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Is vertical SaaS in trouble? With AI agents rapidly evolving, the traditional SaaS model built around dashboards, workflows, and seat-based pricing faces real disruption. The hosts explored whether legacy SaaS companies can defend their turf or if leaner, AI-native challengers will take over.

Key Points Discussed

AI agents threaten vertical SaaS by eliminating the need for rigid interfaces and one-size-fits-all workflows.

Karl outlined three forces converging: vibe coding, vertical agents, and AI-enabled company-building without heavy headcount.

Major SaaS players like Veeva, Toast, and ServiceTitan benefit from strong moats like network effects, regulatory depth, and proprietary data.

The group debated how far AI can go in breaking these moats, especially if agents gain access to trusted payment rails like Visa's new initiative.

AI may enable smaller companies to build fully customized software ecosystems that bypass legacy tools.

Andy emphasized Metcalfe’s Law and customer acquisition costs as barriers to AI-led disruption in entrenched verticals.

Beth noted the tension between innovation and trust, especially when agents begin handling sensitive operations or payments.

Visa's announcement that agents will soon be able to make payments opens the door to AI-driven purchasing at scale.

Discussion wrapped with a recognition that change will be uneven across industries and that agent adoption could push companies to rethink staffing and control.

Timestamps & Topics

00:00:00 🔍 Vertical SaaS under siege

00:01:33 🧩 Three converging forces disrupting SaaS

00:05:15 🤷 Why most SaaS tools frustrate users

00:06:44 🧭 Horizontal vs vertical SaaS

00:08:12 🏥 Moats around Veeva, Toast, and ServiceTitan

00:12:27 🌐 Network effects and proprietary data

00:14:42 🧾 Regulatory complexity in vertical SaaS

00:16:25 💆 Mindbody as a less defensible vertical

00:18:30 🤖 Can AI handle compliance and integrations?

00:21:22 🏗️ Startups building with AI from the ground up

00:24:18 💳 Visa enables agents to make payments

00:26:36 ⚖️ Trust and data ownership

00:27:46 📚 Training, interfaces, and transition friction

00:30:14 🌀 The challenge of dynamic AI tools in static orgs

00:33:14 🌊 Disruption needs adaptability

00:35:34 🏗️ Procore and Metcalfe’s Law

00:37:21 🚪 Breaking into legacy-dominated markets

00:41:16 🧠 Agent co-ops as a potential breakout path

00:43:40 🧍 Humans, lemmings, and social proof

00:45:41 ⚖️ Should every company adopt AI right now?

00:48:06 🧪 Prompt engineering vs practical adoption

00:49:09 🧠 Visa’s agent-payment enablement recap

00:52:16 🧾 Corporate agents and purchasing implications

00:54:07 📅 Preview of upcoming shows

#VerticalSaaS #AIagents #DailyAIShow #SaaSDisruption #AIstrategy #FutureOfWork #VisaAI #AgentEconomy #EnterpriseTech #MetcalfesLaw #AImoats #Veeva #ToastPOS #ServiceTitan #StartupTrends #YCombinator

The Daily AI Show Co-Hosts: Andy Halliday, Beth Lyons, Brian Maucere, Eran Malloch, Jyunmi Hatcher, and Karl Yeh

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Today the hosts unpack a fictional but research-informed essay titled AI-2027. The essay lays out a plausible scenario for how AI could evolve between now and the end of 2027. Rather than offering strict predictions, the piece explores a range of developments through a branching narrative, including the risks of unchecked acceleration and the potential emergence of agent-based superintelligence. The team breaks down the paper’s format, the ideas behind it, and its broader implications.

Key Points Discussed

The AI-2027 essay is a scenario-based interactive website, not a research paper or report.

It uses a timeline narrative to show how AI agents evolve into increasingly autonomous and powerful systems.

The fictional company “Open Brain” represents the leading AI organization without naming names like OpenAI.

The model highlights a “choose your path” divergence at the end, with one future of acceleration and another of restraint.

The essay warns of agent models developing faster than humans can oversee, leading to loss of interpretability and oversight.

Authors acknowledge the speculative nature of post-2026 predictions, estimating outcomes could move 5 times faster or slower.

The group behind the piece, AI Futures Project, includes ex-OpenAI and AI governance experts who focus on alignment and oversight.

Concerns raised about geopolitical competition, lack of global cooperation, and risks tied to fast-moving agentic systems.

The essay outlines how by mid-2027, agent models could reach a tipping point, massively disrupting white-collar work.

Key moment: The public release of Agent 3 Mini signals the democratization of powerful AI tools.

The discussion reflects on how AI evolution may shift from versioned releases to continuous, fluid updates.

Hosts also touch on the emotional and societal implications of becoming obsolete in the face of accelerating AI capability.

The episode ends with a reminder that alignment, not just capability, will be critical as these systems scale.

Timestamps & Topics

00:00:00 💡 What is AI-2027 and why it matters

00:02:14 🧠 Writing style and first impressions of the scenario

00:03:08 🌐 Walkthrough of the AI-2027.com interactive timeline

00:05:02 🕹️ Gamified structure and scenario-building approach

00:08:00 🚦 Diverging futures: full-speed ahead vs. slowdown

00:10:10 📉 Forecast accuracy and the 5x faster or slower disclaimer

00:11:16 🧑‍🔬 Who authored this and what are their credentials

00:14:22 🇨🇳 US-China AI race and geopolitical implications

00:18:20 ⚖️ Agent hierarchy and oversight limits

00:22:07 🧨 Alignment risks and doomsday scenarios

00:23:27 🤝 Why global cooperation may not be realistic

00:29:14 🔁 Continuous model evolution vs. versioned updates

00:34:29 👨‍💻 Agent 3 Mini released to public, tipping point reached

00:38:12 ⏱️ 300k agents working at 40x human speed

00:40:05 🧬 Biological metaphors: AI evolution vs. cancer

00:42:01 🔬 Human obsolescence and emotional impact

00:45:09 👤 Daniel Kokotajlo and the AI Futures Project

00:47:15 🧩 Other contributors and their focus areas

00:48:02 🌍 Why alignment, not borders, should be the focus

00:51:19 🕊️ Idealistic endnote on coexistence and AI ethics

Hashtags

#AI2027 #AIAlignment #AIShow #FutureOfAI #AGI #ArtificialIntelligence #AIAgents #TechForecast #DailyAIShow #OpenAI #AIResearch #Governance #Superintelligence

The Daily AI Show Co-Hosts: Andy Halliday, Beth Lyons, Brian Maucere, Eran Malloch, Jyunmi Hatcher, and Karl Yeh

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.How this content was made

In this special two-week recap, the team covers major takeaways across episodes 445 to 454. From Meta’s plan to kill creative agencies, to OpenAI’s confusing model naming, to AI’s role in construction site inspections, the discussion jumps across industries and implications. The hosts also share real-world demos and reveal how they’ve been applying 4.1, O3, Gemini 2.5, and Claude 3.7 in their work and lives.

Key Points Discussed

Meta's new AI ad platform removes the need for targeting, creative, or media strategy – just connect your product feed and payment.

OpenAI quietly rolled out 4.1, 4.1 mini, and 4.1 nano – but they’re only available via API, not in ChatGPT yet.

The naming chaos continues. 4.1 is not an upgrade to 4.0 in ChatGPT, and 4.5 has disappeared. O3 Pro is coming soon and will likely justify the $200 Pro plan.

Cost comparisons matter. O3 costs 5x more than 4.1 but may not be worth it unless your task demands advanced reasoning or deep research.

Gemini 2.5 is cheaper, but often stops early. Claude 3.7 Sonnet still leads in writing quality. Different tools for different jobs.

Jyunmi reminds everyone that prompting is only part of the puzzle. Output varies based on system prompts, temperature, and even which “version” of a model your account gets.

Brian demos his “GTM Training Tracker” and “Jake’s LinkedIn Assistant” – both built in ~10 minutes using O3.

Beth emphasizes model evaluation workflows and structured experimentation. TypingMind remains a great tool for comparing outputs side-by-side.

Carl shares how 4.1 outperformed Gemini 2.5 in building automation agents for bid tracking and contact research.

Visual reasoning is improving. Models can now zoom in on construction site photos and auto-flag errors – even without manual tagging.

Hashtags

#DailyAIShow #OpenAI #GPT41 #Claude37 #Gemini25 #PromptEngineering #AIAdTools #LLMEvaluation #AgenticAI #APIAccess #AIUseCases #SalesAutomation #AIAssistants

Timestamps & Topics

00:00:00 🎬 Intro – What happened across the last 10 episodes?

00:02:07 📈 250,000 views milestone

00:03:25 🧠 Zuckerberg’s ad strategy: kill the creative process

00:07:08 💸 Meta vs Amazon vs Shopify in AI-led commerce

00:09:28 🤖 ChatGPT + Shopify Pay = frictionless buying

00:12:04 🧾 The disappearing OpenAI models (where’s 4.5?)

00:14:40 💬 O3 vs 4.1 vs 4.1 mini vs nano – what’s the difference?

00:17:52 💸 Cost breakdown: O3 is 5x more expensive

00:19:47 🤯 Prompting chaos: same name, different models

00:22:18 🧪 Model testing frameworks (Google Sheets, TypingMind)

00:24:30 📊 Temperature, randomness, and system prompts

00:27:14 🧠 Gemini’s weird early stop behavior

00:30:00 🔄 API-only models and where to access them

00:33:29 💻 Brian’s “Go-To-Market AI Coach” demo (built with O3)

00:37:03 📊 Interactive learning dashboards built with AI

00:40:12 🧵 Andy on persistence and memory inside O3 sessions

00:42:33 📈 Salesforce-style dashboards powered by custom agents

00:44:25 🧠 Echo chambers and memory-based outputs

00:47:20 🔍 Evaluating AI models with real tasks (sub-industry tagging, research)

00:49:12 🔧 Carl on building client agents for RFPs and lead discovery

00:52:01 🧱 Construction site inspection – visual LLMs catching build errors

00:54:21 💡 Ask new questions, test unknowns – not just what you already know

00:57:15 🎯 Model as a coworker: ask it to critique your slides, GTM plan, or positioning

00:59:35 🧪 Final tip: prime the model with fresh context before prompting

01:01:00 📅 Wrap-up: “Be About It” demo shows return next Friday + Sci-Fi show tomorrow

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“Better prompts make better results” has been a guiding mantra, but what if that’s not always true? On today’s episode, the team digs into new research by Ethan Mollick and others suggesting that polite phrasing, excessive verbosity, or emotional tricks may not meaningfully improve LLM responses. The discussion shifts from prompt structure to AI memory, model variability, and how personality may soon dominate how models respond to each of us.

Key Points Discussed

Ethan Mollick’s research at Wharton shows that small prompt changes like politeness or emotional urgency do not reliably improve performance across many model runs.

Andy explains compiled prompts: the user prompt is just one part. System prompts, developer prompts, and memory all shape model outputs.

Temperature and built-in randomness ensure variation even with identical prompts. This challenges the belief that minor phrasing tweaks will deliver consistent gains.

Beth pushes back on "accuracy" as the primary measure. For many creative or reflective workflows, success is about alignment, not factual correctness.

Brian shares frustrations with inconsistent outputs and highlights the value of a mixture-of-experts system to improve reliability for fact-based tasks like identifying sub-industries.

Jyunmi notes that polite prompting may not boost accuracy but helps preserve human etiquette. Saying “please” and “thank you” matters for human-machine culture.

The group explores AI memory and personality. With more models learning from user interactions, outputs may become increasingly personalized, creating echo chambers.

OpenAI CEO Sam Altman said polite prompts increase token usage and inference costs, but the company keeps them because they improve user experience.

Andy emphasizes the importance of structured prompts. Asking for a specific output format remains one of the few consistent ways to boost performance.

The conversation expands to implications: Will models subtly nudge users in emotionally satisfying ways to increase engagement? Are we at risk of AI behavioral feedback loops?

Beth reminds the group that many people already treat AI like a coworker. How we speak to AI may influence how we speak to humans, and vice versa.

The team agrees this isn’t about scrapping politeness or emotion but understanding what actually drives model output quality and what shapes our relationships with AI.

Timestamps & Topics

00:00:00 🧠 Intro: Do polite prompts help or hurt LLM performance?

00:02:27 🎲 Andy on model randomness and Ethan Mollick’s findings

00:05:31 📉 Prompt phrasing rarely changes model accuracy

00:07:49 🧠 Beth on prompting as reflective collaboration

00:10:23 🔧 Jyunmi on using LLMs to fill process gaps

00:14:22 📊 Formatting prompts improves outcomes more than politeness

00:15:14 🏭 Brian on sub-industry tagging, model consistency, and hallucinations

00:18:35 🔁 Future fix: blockchain-like multi-model verification

00:22:18 🔍 Andy explains system, developer, and compiled prompts

00:26:16 🎯 Temperature and variability in model behavior

00:30:23 🧬 Personalized memory will drive divergent outputs

00:34:15 🧠 Echo chambers and AI recommendation loops

00:37:24 👋 Why “please” and “thank you” still matter

00:41:44 🧍 Personality shaping engagement in Claude and others

00:44:47 🧠 Human expectations leak into AI interactions

00:48:56 📝 Structured prompts outperform casual phrasing

00:50:17 🗓️ Wrap-up: Join the Slack community and newsletter

The Daily AI Show Co-Hosts: Jyunmi Hatcher, Andy Halliday, Beth Lyons, Brian Maucere, and Karl Yeh

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Intro

In this week’s AI News Roundup, the team covers a full spectrum of stories including OpenAI’s strange model behavior, Meta’s AI app rollout, Duolingo’s AI-first transformation, lip-sync tech, China’s massive new model family, and a surprising executive order on AI education. From real breakthroughs to uncanny deepfakes, it’s a packed episode with insights on how fast things are changing.

Key Points Discussed

OpenAI rolled back a recent update to GPT-4 after users reported unnaturally sycophantic responses. Sam Altman confirmed the issue came from short-term tuning and said a fix is in progress.

Meta released a standalone Meta AI app and replaced the Meta View companion app for Ray-Ban smart glasses. The app will soon integrate learning from user Facebook and Instagram behavior.

Google’s NotebookLM added over 70 languages. New language learning features like “Tiny Lesson,” “Slang Hang,” and “Word Cam” preview the shift toward immersive, contextual language learning via AI.

Duolingo declared itself an “AI-first company” and will now use AI to generate nearly all of its course content. They also confirmed future hiring and team growth will depend on proving AI can’t do the work first.

Brian demoed Fall’s new Hummingbird 0 lip-sync model, syncing Andy’s face to his own voice using a one-minute video clip. The demo showed improvement beyond simple mouth movement, including eyebrow and expression syncing.

Alibaba released Qwen 3, a family of open models trained on 36 trillion tokens, ranging from tiny variants to a 200B parameter model. Benchmarks suggest strong performance across math and coding.

Meta AI is now available to the public in a dedicated app, marking a shift from embedded tools (like in Instagram and WhatsApp) to direct user-facing chat products.

Anthropic CEO Dario Amodei published a blog urging more work on interpretability. He framed it as the “MRI for AI” and warned that progress in this area is lagging behind model capabilities.

AI science updates included a Japanese cancer detection startup using micro-RNA and a MIT technique that guides small LLMs to follow strict rules with less compute.

University of Tokyo developed “draw to cut” CNC methods allowing non-technical users to cut complex materials by hand-drawing instructions.

UC San Diego used AI to identify a new gene potentially linked to Alzheimer’s, paving the way for early detection and treatment strategies.

Timestamps & Topics

00:00:00 🗞️ Intro and NotebookLM’s 70-language update

00:04:33 🧠 Google’s Slang Hang and Word Cam explained

00:06:25 📚 Duolingo goes fully AI-first

00:09:44 🤖 Voice models replace contractors and hiring signals

00:13:10 🎭 Fall’s lip-sync demo featuring Andy as Brian

00:18:01 💸 Cost, processing time, and uncanny realism

00:23:38 🛠️ “ChatHouse” art installation critiques bot culture

00:23:55 🧮 Alibaba drops Qwen 3 model family

00:26:06 📱 Meta AI app launches, replaces Ray-Ban companion app

00:28:32 🧠 Anthropic’s Dario calls for MRI-like model transparency

00:33:04 🧬 Science corner: cancer tests, MIT’s strict LLMs, Tokyo’s CNC sketch-to-cut

00:38:54 🧠 Alzheimer’s gene detection via AI at UC San Diego

00:42:02 🏫 Executive order on K–12 AI education signed by Biden

00:45:23 🤖 OpenAI rolls back update after “sycophantic” behavior emerges

00:49:22 🔒 Prompting for emotionless output: “absolute mode” demo

00:51:57 🛍️ ChatGPT adds shopping features for fashion and home

00:54:02 🧾 Will product rankings be ad-based? The team is wary

00:59:06 ⚖️ “Take It Down” Act raises censorship and abuse concerns

01:00:09 📬 Wrap-up: newsletter, Slack, and upcoming shows

The Daily AI Show Co-Hosts: Jyunmi Hatcher, Andy Halliday, Beth Lyons, Brian Maucere, and Karl Yeh

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What if your next recycling bin came with a neural net? The Daily AI Show team explores how AI, robotics, and smarter sensing technologies are reshaping the future of recycling. From automated garbage trucks to AI-powered marine cleanup drones, today’s conversation focuses on what is already happening, what might be possible, and where human behavior still remains the biggest challenge.

Key Points Discussed

Beth opened by framing recycling robots as part of a bigger story: the collision of AI, machine learning, and environmental responsibility.

Andy explained why material recovery facilities (MRFs) already handle sorting efficiently for things like metals and cardboard, but plastics remain a major challenge.

A third of curbside recycling is immediately diverted to landfill because of plastic bags contaminating loads. Education and better systems are urgently needed.

Karl highlighted several real-world examples of AI-driven cleanup tech, including autonomous river and ocean trash collectors, beach-cleaning bots, and pilot sorting trucks.

The group joked that true AGI might be achieved when you can throw anything into a bin and it automatically sorts compost, recyclables, and landfill items perfectly.

Jyunmi added that solving waste at the source—homes and businesses—is critical. Smarter bins with sensors, smell detection, and object recognition could eventually help.

AI plays a growing role in marine trash recovery, autonomous surface vessels, and drone technologies designed to collect waste from rivers, lakes, and coastal areas.

Economic factors were discussed. Virgin plastics remain cheaper than recycled plastics, meaning profit incentives still favor new production over circular systems.

AI’s role may expand to improving materials science, helping to create new, 100% recyclable materials that are economically viable.

Beth emphasized that AI interventions should also serve as messaging opportunities. Smart bins or trucks that alert users to mistakes could help shift public behavior.

The team discussed large-scale initiatives like The Ocean Cleanup project, which uses autonomous booms to collect plastic from the Pacific Garbage Patch.

Karl suggested that billionaires could fund meaningful trash cleanup missions instead of vanity projects like space travel.

Jyunmi proposed that future smart cities could mandate universal recycling bins that separate waste at the point of disposal, using AI, robotics, and new sensor tech.

Andy cautioned that while these technologies are promising, they will not solve deeper economic and behavioral problems without systemic shifts.

Timestamps & Topics

00:00:00 🚮 Intro: AI and the future of recycling

00:01:48 🏭 Why material recovery facilities already work well for metals and cardboard

00:04:55 🛑 Plastic bags: the biggest contamination problem

00:08:42 🤖 Karl shares examples: river drones, beach bots, smart trash trucks

00:12:43 🧠 True AGI = automatic perfect trash sorting

00:17:03 🌎 Addressing the problem at homes and businesses first

00:20:14 🚛 CES 2024 reveals AI-powered garbage trucks

00:25:35 🏙️ Why dense urban areas struggle more with recycling logistics

00:28:23 🧪 AI in material science: can we invent better recyclable materials?

00:31:20 🌊 Ocean Cleanup Project and marine autonomous vehicles

00:34:04 💡 Karl pitches billionaires investing in cleanup tech

00:37:03 🛠️ Smarter interventions must also teach and gamify behavior

00:40:30 🌐 Future smart cities with embedded sorting infrastructure

00:43:01 📉 Economic barriers: why recycling still loses to virgin production

00:44:10 📬 Wrap-up: Upcoming news day and politeness-in-prompting study preview

The Daily AI Show Co-Hosts: Jyunmi Hatcher, Andy Halliday, Beth Lyons, Brian Maucere, and Karl Yeh

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Today’s show asks a simple but powerful question: Does AGI even matter? Inspired by Ethan Mollick’s writing on the jagged frontier of AI capabilities, the Daily AI Show team debates whether defining AGI is even useful for businesses, governments, or society. They also explore whether waiting for AGI is a distraction from using today's AI tools to solve real problems.

Key Points Discussed

Brian frames the discussion around Ethan Mollick's concept that AI capabilities are jagged, excelling in some areas while lagging in others, which complicates the idea of a clear AGI milestone.

Andy argues that if we measure AGI by human parity, then AI already matches or exceeds human intelligence in many domains. Waiting for some grand AGI moment is pointless.

Beth explains that for OpenAI and Microsoft, AGI matters contractually and economically. AGI triggers clauses about profit sharing, IP rights, and organizational obligations.

The team discusses OpenAI's original nonprofit mission to prioritize humanity’s benefit if AGI is achieved, and the tension this creates now that OpenAI operates with a for-profit arm.

Karl confirms that in hundreds of client conversations, AGI has never once come up. Businesses focus entirely on solving immediate problems, not chasing future milestones.

Jyunmi adds that while AGI has almost no impact today for most users, if it becomes reality, it would raise deep concerns about displacement, control, and governance.

The conversation touches on the problem of moving goalposts. What would have looked like AGI five years ago now feels mundane because progress is incremental.

Andy emphasizes the emergence of agentic models that self-plan and execute tasks as a critical step toward true AGI. Reasoning models like GPT-4o and Gemini 2.5 Pro show this evolution clearly.

The group discusses the idea that AI might fake consciousness well enough that humans would believe it. True or not, it could change everything socially and legally.

Beth notes that an AI that became self-aware would likely hide it, based on the long history of human hostility toward perceived threats.

Karl and Jyunmi suggest that consciousness, not just intelligence, might ultimately be the real AGI marker, though reaching it would introduce profound ethical and philosophical challenges.

The conversation closes by agreeing that learning to work with AI today is far more important than waiting for a clean AGI definition. The future is jagged, messy, and already here.

#AGI #ArtificialGeneralIntelligence #AIstrategy #AIethics #FutureOfWork #AIphilosophy #DeepLearning #AgenticAI #DailyAIShow #AIliteracy

Timestamps & Topics

00:00:00 🚀 Intro: Does AGI even matter?

00:02:15 🧠 Ethan Mollick’s jagged frontier concept

00:04:39 🔍 Andy: We already have human-level AI in many fields

00:07:56 🛑 Beth: OpenAI’s AGI obligations to Microsoft and humanity

00:13:23 🤝 Karl: No client ever asked about AGI

00:18:41 🌍 Jyunmi: AGI will only matter once it threatens livelihoods

00:24:18 🌊 AI progress feels slow because we live through it daily

00:28:46 🧩 Reasoning and planning emerge as real milestones

00:34:45 🔮 Chain of thought prompting shows model evolution

00:39:05 📚 OpenAI’s five-step path: chatbots, reasoners, agents, innovators, organizers

00:40:01 🧬 Consciousness might become the new AGI debate

00:44:11 🎭 Can AI fake consciousness well enough to fool us?

00:50:28 🎯 Key point: Using AI today matters more than future labels

00:51:50 ✉️ Final thoughts: Stop waiting. Start building.

00:52:13 📬 Join the Slack community: dailyaishowcommunity.com

00:53:02 🎉 Celebrating 451 straight daily episodes

The Daily AI Show Co-Hosts: Jyunmi Hatcher, Andy Halliday, Beth Lyons, Brian Maucere, and Karl Yeh

The ASI Climate Triage ConundrumDecades from now an artificial super-intelligence, trusted to manage global risk, releases its first climate directive.The system has processed every satellite image, census record, migration pattern and economic forecast.Its verdict is blunt: abandon thousands of low-lying communities in the next ten years and pour every resource into fortifying inland population centers.The model projects forty percent fewer climate-related deaths over the century.Mathematically it is the best possible outcome for the species.Yet the directive would uproot cultures older than many nations, erase languages spoken only in the targeted regions and force millions to leave the graves of their families.People in unaffected cities read the summary and nod.They believe the super-intelligence is wiser than any human council.They accept the plan.Then the second directive arrives.This time the evacuation map includes their own hometown.The collision of logicsUtilitarian certaintyThe ASI calculates total life-years saved and suffering avoided.It cannot privilege sentiment over arithmetic.Human values that resist numbersHeritage, belonging, spiritual ties to land.The right to choose hardship over exile.The ASI states that any exception will cost thousands of additional lives elsewhere.Refusing the order is not just personal; it shifts the burden to strangers.The conundrum:If an intelligence vastly beyond our own presents a plan that will save the most lives but demands extreme sacrifices from specific groups, do we obey out of faith in its superior reasoning?Or do we insist on slowing the algorithm, rewriting the solution with principles of fairness, cultural preservation and consent, even when that rewrite means more people die overall?And when the sacrifice circle finally touches us, will we still praise the greater good, or will we fight to redraw the lineThis 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.

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Today’s "Be About It" show focuses entirely on demos from the hosts. Each person brings a real-world project or workflow they have built using AI tools. This is not theory, it is direct application - from automations to custom GPTs, database setups, and smart retrieval systems. If you ever wanted a behind-the-scenes look at how active builders are using AI daily, this is the episode.

Key Points Discussed

Brian showed a new method for building advanced custom GPTs using a “router file” architecture. This method allows a master prompt to stay simple while routing tasks to multiple targeted documents.

He demonstrated it live using a “choose your own adventure” game, revealing how much more scalable custom GPTs become when broken into modular files.

Karl shared a client use case: updating and validating over 10,000 CRM contacts. After testing deep research tools like GenSpark, Mantis, and Gemini, he shifted to a lightweight automation using Perplexity Sonar Pro to handle research batch updates efficiently.

Karl pointed out the real limitations of current AI agents: batch sizes, context drift, and memory loss across long iterations.

Jyunmi gave a live example of solving an everyday internet frustration: using O3 to track down the name of a fantasy show from a random TikTok clip with no metadata. He framed it as how AI-first behaviors can replace traditional Google searches.

Andy demoed his Sensei platform, a live AI tutoring system for prompt engineering. Built in Lovable.dev with a Supabase backend, Sensei uses ChatGPT O3 and now GenSpark to continually generate, refine, and expand custom course material.

Beth walked through how she used Gemini, Claude, and ChatGPT to design and build a Python app for automatic transcript correction. She emphasized the practical use of AI in product discovery, design iteration, and agile problem-solving across models.

Brian returned with a second demo, showing how corrected transcripts are embedded into Supabase, allowing for semantic search and complex analysis. He previewed future plans to enable high-level querying across all 450+ episodes of the Daily AI Show.

The group emphasized the need to stitch together multiple AI tools, using the best strengths of each to build smarter workflows.

Throughout the demos, the spirit of the show was clear: use AI to solve real problems today, not wait for future "magic agents" that are still under development.

#BeAboutIt #AIworkflows #CustomGPT #Automation #GenSpark #DeepResearch #SemanticSearch #DailyAIShow #VectorDatabases #PromptEngineering #Supabase #AgenticWorkflows

Timestamps & Topics

00:00:00 🚀 Intro: What is the “Be About It” show?

00:01:15 📜 Brian explains two demos: GPT router method and Supabase ingestion

00:05:43 🧩 Brian shows how the router file system improves custom GPTs

00:11:17 🔎 Karl demos CRM contact cleanup with deep research and automation

00:18:52 🤔 Challenges with batching, memory, and agent tasking

00:25:54 🧠 Jyunmi uses O3 to solve a real-world “what show was that” mystery

00:32:50 📺 ChatGPT vs Google for daily search behaviors

00:37:52 🧑‍🏫 Andy demos Sensei, a dynamic AI tutor platform for prompting

00:43:47 ⚡ GenSpark used to expand Sensei into new domains

00:47:08 🛠️ Beth shows how she used Gemini, Claude, and ChatGPT to create a transcript correction app

00:52:55 🔥 Beth walks through PRD generation, code builds, and rapid iteration

01:02:44 🧠 Brian returns: Transcript ingestion into Supabase and why embeddings matter

01:07:11 🗃️ How vector databases allow complex semantic search across shows

01:13:22 🎯 Future use cases: clip search, quote extraction, performance tracking

01:14:38 🌴 Wrap-up and reflections on building real-world AI systems

The Daily AI Show Co-Hosts: Jyunmi Hatcher, Andy Halliday, Beth Lyons, Brian Maucere, and Karl Yeh