, and recycled media hype around “low cost” training claims.GPT 5.1 rollout improves instruction following and reduces jargon, but shifts may break existing custom GPT setups.Issues with user preferences, model selection, and memory overriding developer-built instructions.Prediction that custom GPTs and Gems may evolve into more structured, code-like agents built through vibe-coding style interfaces.Exploration of how ecosystems (Google, Microsoft, OpenAI) may soon matter more than the standalone model.Sakana AI becomes the most valuable private company in Japan.Reflection on how quickly the AI industry has changed public visibility for figures like Jensen Huang.Conversation on enterprise-grade update cycles and the future of agent maintenance.Apple expected to benefit from Gemini integration as Siri gets significantly stronger with minimal user friction.Timestamps & Topics00:00:00 💡 Monday kickoff and holiday timing00:03:07 🤖 Gemini 3 expectations and early leaks00:05:49 🔍 Google catching OpenAI for the first time00:08:02 🧪 Benchmark saturation and real-world testing00:10:16 🔄 Switching fatigue and user lock-in00:11:22 📉 Kimi, DeepSeek, and misleading training cost narratives00:15:15 ⚙️ GPT 5.1 updates and instruction-following improvements00:18:12 🧩 Problems with custom GPT triggers and file handling00:19:41 🔧 Skill-building workflows with Claude vs GPT 5.100:22:56 🔗 Tool clutter and connector issues in ChatGPT00:23:28 🧠 Google Gemini integrations and AI Studio00:24:57 🃏 Gemini 3 hype and online exaggerations00:27:22 🧬 Microsoft’s superintelligence lab and safety stance00:28:10 👤 Public persona shifts in the AI industry00:33:17 🚀 Sakana AI becomes Japan’s highest-valued private company00:37:25 🧠 Future of custom GPTs and vibe-coded agent systems00:43:04 🔐 Persistent memory challenges for developer-built tools00:52:40 🗂️ Agent-based onboarding and learning systems00:55:07 🌐 Full-ecosystem advantage for Google00:56:53 📱 Apple expected to benefit from Gemini-powered Siri01:00:02 🧩 The real competition is the ecosystem, not the model01:01:14 🏁 Wrap-up and after-show banterThe Daily AI Show Co-Hosts: Andy Halliday, Beth Lyons, Brian Maucere, Eran Malloch, Jyunmi Hatcher, and Karl Yeh

Shared entertainment has always shaped how people connect. Families once gathered around a single television. College friends planned their week around a show everyone watched at the same time. Movie theatres turned an audience into a temporary community. Even when streaming arrived, the biggest stories still found ways to bring people together for premieres, finales, and cultural moments.AI will not replace that. Big films, concerts, and live events will still matter. But side by side with those experiences, AI will offer something new. It can generate long form movies or albums that match your taste perfectly. You do not wait for them. You do not compromise with anyone. They are delivered instantly, shaped around your favorite pacing, themes, and emotional patterns. It is entertainment that fits like a glove, and it will be hard not to reach for it.As people start to mix both worlds, an uncomfortable tension appears. Tailored stories scratch the immediate itch and feel more rewarding minute to minute. Shared stories ask more from you. They take longer. They do not always match your preferences, yet they create the moments larger than yourself.The conundrum:If AI gives us instant entertainment that feels perfect, will we still choose the slower, shared experiences that once helped us feel connected to something bigger, or will the pull of personal comfort slowly reshape what we show up for? And if our habits shift over time, what happens to the cultural moments that rely on many people choosing the same story at the same time?

Brian and Beth hosted this Friday wrap-up episode, opening with updates about the show’s growth, community, and weekend lineup. They celebrated nearly 600 consecutive weekday episodes and reminded listeners about the Saturday AI Conundrum podcast and Sunday newsletter. From there, the conversation moved through a mix of AI news and cultural stories — covering billion-dollar valuations, AI espionage, chatbot-related divorces, DeepMind’s new Sema-2 model, and Tesla’s workforce challenges.Key Points DiscussedThinking Machines’ $50B Valuation – Former OpenAI CTO Mira Murati’s startup, Thinking Machines Lab, is reportedly seeking a $50B valuation just months after being valued at $12B. The hosts debated whether this surge signals innovation or signs of an AI bubble.AI-Powered Cyber Espionage – Anthropic reported the first known AI-orchestrated cyberattack, traced to a China-based agent network using Claude Code. The team discussed how this lowers the barrier for sophisticated hacking and how most IT teams are unprepared for AI-driven threats.AI Relationships and Divorce Law – A Wired article described rising legal cases where people secretly spend money or form emotional attachments to chatbots. Brian compared this to addiction patterns, while Beth questioned how courts would treat AI-based infidelity versus human-only digital relationships.Google DeepMind’s Sema-2 Breakthrough – The hosts reviewed DeepMind’s new world model built on Gemini, which can generalize learning across simulated 3D environments. Beth explained how Sema-2 represents another step toward embodied AI and spatial reasoning.Tesla’s “Hardest Year” Warning – Tesla’s AI chief told staff that 2026 will be “the hardest year of their lives,” referencing the company’s push to scale both Optimus robots and robotaxis. Beth noted the irony of engineers potentially “building their replacements,” while Brian reflected on the trade-offs between automation and worker safety.Google Photos’ “Nano Banana” AI Editor – Google rolled out new photo-editing capabilities, including facial edits and removal tools. The hosts joked about modern “cutting out” exes from family photos and discussed privacy risks of permanent AI edits.AI in Education & Hiring – Brian shared insights from a local panel where he spoke about AI in small business and education. He argued that skills and portfolios now matter more than degrees. Beth agreed, adding that communication skills and public sharing of projects are the best differentiators for early-career talent.Communication Confidence for Gen Z – They ended with a lighthearted discussion about how confidence and clarity in speech will matter more in a world where humans and AI collaborate side by side.Timestamps & Topics00:00:00 💡 Intro, community updates, and weekend lineup00:04:54 💰 Thinking Machines’ $50B valuation debate00:09:03 ⚠️ Anthropic’s AI cyber espionage report00:17:11 💔 AI chatbots and divorce implications00:25:18 🧠 DeepMind’s Sema-2 and world model learning00:29:17 🤖 Tesla’s “hardest year” and automation pressures00:35:22 📸 Google Photos’ Nano Banana editor00:41:21 🎓 AI in education and hiring insights00:49:00 🗣️ Communication, confidence, and generational skills00:55:00 🏁 Wrap-up and weekend remindersThe Daily AI Show Co-Hosts: Brian Maucere and Beth Lyons

Beth and Andy hosted a packed show covering OpenAI’s new GPT-5.1 release, Google’s private AI compute system, the evolution of world models, and a deep dive into digital twins. The episode explored how AI is moving toward personalization, privacy, embodied intelligence, and the preservation of human knowledge.Key Points DiscussedGPT-5.1 Launch – OpenAI released GPT-5.1 with faster responses, better adherence to instructions, and new built-in personas like Professional, Quirky, or Cynical Nerd. It adds model personalization and allows users to adjust tone and behavior.Personalized AI Behavior – The hosts discussed the importance of AIs that can challenge users instead of just agreeing. They imagined “personality sliders” for blending traits, creating a more balanced AI collaborator.Google’s Private AI Compute – Google’s new Pixel feature isolates personal data from cloud models, echoing Salesforce’s Trust Layer. It enables secure AI functions like photo edits and summaries without exposing private info.World Models and the Rise of Embodied AI – Fei-Fei Li’s World Labs released Marble, a tool that turns text or sketches into editable 3D environments for VR, gaming, and robotics. A new Middle Eastern research lab unveiled Pan, a world model that merges language, vision, and action while separating reasoning from perception for better realism.Data Center Economics – Microsoft’s $5B inference bill with Azure raised concerns about AI’s unsustainable costs. Andy noted OpenAI’s inference expenses now far exceed revenue, creating pressure for price adjustments or new business models.Geoffrey Hinton’s Warning – The “Godfather of AI” reiterated that the math doesn’t work unless automation reduces headcount, reviving conversations about universal basic income (UBI).Digital Twins and Human Knowledge Preservation – Beth introduced insights from Cindy Coons and Paul Roetzer on creating AI versions of individuals for consulting, business continuity, or legacy preservation.Applications for Digital Twins – Andy outlined three categories: corporate knowledge retention, influencer or expert scaling, and personal legacy storage.Challenges and Risks – The process is time-intensive, expensive, and relies on platform survival. Andy shared lessons from his early startup OurStory.com, which lost user data after being acquired.Top Digital Twin Startups – Andy listed five emerging players:Delphi AI – Used by Harvard Business School and Arnold Schwarzenegger.UARRE AI (formerly Eternals) – Focused on creators and professional legacy.Vivian – Builds digital twins for employees in enterprises.Personal AI – Offers edge-based, locally stored personal models.MindBank AI – Creates quick video-based twins and uses AI interviewers for continuous knowledge capture.Future Vision – Beth imagined digital twins as interactive journals or consulting tools that think and respond like their human counterparts, expanding how we define digital presence.Timestamps & Topics00:00:00 💡 Intro and GPT-5.1 release00:03:30 🧠 Model personas and user customization00:09:00 🎛️ Personality sliders and creative control00:11:00 🔒 Google’s private AI compute and data trust00:12:20 🌍 Fei-Fei Li’s Marble world model00:16:40 🧩 Pan world model from the Middle East00:22:28 🏗️ Microsoft’s super-factory and inference costs00:27:31 💰 Hinton’s automation and UBI discussion00:29:06 🧍 Digital twins overview and use cases00:34:21 🧠 Corporate vs. personal knowledge preservation00:45:06 💾 Top 5 digital twin platforms00:57:12 🪞 Future of self-consulting and legacy AI01:00:44 🏁 Closing remarks and preview of next episodeThe Daily AI Show Co-Hosts: Beth Lyons, Andy Halliday, and guest commentary from community members

Beth returned from the Create Conference 2025 to co-host with Andy, kicking off a wide-ranging episode on global AI investments, model development, and the next frontier in computing. They discussed SoftBank’s Nvidia sell-off, Microsoft’s “humanist AI” stance, Yann LeCun’s new company, OpenAI’s upcoming group chat feature, and several major breakthroughs in quantum computing.Key Points DiscussedSoftBank Exits Nvidia – Masayoshi Son sold SoftBank’s $6B Nvidia stake to fund new OpenAI and Stargate investments. The hosts debated whether this was profit-taking or a strategic reallocation.Microsoft’s Humanist AI Vision – Mustafa Suleyman announced Microsoft’s commitment to “humanist AI,” while Elon Musk countered that robotic labor is inevitable. Beth compared ownership structures and how control influences AI direction.Yann LeCun Leaves Meta – Meta’s Chief AI Scientist left to launch a new company focused on world models — spatial intelligence systems designed to understand and interact with 3D environments.World Model Race – The team discussed Fei-Fei Li’s World Labs, Google DeepMind’s Genie models, and Nvidia’s Spatial Intelligence Lab, all aiming to build next-generation embodied AI for robotics.China’s $1.30 Coding Agent – ByteDance unveiled an AI coding assistant that rivals U.S. developer tools like Cursor, setting records on SWE-bench and handling 256K tokens per query for just $1.30 per month.Claude Use Case Library – Anthropic launched a searchable /resources/use-cases hub to help users discover practical AI workflows from legal research to financial analysis.11 Labs’ Iconic Voice Marketplace – 11 Labs released licensed AI recreations of historical and cultural figures like Michael Caine, Maya Angelou, and Amelia Earhart, raising questions about consent, nostalgia, and ethics in digital likeness.Quantum Simulation Milestone – A European team simulated a 50-qubit logical quantum computer using Nvidia G200 superchips, quadrupling prior benchmarks and advancing hybrid classical-quantum computation.Continuum’s Quantum Breakthrough – The new Helios machine converts 98 physical qubits into 48 logical ones, improving fault tolerance and paving the way for stable, room-temperature quantum systems.Infrastructure Bottlenecks – Andy noted that the biggest constraint on AI growth isn’t chips but construction materials like sand and concrete, which are delaying new data centers.Timestamps & Topics00:00:00 💡 Intro and SoftBank exits Nvidia00:04:39 🤖 Microsoft’s “humanist AI” vs. Musk’s robot inevitability00:06:41 🧠 Yann LeCun leaves Meta to build world models00:10:13 🌍 Fei-Fei Li’s World Labs and embodied AI00:21:20 🇨🇳 China’s $1.30 coding agent00:28:31 💡 Efficient training and model cost debate00:28:50 🧩 Claude’s new use-case library00:31:13 🎙️ 11 Labs launches iconic voice marketplace00:39:56 ⚛️ Quantum computing breakthroughs and Helios machine00:49:07 ⚙️ Energy, data center, and material constraints00:51:44 🧍‍♂️ Digital twins preview for next episodeThe Daily AI Show Co-Hosts: Beth Lyons and Andy Halliday

Brian, Andy, and Jyunmi kicked off the show with a quick Veterans Day thank-you before diving into one of the most science-heavy shows in recent weeks. Topics ranged from AI-assisted dementia detection and brain decoding to new tools for developers and learners — including Time Magazine’s new AI archive and a deep dive into Google NotebookLM’s new mobile features.Key Points DiscussedAI in Dementia Detection – A new study published in JAMA Network Open showed that embedding AI into electronic health records raised dementia diagnoses by 31% and follow-ups by 41%, proving AI can catch early warning signs in real-world clinics.AI Brain Decoder – Scientists used a noninvasive brain scanner to let AI accurately describe what participants were seeing — even recalling or imagining actions like “a dog pushing a ball.” The group marveled at its potential for neurocommunication and ethical implications.Lovable Hits 8 Million Users – The team discussed the rapid growth of Lovable and its no-code app-building platform, with Brian and Andy sharing personal experiences building and managing credits within the tool.Time Magazine’s AI Agent – Time launched an AI trained on its 102-year archive, allowing users to query 750,000 stories in 13 languages. The hosts applauded the idea as “the new microfiche” and a model for how legacy media can use AI responsibly.China’s Kimmi K2 Thinking Model – Andy explained how Moonshot Labs’ open-source reasoning model outperforms GPT-5 in long-form tasks while costing under $5M to train. It’s available via LMGateway.io, which lets developers access multiple AI models through one API.Dr. Fei-Fei Li on Spatial Intelligence – Briefly previewed for a future episode, her new paper explores spatial reasoning as the next frontier of AI cognition.Google NotebookLM’s Mobile App Update – Major new features include chat synchronization, flashcards, quizzes, selective source control, and a 6× memory boost for longer learning sessions.Chrome Extensions for NotebookLM – Two standout add-ons:NotebookLM to PDF – Saves chat threads as PDFs to add back as notebook sources.YouTube to NotebookLM – Imports entire YouTube playlists or channels for instant research and study integration.Tool of the Day – TLDR.wtf (Too Long, Don’t Watch) – A single-developer app that creates highlight reels of long YouTube videos by extracting the highest-signal moments based on transcript analysis.Live Test on the Show – Brian tried TLDR on a past Daily AI Show episode in real time. It instantly generated timestamped highlight chapters, impressing the team with its speed and potential for content creators.Timestamps & Topics00:00:00 🇺🇸 Veterans Day intro00:03:00 🧠 AI-assisted dementia detection study00:06:07 🧩 Noninvasive brain decoder00:11:00 💻 Lovable reaches 8M users00:15:11 🗞️ Time Magazine’s AI archive00:19:03 🇨🇳 Kimmi K2 Thinking open-source model00:25:14 🧠 Fei-Fei Li’s spatial intelligence preview00:26:29 📚 Google NotebookLM mobile app update00:31:21 🧩 Chrome extensions for NotebookLM00:37:41 🎥 TLDR.wtf highlight tool demo00:45:54 🏁 Closing notes and live-stream mishapThe Daily AI Show Co-Hosts: Brian Maucere, Andy Halliday, and Jyunmi Hatcher

Brian and Andy opened the week discussing how AI agrees too easily and why that’s a problem for creative and critical work. They explored new studies, news stories, and a few entertaining finds, including a lifelike humanoid robot demo and the latest State of AI 2025 report from McKinsey. The episode ended with a detailed discussion about Tony Robbins’ new AI bootcamp and the marketing tactics behind large-scale AI education programs.Key Points DiscussedAI’s Sycophancy Problem – A Stanford study showed chatbots often treat user beliefs as facts. Brian and Andy discussed how models over-agree, creating digital echo chambers that reinforce a user’s thinking instead of challenging it.Building AI That Pushes Back – They explored multi-agent designs that include critic or evaluator agents to create debate and prevent blind agreement. Brian shared how he builds layered GPTs with feedback loops for stronger outputs.Gemini’s Pushback Example – Brian described a test with Gemini where the model warned him not to skip warm-ups before running. It became a good example of gentle, fact-based correction that AI needs more of.AI Water Usage and Context – The hosts discussed how headlines exaggerate AI’s energy and water use. One Arizona county’s data center uses only 0.12% of local water versus golf courses’ 3.8%, showing why context matters in reporting.The Neuron Newsletter Sold – Andy revealed that The Neuron, one of AI’s biggest newsletters, was sold to Technology Advice in early 2025 after reaching 500,000 subscribers.Realistic Robot Demo – They reviewed a Chinese startup’s viral humanoid robot video that looked so human the team had to cut it open on stage to prove it wasn’t a person.McKinsey’s State of AI 2025 Report – Carl summarized the key findings: AI is widely adopted but rarely transformative yet. Companies still struggle to embed AI deeply into operations despite universal use.Perplexity and Comet Updates – Andy noted Comet’s major upgrade, allowing its assistant to view and process multiple browser tabs at once for complex tasks.AI Creativity: “Minnesota Nice” Short Film – Brian highlighted a one-person AI film project praised for consistent characters and cinematic style, showing how far AI storytelling tools have come.Higgsfield’s “Recast” Feature – Andy shared news of a new video tool that swaps real people with AI characters, blending live footage and generated animation seamlessly.Tony Robbins’ AI Bootcamp Debate – The group examined the recent 100,000-person Tony Robbins “AI Advantage” webinar. They agreed it was mostly a sales funnel for a $1,000 AI course promising “digital clones” of attendees.Sabrina Romano, Rachel Woods, and Ali Miller delivered valuable sessions but later clarified they weren’t instructors in the paid program.The hosts discussed affiliate marketing structures, high-pressure sales tactics, and the growing wave of AI “get rich quick” schemes online.Timestamps & Topics00:00:00 💡 Intro and Stanford study on AI belief bias00:06:00 🤖 Sycophancy and why AI over-agrees00:09:45 🧩 Building AI agents that critique each other00:17:30 🏃 Gemini’s safety pushback example00:19:40 💧 AI water use myths and data center context00:22:15 📰 The Neuron newsletter ownership change00:24:20 🤖 Viral humanoid robot demo from China00:27:39 📊 McKinsey’s State of AI 2025 findings00:31:17 🌐 Comet browser assistant upgrade00:35:39 🎬 “Minnesota Nice” AI short film00:38:27 🎥 Higgsfield’s new Recast tool00:41:08 🧠 Tony Robbins’ AI Advantage breakdown00:53:45 💼 Affiliate marketing and AI course culture00:54:34 🏁 Wrap-up and preview of next episodeThe Daily AI Show Co-Hosts: Brian Maucere, Andy Halliday, and Karl Yeh

Data marketplaces evolve so people can sell narrow, time-limited permissions to use discrete behaviors or signals. Think one-week location access, one-month shopping patterns, one-off emotional tags that are creating real income for those who opt in. This market gives individuals bargaining power and an income stream that flips the usual extraction model, it can fund people who now choose what to trade. Yet turning consent into currency risks making privacy a class good, pushing the poorest to sell away long-term autonomy, while normalizing transactional consent that masks future harms and networked profiling.The conundrum:If selling microconsent empowers people economically and reduces opaque exploitation, do we let privacy become a tradable asset and regulate the market to limit coercion, or do we keep privacy non-transferable to protect social equality, even if that denies some people a real source of income?

Brian, Andy, Beth, and Karl wrapped up the week with news ranging from Elon Musk’s massive new Tesla compensation package to Google’s latest Gemini API updates. The episode also featured lively discussions about AI’s role in education and work, Google’s new file search and maps features, and a full training segment from Karl on how AI fluency is becoming the real differentiator inside companies.Key Points DiscussedElon Musk’s $1 Trillion Tesla Package – Tesla shareholders approved Musk’s new compensation deal tied to milestones like selling one million Optimus robots. The team questioned its fairness and Musk’s growing influence after a SpaceX ally was appointed NASA administrator.XAI Employee Data Controversy – Reports surfaced that xAI employees were required to provide facial and voice data to train its adult chatbot persona, raising privacy and consent concerns.Google Maps + Gemini – Google added conversational features to Maps, such as describing landmarks (“turn right after Chick-fil-A”) and answering live questions about locations or crowd activity.Gemini API File Search – Google launched a new Retrieval-Augmented Generation (RAG) system with free storage and pay-per-embedding pricing, making large-scale document search cheaper for developers.AI + Travel Vision – Brian imagined future travel apps combining Maps, RAG, and real-time narration to create dynamic AI “road trip guides” that teach local history or create interactive family games.Google’s Ironwood TPU – Google unveiled its 7th-gen tensor processing unit, outperforming Nvidia’s Blackwell chips with 42 exaflops of compute power.OpenAI Clarifies Government Backstop Rumor – Sam Altman denied reports that OpenAI sought government financial guarantees, calling prior CFO remarks “misinterpreted.”Meta’s Stock Drop and AI Struggles – Meta lost 17% of its value amid doubts about its AI investments, weak Llama 5 performance, and internal leaks revealing that 10% of ad revenue came from fraudulent ads.AI Training & Fluency Segment (Karl’s Workshop) –Most companies train for tools, not problem-solving with AI.The real skill is AI fluency — knowing what’s possible and how to decompose problems across multiple models.Tool combinations (Claude + GenSpark + Runway) can outperform single tools but require cross-platform knowledge.“AI Ops” roles may emerge to connect experts and models, similar to RevOps or DevOps.Companies need internal “AI champions” who can translate use cases and drive adoption across teams.Timestamps & Topics00:00:00 💡 Intro and Tesla’s trillion-dollar stock package00:08:14 ⚠️ xAI biometric data controversy00:09:22 🗺️ Google Maps + Gemini conversational updates00:12:34 🔍 Gemini API File Search announcement00:15:38 🚗 AI travel guide and storytelling idea00:21:25 ⚙️ Google’s Ironwood TPU surpasses Nvidia00:25:31 🧾 OpenAI backstop clarification00:26:19 📉 Meta’s 17% stock drop and fraud ad report00:31:35 🧠 Karl’s AI fluency and training segment00:49:27 💼 The rise of AI Ops and internal champions00:58:03 🏁 Wrap-up and community shoutoutsThe Daily AI Show Co-Hosts: Brian Maucere, Andy Halliday, Beth Lyons, and Karl Yeh