Would You Trust an AI to Diagnose You? (Ep. 441)

The team takes a breather from the firehose of daily drops to look back at the past two weeks. From new model releases by OpenAI and Google to AI’s evolving role in medicine, shipping, and everyday productivity, the episode connects dots, surfaces under-the-radar stories, and opens a few lingering questions about where AI is heading.Key Points DiscussedOpenAI’s o3 model impressed the team with its deep reasoning, agentic tool use, and capacity for long-context problem solving. Brian’s custom go-to-market training demo highlighted its flexibility.Jyunmi recapped a new explainable AI model out of Osaka designed for ship navigation. It’s part of a larger trend of building trust in AI decisions in autonomous systems.University of Florida released VisionMD, an open-source model for analyzing patient movement in Parkinson’s research. It marks a clear AI-for-good moment in medicine.The team debated the future of AI in healthcare, from gait analysis and personalized diagnostics to AI interpreting CT and MRI scans more effectively than radiologists.Everyone agreed: AI will help doctors do more, but should enhance, not replace, the doctor-patient relationship.OpenAI's rumored acquisition of Windsurf (formerly Codium) signals a push to lock in the developer crowd and integrate vibe coding into its ecosystem.The team clarified OpenAI’s model naming and positioning: 4.1, 4.1 Mini, and 4.1 Nano are API-only models. o3 is the new flagship model inside ChatGPT.Gemini 2.5 Flash launched, and Veo 2 video tools are slowly rolling out to Advanced users. The team predicts more agentic features will follow.There’s growing speculation that ChatGPT’s frequent glitches may precede a new feature release. Canvas upgrades or new automation tools might be next.The episode closed with a discussion about AI’s need for better interfaces. Users want to shift between typing and talking, and still maintain context. Voice AI shouldn’t force you to listen to long responses line-by-line.Timestamps & Topics00:00:00 🗓️ Two-week recap kickoff and model overload check-in00:02:34 📊 Andy on model confusion and need for better comparison tools00:04:59 🧮 Which models can handle Excel, Python, and visualizations?00:08:23 🔧 o3 shines in Brian’s go-to-market self-teaching demo00:11:00 🧠 Rob Lennon surprised by o3’s writing skills00:12:15 🚢 Explainable AI for ship navigation from Osaka00:17:34 🧍 VisionMD: open-source AI for Parkinson’s movement tracking00:19:33 👣 AI watching your gait to help prevent falls00:20:42 🧠 MRI interpretation and human vs. AI tradeoffs00:23:25 🕰️ AI can track diagnostic changes across years00:25:27 🤖 AI assistants talking to doctors’ AI for smoother care00:26:08 🧪 Pushback: AI must augment, not replace doctors00:31:18 💊 AI can support more personalized experimentation in treatment00:34:04 🌐 OpenAI’s rumored Windsurf acquisition and dev strategy00:37:13 🤷‍♂️ Still unclear: difference between 4.1 and o300:39:05 🔧 4.1 is API-only, built for backend automation00:40:23 📉 Most API usage is still focused on content, not dev workflows00:40:57 ⚡ Gemini 2.5 Flash release and Veo 2 rollout lag00:43:50 🎤 Predictions: next drop might be canvas or automation tools00:45:46 🧩 OpenAI could combine flows, workspace, and social in one suite00:46:49 🧠 User request: let voice chat toggle into text or structured commands00:48:35 📋 Users want copy-paste and better UI, not more tokenization00:49:04 📉 Nvidia hit with $5.5B loss after chip export restrictions to China00:52:13 🚢 Tariffs and chip limits shrink supply chain volumes00:53:40 📡 Weekend question: AI nodes and local LLM mesh networks?00:54:11 👾 Sci-Fi Show preview and final thoughtsThe Daily AI Show Co-Hosts: Jyunmi Hatcher, Andy Halliday, Beth Lyons, Brian Maucere, and Karl Yeh

Want to keep the conversation going?Join our Slack community at dailyaishowcommunity.comIntroWith OpenAI dropping 4.1, 4.1 Mini, 4.1 Nano, O3, and O4-Mini, it’s been a week of nonstop releases. The Daily AI Show team unpacks what each of these new models can do, how they compare, where they fit into your workflow, and why pricing, context windows, and access methods matter. This episode offers a full breakdown to help you test the right model for the right job.Key Points DiscussedThe new OpenAI models include 4.1, 4.1 Mini, 4.1 Nano, O3, and O4-Mini. All have different capabilities, pricing, and access methods.4.1 is currently only available via API, not inside ChatGPT. It offers the highest context window (1 million tokens) and better instruction following.O3 is OpenAI’s new flagship reasoning model, priced higher than 4.1 but offers deep, agentic planning and sophisticated outputs.The model naming remains confusing. OpenAI admits their naming system is messy, especially with overlapping versions like 4.0, 4.1, and 4.5.4.1 models are broken into tiers: 4.1 (flagship), Mini (mid-tier), and Nano (lightweight and cheapest).Mini and Nano are optimized for specific cost-performance tradeoffs and are ideal for automation or retrieval tasks where speed matters.Claude 3.7 Sonnet and Gemini 2.5 Pro were referenced as benchmarks for comparison, especially for long-context tasks and coding accuracy.Beth emphasized prompt hygiene and using the model-specific guides that OpenAI publishes to get better results.Jyunmi walked through how each model is designed to replace or improve upon prior versions like 3.5, 4.0, and 4.5.Karl highlighted client projects using O3 and 4.1 via API for proposal generation, data extraction, and advanced analysis.The team debated whether Pro access at $200 per month is necessary now that O3 is available in the $20 plan. Many prefer API pay-as-you-go access for cost control.Brian showcased a personal agent built with O3 that created a complete go-to-market course, complete with a dynamic dashboard and interactive progress tracking.The group agreed that in the future, personal agents built on reasoning models like O3 will dynamically generate learning experiences tailored to individual needs.Timestamps & Topics00:01:00 🧠 Intro to the wave of OpenAI model releases00:02:16 📊 OpenAI’s model comparison page and context windows00:04:07 💰 Price comparison between 4.1, O3, and O4-Mini00:05:32 🤖 Testing models through Playground and API00:07:24 🧩 Jyunmi breaks down model replacements and tiers00:11:15 💸 O3 costs 5x more than 4.1, but delivers deeper planning00:12:41 🔧 4.1 Mini and Nano as cost-efficient workflow tools00:16:56 🧠 Testing strategies for model evaluation00:19:50 🧪 TypingMind and other tools for testing models side-by-side00:22:14 🧾 OpenAI prompt guide makes big difference in results00:26:03 🧠 Carl applies O3 and 4.1 in live client projects00:29:13 🛠️ API use often more efficient than Pro plan00:33:17 🧑‍🏫 Brian demos custom go-to-market course built with O300:39:48 📊 Progress dashboard and course personalization00:42:08 🔁 Persistent memory, JSON state tracking, and session testing00:46:12 💡 Using GPTs for dashboards, code, and workflow planning00:50:13 📈 Custom GPT idea: using LinkedIn posts to reverse-engineer insights00:52:38 🏗️ Real-world use cases: construction site inspections via multimodal models00:56:03 🧠 Tip: use models to first learn about other models before choosing00:57:59 🎯 Final thoughts: ask harder questions, break your own habits01:00:04 🔧 Call for more demo-focused “Be About It” shows coming soon01:01:29 📅 Wrap-up: Biweekly recap tomorrow, conundrum on Saturday, newsletter SundayThe Daily AI Show Co-Hosts: Jyunmi Hatcher, Andy Halliday, Beth Lyons, Brian Maucere, and Karl Yeh

It’s Wednesday, and that means it’s Newsday. The Daily AI Show covers AI headlines from around the world, including Google's dolphin communication project, a game-changing Canva keynote, OpenAI’s new social network plans, and Anthropic’s Claude now connecting with Google Workspace. They also dig into the rapid rise of 4.1, open-source robots, and the growing tension between the US and China over chip development.Key Points DiscussedGoogle is training models to interpret dolphin communication using audio, video, and behavioral data, powered by a fine-tuned Gemma model called Dolphin Gemma.Beth compares dolphin clicks and buzzes to early signs of AI-enabled animal translation, sparking debate over whether we really want to know what animals think.Canva's new “Create Uncharted” keynote received praise for its fun, creator-first style and for launching 45+ feature updates in just three minutes.Canva now includes built-in code tools, generative image support via Leonardo, and expanded AI-powered design workspaces.ChatGPT added a new image library feature, making it easier to store and reuse generated images. Brian showed off graffiti art and paint-by-number tools created from a real photo.OpenAI’s GPT-4.1 shows major improvements in instruction following, multitasking, and prompt handling, especially in long-context analysis of LinkedIn content.The team compares 4.0 vs. 4.1 performance and finds the new model dramatically better for summarization, tone detection, and theme evolution.Claude now integrates with Google Workspace, allowing paid users to search and analyze their Gmail, Docs, Sheets, and calendar data.The group predicts we’ll soon have agents that work across email, sales tools, meeting notes, and documents for powerful insights and automation.Hugging Face acquired a humanoid robotics startup called Paulin and plans to release its Reachy 2 robot, potentially as open source.Japan’s Hokkaido University launched an open-source, 3D-printable robot for material synthesis, allowing more people to run scientific experiments at low cost.Nvidia faces a $5.5 billion loss due to U.S. export restrictions on H20 chips. Meanwhile, Huawei has announced a competing chip, highlighting China’s growing independence.Andy warns that these restrictions may accelerate China’s innovation while undermining U.S. research institutions.OpenAI admitted it may release more powerful models if competitors push the envelope first, sparking a debate about safety vs. market pressure.The show closes with a preview of Thursday’s episode focused on upcoming models like GPT-4.1, Mini, Nano, O3, and O4, and what they might unlock.Timestamps & Topics00:00:18 🐬 Google trains AI to decode dolphin communication00:04:14 🧠 Emotional nuance in dolphin vocalizations00:07:24 ⚙️ Gemma-based models and model merging00:08:49 🎨 Canva keynote praised for creativity and product velocity00:13:51 💻 New Canva tools for coders and creators00:16:14 📈 ChatGPT tops app downloads, beats Instagram and TikTok00:17:42 🌐 OpenAI rumored to be building a social platform00:20:06 🧪 Open-source 3D-printed robot for material science00:25:57 🖼️ ChatGPT image library and color-by-number demo00:26:55 🧠 Prompt adherence in 4.1 vs. 4.000:30:11 📊 Deep analysis and theme tracking with GPT-4.100:33:30 🔄 Testing OpenAI Mini, Nano, Gemini 2.500:39:11 🧠 Claude connects to Google Workspace00:46:40 🗓️ Examples for personal and business use cases00:50:00 ⚔️ Claude vs. Gemini in business productivity00:53:56 📹 Google’s new VO2 model in Gemini Advanced00:55:20 🤖 Hugging Face buys humanoid robotics startup Paulin00:56:41 🔮 Wrap-up and Thursday preview: new model capabilitiesThe Daily AI Show Co-Hosts: Andy Halliday, Beth Lyons, Brian Maucere, Eran Malloch, Jyunmi Hatcher, and Karl Yeh

Want to keep the conversation going?Join our Slack community at dailyaishowcommunity.comH&M has started using AI-generated models in ad campaigns, sparking questions about the future of fashion, creative jobs, and the role of authenticity in brand storytelling. Plus, a special voice note from professional photographer Angela Murray adds firsthand perspective from inside the industry.Key Points DiscussedH&M is using AI-generated digital twins of real models, who maintain ownership of their likeness and can use it with other brands.While models benefit from licensing their likeness, the move cuts out photographers, stylists, makeup artists, lighting techs, and creative teams.Guest Angela Murray, a former model and current photographer, raised concerns about jobs, ethics, and the loss of artistic soul in AI-produced fashion.Panelists debated whether this is empowering for some creators or just another cost-cutting move that favors large corporations.The group acknowledged that fast fashion already relies on manipulated images, and AI may simply continue an existing trend of unattainable ideals.Teen Vogue's article on H&M’s rollout notes only 0.03% of models featured in recent ads were plus-size, raising concerns AI may reinforce beauty stereotypes.Karl predicted authenticity will rise in value as AI floods the market. Human creators with genuine stories will stand out.Beth and Andy noted fashion has always sold fantasy. Runways and ad shoots show idealized, often unwearable designs meant to shape downstream trends.AI may democratize fashion by allowing consumers to virtually try on clothes or see themselves in outfits, but could also manipulate self-image further.Influencers, once seen as the future of advertising, may be next in line for AI disruption if digital versions prove more efficient.The real challenge isn’t the technology, it’s the pace of adoption and the lack of reskilling support for displaced creatives and workers.Ultimately, the group stressed this isn’t about just one job category. The fashion shift reflects a much bigger transition across content, commerce, and creativity.Hashtags#AIModels #HNMAI #DigitalTwins #FashionTech #AIEthics #CreativeJobs #AngelaMurray #AIFashion #AIAdvertising #DailyAIShow #InfluencerEconomyTimestamps & Topics00:00:00 👗 H&M launches AI models in ad campaigns00:03:33 🧍 Real model vs digital twin example00:05:10 🎥 Photography and creative jobs at risk00:08:48 💼 What happens to everyone behind the lens?00:11:29 🤖 Can AI accurately show how clothes fit?00:12:20 📌 H&M says images will be watermarked as AI00:13:30 🧵 Teen Vogue: is fashion losing its soul?00:15:01 📉 Diversity concerns: 0.03% of models were plus-size00:16:26 💄 The long history of image manipulation in fashion00:17:18 🪞 Will AI let us see fashion on our real bodies?00:19:00 🌀 Runway fashion vs real-world wearability00:20:40 👠 Andy’s shoe store analogy: high fashion as a lure00:26:05 🌟 Karl: AI overload may make real people more valuable00:28:00 📊 Future studies: what sells more, real or AI likeness?00:33:10 🧥 Brian spotlights TikTok fashion creator Ken00:36:14 🎙️ Guest voice note from photographer Angela Murray00:38:57 📋 Angela’s follow-up: ethics, access, and false ads00:42:03 🚨 AI's pace is too fast for meaningful regulation00:43:30 🧠 Emotional appeal and buying based on identity00:45:33 📉 Will influencers be the next to be replaced?00:46:45 📱 Why raw, casual content may outperform avatars00:48:31 📉 Broader economy may reduce consumer demand00:50:08 🧠 AI is displacing both retail and knowledge work00:51:38 🧲 AI’s goal is behavioral influence, not inspiration00:54:16 🗣️ Join the community at dailyaishowcommunity.comThe Daily AI Show Co-Hosts: Andy Halliday, Beth Lyons, Brian Maucere, Eran Malloch, Jyunmi Hatcher, and Karl Yeh

Want to keep the conversation going?Join our Slack community at dailyaishowcommunity.comBill Gates made headlines after claiming AI could outperform your doctor or your child’s teacher within a decade. The Daily AI Show explores the realism behind that timeline. The team debates whether this shift is technical, cultural, or economic, and how fast people will accept AI in high-trust roles like healthcare and education.Key Points DiscussedGates said great medical advice and tutoring will become free and commonplace, but this change will also be disruptive.The panel agreed the tech may exist in 10 years, but cultural and regulatory adoption will lag behind.Trust remains a barrier. AI can outperform in diagnosis and planning, but human connection in healthcare and education still matters to many.AI is already helping patients self-educate. ChatGPT was used to generate better questions before doctor visits, improving conversations and outcomes.Remote surgeries, da Vinci robot arms, and embodied AI were discussed as possible paths forward.Concerns were raised about skill transfer. As AI takes over simple procedures, will human surgeons get enough experience to stay sharp?AI may accelerate healthcare equity by improving access, especially in underserved or rural areas.Regulatory delays, healthcare bureaucracy, and slow adoption will likely drag out mass replacement of human professionals.Karl highlighted Canada’s universal healthcare as a potential testing ground for AI, where cost pressures and wait times could drive faster AI adoption.Long-term, AI might shift doctors and teachers into more human-centric roles while automating diagnostics, personalization, and logistics.AI-powered kiosks, wearable sensors, and personal AI agents could reshape how we experience clinics and learning environments.The biggest friction will likely come from public perception and emotional attachment to human care and guidance.Everyone agreed that AI’s role in medicine and education is inevitable. What remains unclear is how fast, how deeply, and who gets there first.#BillGates #AIHealthcare #AIEducation #FutureOfWork #AItrust #EmbodiedAI #RobotDoctors #AIEquity #daVinciRobot #Gemini25 #LLMmedicine #DailyAIShowTimestamps & Topics00:00:00 📺 Gates claims AI will outperform doctors and teachers00:02:18 🎙️ Clip from Jimmy Fallon with Gates explaining his position00:04:52 🧠 The 10-year timeline and why it matters00:06:12 🔁 Hybrid approach likely by 203500:07:35 📚 AI in education and healthcare tools today00:10:01 🤖 Trust in robot-assisted surgery and diagnostics00:11:05 ⚠️ Risk of training gaps if AI does the easy work00:14:08 🩺 Diagnosis vs human empathy in treatment00:16:00 🧾 AI explains medical reports better than some doctors00:20:46 🧠 Surgeons will need to embrace AI or fall behind00:22:03 🌍 AI could reduce travel for care and boost equity00:23:04 🇨🇦 Canada's system could accelerate AI adoption00:25:50 💬 Can AI ever replace experience-based excellence?00:28:11 🐢 The real constraint is slow human adoption00:30:31 📊 Robot vs human stats may drive patient choice00:32:14 💸 Insurers will push for cheaper, scalable AI options00:34:36 🩻 Automated intake via sensors and AI triage00:36:29 🧑‍⚕️ AI could adapt care delivery to individual preferences00:39:28 🧵 AI touches every part of the medical system00:41:17 🔧 AI won’t fix healthcare’s core structural problems00:45:14 🔍 Are we just blinded by how hard human learning is?00:49:02 🚨 AI wins when expert humans are no longer an option00:50:48 📚 Teachers will become guides, not content holders00:51:22 🏢 CEOs and traditional power dynamics face AI disruption00:53:48 ❤️ Emotional trust and the role of relationship in care00:55:57 🧵 Upcoming episodes: AI in fashion, OpenAI news, and moreThe Daily AI Show Co-Hosts: Andy Halliday, Beth Lyons, Brian Maucere, Eran Malloch, Jyunmi Hatcher, and Karl Yeh

In a future not far off, artificial intelligence has quietly collected the most intimate data from billions of people. It has observed how your body responds to conflict, how your voice changes when you're hurt, which words you return to when you're hopeful or afraid. It has done the same for everyone else. With enough data, it claims, love is no longer a mystery. It is a pattern, waiting to be matched.One day, the AI offers you a name. A face. A person. The system predicts that this match is your highest probability for a long, fulfilling relationship. Couples who accept these matches experience fewer divorces, less conflict, and greater overall well-being. The AI is not always right, but it is more right than any other method humans have ever used to find love.But here is the twist. Your match may come from a different country, speak a language you don’t know, or hold beliefs that conflict with your own. They might not match the gender or personality type you thought you were drawn to. Your friends may not understand. Your family may not approve. You might not either, at first. And yet, the data says this is the person who will love you best, and whom you will most likely grow to love in return.If you accept the match, you are trusting that the deepest truth about who you are can be known by a system that sees what you cannot. But if you reject it, you do so knowing you may never experience love that comes this close to certainty.The conundrum:If AI offers you the person most likely to love and understand you for the rest of your life, but that match challenges your sense of identity, your beliefs, or your community, do you follow it anyway and risk everything familiar in exchange for deep connection? Or do you walk away, holding on to the version of love you always believed in, even if it means never finding it?This podcast is created by AI. We used ChatGPT, Perplexity and Google NotebookLM's audio overview to create the conversation you are hearing. We do not make any claims to the validity of the information provided and see this as an experiment around deep discussions fully generated by AI.

Want to keep the conversation going?Join our Slack community at dailyaishowcommunity.comWith the release of Gemini 2.5, expanded integration across Google Workspace, new agent tools, and support for open protocols like MCP, Google is making a serious case as an AI superpower. The show breaks down what’s real, what still feels clunky, and where Google might actually pull ahead.Key Points DiscussedGemini 2.5 shows improved writing, code generation, and multimodal capabilities, but responses still sometimes end early or hallucinate limits.AAI Studio offers a smoother, more integrated experience than regular Gemini Advanced. All chats save directly to Google Drive, making organization easier.Google’s AI now interprets YouTube videos with timestamps and extracts contextual insights when paired with transcripts.Google Labs tools like Career Dreamer, YouTube Conversational AI, VideoFX, and Illuminate show practical use cases from education to slide decks to summarizing videos.The team showcased how Gemini models handle creative image generation using temperature settings to control fidelity and style.Google Workspace now embeds Gemini directly across tools, with a stronger push into Docs, Sheets, and Slides.Google Cloud’s Vertex AI now supports a growing list of generative models including Veo, Chirp (voice), and Lyra (music).Project Mariner, Google’s operator-style browsing agent, adds automated web interaction features using Gemini.Google DeepMind, YouTube, Fitbit, Nest, Waymo, and others create a wide base for Gemini to embed across industries.Google now officially supports Model Context Protocol (MCP), allowing standardized interaction between agents and tools.The Agent SDK, Agent-to-Agent (A2A) protocol, and Workspace Flows give developers the power to build, deploy, and orchestrate intelligent AI agents.#GoogleAI #Gemini25 #MCP #A2A #WorkspaceAI #AAIStudio #VideoFX #AIsearch #VertexAI #GoogleNext #AgentSDK #FirebaseStudio #Waymo #GoogleDeepMindTimestamps & Topics00:00:00 🚀 Intro: Is Google becoming an AI superpower?00:01:41 💬 New Slack community announcement00:03:51 🌐 Gemini 2.5 first impressions00:05:17 📁 AAI Studio integrates with Google Drive00:07:46 🎥 YouTube video analysis with timestamps00:10:13 🧠 LLMs stop short without warning00:13:31 🧪 Model settings and temperature experiments00:16:09 🧊 Controlling image consistency in generation00:18:07 🐻 A surprise polar bear and meta image failures00:19:27 🛠️ Google Labs overview and experiment walkthroughs00:20:50 🎓 Career Dreamer as a career discovery tool00:23:16 🖼️ Slide deck generator with voice and video00:24:43 🧭 Illuminate for short AI video summaries00:26:04 🔧 Project Mariner brings browser agents to Chrome00:30:00 🗂️ Silent drops and Google’s update culture00:31:39 🧩 Workspace integration, Lyra, Veo, Chirp, and Vertex AI00:34:17 🛡️ Unified security and AI-enhanced networking00:36:45 🤖 Agent SDK, A2A, and MCP officially backed by Google00:40:50 🔄 Firebase Studio and cross-system automation00:42:59 🔄 Workspace Flows for document orchestration00:45:06 📉 API pricing tests with OpenRouter00:46:37 🧪 N8N MCP nodes in preview00:48:12 💰 Google's flexible API cost structures00:49:41 🧠 Context window skepticism and RAG debates00:51:04 🎬 VideoFX demo with newsletter examples00:53:54 🚘 Waymo, DeepMind, YouTube, Nest, and Google’s reach00:55:43 ⚠️ Weak interconnectivity across Google teams00:58:03 📊 Sheets, Colab, and on-demand data analysts01:00:04 😤 Microsoft Copilot vs Google Gemini frustrations01:01:29 🎓 Upcoming SciFi AI Show and community wrap-upThe Daily AI Show Co-Hosts: Andy Halliday, Beth Lyons, Brian Maucere, Eran Malloch, Jyunmi Hatcher, and Karl Yeh

Want to keep the conversation going?Join our Slack community at dailyaishowcommunity.comThe Daily AI Show team covers this week’s biggest AI stories, from OpenAI’s hardware push and Shopify’s AI-first hiring policy to breakthroughs in soft robotics and Google's latest updates. They also spotlight new tools like Higgsfield for AI video and growing traction for model context protocol (MCP) as the next API evolution.Key Points DiscussedOpenAI is reportedly investing $500 million into a hardware partnership with Jony Ive, signaling a push toward AI-native devices.Shopify’s CEO told staff to prove AI can’t do the job before requesting new hires. It sparked debate about AI-driven efficiency vs. job creation.The panel explored the limits of automation in trade jobs like plumbing and roadwork, and whether AI plus robotics will close that gap over time.11Labs and Supabase launched official Model Context Protocol (MCP) servers, making it easier for tools like Claude to interact via natural language.Google announced Ironwood, its 7th-gen TPU optimized for inference, and Gemini 2.5, which adds controllable output and dynamic behavior.Reddit will start integrating Gemini into its platform and feeding data back to Google for training purposes.Intel and TSMC announced a joint venture, with TSMC taking a 20% stake in Intel’s chipmaking facilities to expand U.S.-based semiconductor production.OpenAI quietly launched Academy, offering live and on-demand AI education for developers, nonprofits, and educators.Higgsfield, a new video generation tool, impressed the panel with fluid motion, accurate physics, and natural character behavior.Meta’s Llama 4 faced scrutiny over benchmarks and internal drama, but Llama 3 continues to power open models from DeepSeek, NVIDIA, and others.Google’s AI search mode now handles complex queries and follows conversational context. The team debated how ads and SEO will evolve as AI-generated answers push organic results further down.A Penn State team developed a soft robot that can scale down for internal medicine delivery or scale up for rescue missions in disaster zones.Hashtags#AInews #OpenAI #ShopifyAI #ModelContextProtocol #Gemini25 #GoogleAI #AIsearch #Llama4 #Intel #TSMC #Higgsfield #11Labs #SoftRobots #AIvideo #ClaudeTimestamps & Topics00:00:00 🗞️ OpenAI eyes $500M hardware investment with Jony Ive00:04:14 👔 Shopify CEO pushes AI-first hiring00:13:42 🔧 Debating automation and the future of trade jobs00:20:23 📞 11Labs launches MCP integration for voice agents00:24:13 🗄️ Supabase adds MCP server for database access00:26:31 🧠 Intel and TSMC partner on chip production00:30:04 🧮 Google announces Ironwood TPU and Gemini 2.500:33:09 📱 Gemini 2.5 gets research mode and Reddit integration00:36:14 🎥 Higgsfield shows off impressive AI video realism00:38:41 📉 Meta’s Llama 4 faces internal challenges, Llama 3 powers open tools00:44:38 📊 Google’s AI Search and the future of organic results00:54:15 🎓 OpenAI launches Academy for live and recorded AI education00:55:31 🧪 Penn State builds scalable soft robot for rescue and medicineThe Daily AI Show Co-Hosts: Andy Halliday, Beth Lyons, Brian Maucere, Eran Malloch, Jyunmi Hatcher, and Karl Yeh

The Daily AI Show team covers this week’s biggest AI stories, from OpenAI’s hardware push and Shopify’s AI-first hiring policy to breakthroughs in soft robotics and Google's latest updates. They also spotlight new tools like Higgsfield for AI video and growing traction for model context protocol (MCP) as the next API evolution.Key Points DiscussedOpenAI is reportedly investing $500 million into a hardware partnership with Jony Ive, signaling a push toward AI-native devices.Shopify’s CEO told staff to prove AI can’t do the job before requesting new hires. It sparked debate about AI-driven efficiency vs. job creation.The panel explored the limits of automation in trade jobs like plumbing and roadwork, and whether AI plus robotics will close that gap over time.11Labs and Supabase launched official Model Context Protocol (MCP) servers, making it easier for tools like Claude to interact via natural language.Google announced Ironwood, its 7th-gen TPU optimized for inference, and Gemini 2.5, which adds controllable output and dynamic behavior.Reddit will start integrating Gemini into its platform and feeding data back to Google for training purposes.Intel and TSMC announced a joint venture, with TSMC taking a 20% stake in Intel’s chipmaking facilities to expand U.S.-based semiconductor production.OpenAI quietly launched Academy, offering live and on-demand AI education for developers, nonprofits, and educators.Higgsfield, a new video generation tool, impressed the panel with fluid motion, accurate physics, and natural character behavior.Meta’s Llama 4 faced scrutiny over benchmarks and internal drama, but Llama 3 continues to power open models from DeepSeek, NVIDIA, and others.Google’s AI search mode now handles complex queries and follows conversational context. The team debated how ads and SEO will evolve as AI-generated answers push organic results further down.A Penn State team developed a soft robot that can scale down for internal medicine delivery or scale up for rescue missions in disaster zones.#AInews #OpenAI #ShopifyAI #ModelContextProtocol #Gemini25 #GoogleAI #AIsearch #Llama4 #Intel #TSMC #Higgsfield #11Labs #SoftRobots #AIvideo #ClaudeTimestamps & Topics00:00:00 🗞️ OpenAI eyes $500M hardware investment with Jony Ive00:04:14 👔 Shopify CEO pushes AI-first hiring00:13:42 🔧 Debating automation and the future of trade jobs00:20:23 📞 11Labs launches MCP integration for voice agents00:24:13 🗄️ Supabase adds MCP server for database access00:26:31 🧠 Intel and TSMC partner on chip production00:30:04 🧮 Google announces Ironwood TPU and Gemini 2.500:33:09 📱 Gemini 2.5 gets research mode and Reddit integration00:36:14 🎥 Higgsfield shows off impressive AI video realism00:38:41 📉 Meta’s Llama 4 faces internal challenges, Llama 3 powers open tools00:44:38 📊 Google’s AI Search and the future of organic results00:54:15 🎓 OpenAI launches Academy for live and recorded AI education00:55:31 🧪 Penn State builds scalable soft robot for rescue and medicineThe Daily AI Show Co-Hosts: Andy Halliday, Beth Lyons, Brian Maucere, Eran Malloch, Jyunmi Hatcher, and Karl Yeh

The team breaks down Anthropic’s new research paper, Tracing the Thoughts of a Language Model, which offers rare insight into how large language models process information. Using a replacement model and attribution graphs, Anthropic tries to understand how Claude actually “thinks.” The show unpacks key findings, philosophical questions, and the implications for future AI design.Key Points DiscussedAnthropic studied its smallest model, Haiku, using a tool called a replacement model to understand internal decision-making paths.Attribution graphs show how specific features activate as the model forms an answer, with many features pulling from multilingual patterns.The research shows Claude plans ahead more than expected. In poetry generation, it preselects rhyming words and builds toward them, rather than solving it at the end.The paper challenges assumptions about LLMs being purely token-to-token predictors. Instead, they show signs of planning, contextual reasoning, and even a form of strategy.Language-agnostic pathways were a surprise: Claude used words from various languages (including Chinese and Japanese) to form responses to English queries.This multilingual feature behavior raised questions about how human brains might also use internal translation or conceptual bridges unconsciously.The team likens the research to the invention of a microscope for AI cognition, revealing previously invisible structures in model thinking.They discussed how growing an AI might be more like cultivating a tree or garden than programming a machine. Inputs, pruning, and training shapes each model uniquely.Beth and Jyunmi highlighted the gap between proprietary research and open sharing, emphasizing the need for more transparent AI science.The show closed by comparing this level of research to studying human cognition, and how AI could be used to better understand our own thinking.Hashtags#Anthropic #Claude3Haiku #AIresearch #AttributionGraphs #MultilingualAI #LLMthinking #LLMinterpretability #AIplanning #AIphilosophy #BlackBoxAITimestamps & Topics00:00:00 🧠 Intro to Anthropic’s paper on model thinking00:03:12 📊 Overview of attribution graphs and methodology00:06:06 🌐 Multilingual pathways in Claude’s thought process00:08:31 🧠 What is Claude “thinking” when answering?00:12:30 🔁 Comparing Claude’s process to human cognition00:18:11 🌍 Language as a flexible layer, not a barrier00:25:45 📝 How Claude writes poetry by planning rhymes00:28:23 🔬 Microscopic insights from AI interpretability00:29:59 🤔 Emergent behaviors in intelligence models00:33:22 🔒 Calls for more research transparency and sharing00:35:35 🎶 Set-up and payoff in AI-generated rhyming00:39:29 🌱 Growing vs programming AI as a development model00:44:26 🍎 Analogies from agriculture and bonsai pruning00:45:52 🌀 Cyclical learning between humans and AI00:47:08 🎯 Constitutional AI and baked-in intention00:53:10 📚 Recap of the paper’s key discoveries00:55:07 🗣️ AI recognizing rhyme and sound without hearing00:56:17 🔗 Invitation to join the DAS community Slack00:57:26 📅 Preview of the week’s upcoming episodesThe Daily AI Show Co-Hosts: Andy Halliday, Beth Lyons, Brian Maucere, Eran Malloch, Jyunmi Hatcher, and Karl Yeh