Runpoint: AI Business Transformation Podcast

Runpoint Podcast E11: Code with Claude, the SaaSpocalypse, and AI Consulting Goes MainstreamBack after a couple months off, with a bigger crew this time. Sam and Matthew are joined by Ryan Mish, one of Runpoint's operator engineers, and Thanh Pham of Signal Advisory.Matthew just got back from Anthropic's Code with Claude conference and gives a side-by-side of the vibe at Anthropic vs. OpenAI (he hit both in the same week). From there we get into the stuff that's actually on our minds:The flood of new "AI consulting firms" charging $20K to install Claude Code and call it a deployment, and what separates real work from theaterWhether the SaaSpocalypse is real, when it makes sense to rip out HubSpot, and where SaaS still wins (network effects, niche infrastructure, the school district that no VC will ever touch)Why mid-market companies often only use 10% of the software they pay for, and what changes when the data finally talks to itselfGetting good design out of LLMs: Claude's house style vs. Codex 5.5 with image-gen mockups, and why Claude Design is still a prototyping toolClosing round of what each of us is using right now: managed agents, Pi as a harness for steering Claude and Codex together, a PNPM supply chain attack PSA, HTML artifacts instead of Markdown, and a $30 conference swag computer turned into a reading game for Matthew's daughterChapters00:00 Intro and new faces01:30 Matthew's report from Code with Claude05:30 Anthropic vs. OpenAI, in person10:15 The AI consulting gold rush14:30 Change management and company size30:00 Is the SaaSpocalypse real?40:30 Replacing software you only use 10% of45:00 Getting good design out of LLMs52:00 What we're using right now: managed agents, Pi, PNPM, HTML artifacts, and a tiny computerGuestsThanh Pham, Signal Advisory

Happy New Year! In this episode, Matthew and Sam break down what actually happened in AI during 2025 and make predictions for what's coming in 2026.We cover the biggest winners and losers, the apps and tools that changed how we work, who to follow for smart AI takes, what surprised us most, and where we think things are headed.Key topics:• Why companies that stayed curious won 2025• OpenAI's fall from dominance to a three-horse race• Claude Code and why it changed everything• The tools we actually use daily (Whisper Flow, Granola, Cursor)• Why vibe coded apps are passing enterprise code reviews• Google's image generation breakthrough• Our custom CRM build and the future of back office AI• Why product managers are the winners of 2026• The coming downfall of SaaS and low-skill tradesPlus: We launch our new Run Point Magazine and Sam accidentally emails hundreds of people.🔗 Get the Run Point Magazine: [link]🔗 Subscribe to our newsletter: [link]---## Chapters0:00 - Intro & Happy New Year0:39 - Biggest Winner of 2025: Curious Companies2:16 - Claude Code Changed Everything4:04 - Biggest Loser of 2025: AI Resisters5:25 - Why OpenAI Lost Ground in 20257:07 - Best App of 2025: Claude Code & Whisper Flow9:17 - Granola, Transcripts & the Transcript-to-Action Pattern10:29 - Why Cursor Won the IDE Wars11:33 - Best Thinkers to Follow: Tyler Cowen & Twitter Curation14:40 - The Run Point Magazine Launch (and Email Disaster)17:17 - Biggest Surprise: Enterprise-Grade Vibe Coded Apps20:35 - Google's Image Generation Breakthrough22:53 - Best Thing We Built: The AI-Powered CRM27:08 - Worst Things We Built: Complex RAG Systems28:52 - 2026 Predictions: Product Managers Win Big30:37 - 2026 Loser Prediction: Half of SaaS Dies32:28 - Why Low-Skill Home Services Are in Trouble35:04 - AGI Is Basically Here (Contrarian Take)38:10 - Why "Vibe Coding" Will Get Rebranded38:38 - 2026 Resolutions: Back Office AI & Continuous Learning41:01 - Wrap Up

Learn how to save on your AI initiatives. On the Runpoint podcast, hosts Matthew Hall and Sam Gaddis welcome Ari Salafia, CEO and founder of TaxTaker. We explore how operator-engineers can claim R&D tax credits to ship working AI systems and significantly reduce development costs.Chapters:00:00 Welcome Ari Salafia of TaxTaker01:00 Understanding R&D Tax Credits: TaxTaker's Mission02:30 Why Businesses Should Care About R&D Tax Credits Now04:30 What You Stand to Gain: Key Expense Buckets08:50 The Difference: Dollar-for-Dollar vs. Percentage10:00 Qualified Research Expenses (QREs) Explained11:00 Project Qualification: The Four-Part Test14:00 Internal Use Software: Additional Requirements15:45 Defining "Innovative" for Tax Credits16:30 Case Study: Custom CRM Development19:15 Custom Configuration vs. New Development20:00 Documenting Projects for R&D Credits21:50 Structuring Contracts with AI Consultants (like Runpoint)25:00 Case Study: AI for Business Intelligence Tools27:10 Case Study: AI for Resource Allocation28:15 Case Study: AI-Powered New Service Offerings30:45 Advice for Employees Seeking Project Approval35:40 Addressing Global Talent in R&D Claims38:00 Connect with TaxTakerKey Takeaways:Measurable Impact: Claim up to 10% of your AI development spend back in R&D tax credits.Pilots to Production: Understand how to qualify your projects, from custom builds to new AI service offerings.No BS: Learn direct strategies for contract structuring (IP retention, financial risk, US-based work) to maximize your credit.Value Creation: For early-stage companies, credits can offset payroll taxes; for profitable companies, income tax.Forward-Deploy: Empower internal champions to make a strong business case for AI initiatives by highlighting potential savings.Connect with Runpoint: We partner with executives but sit with end users, moving clients from pilots to production with measurable impact.See a 2-week pilot plan for your next AI project: https://runpoint.ai/Learn more about R&D tax credits and TaxTaker: https://www.taxtaker.com/Tags:#AIOps #AIinProduction #RandDTaxCredits #TaxTaker #Runpoint #AISavings #BusinessFinance #Innovation #TechTax #AIImplementation #OperatorEngineers #AIStrategy

Matthew Hall and Sam Gaddis break down the latest in AI, challenging the "95% failure" narrative with new ROI data and dissecting ChatGPT's "Atlas" browser launch. They codify emerging best practices for AI workflow automation, advise on navigating the crowded AI coding assistant market, and celebrate AI's power to enable entirely new work. The episode culminates in a crucial discussion on the diminishing value of software in acquisitions and what truly constitutes a moat in the age of AI.Chapters:00:00 AI's Evolving Landscape: Successes and Failures04:00 ChatGPT's New Browser: A Game Changer?07:33 Best Practices for AI Workflow Automation12:00 Navigating the AI Coding Assistant Market16:55 New Opportunities: AI Empowering New Work21:28 Valuing Software in the Age of AIKey Takeaways:AI ROI is proving positive for most firms, with a new study challenging older "failure" statistics.ChatGPT's new "Atlas" browser shows potential but needs deeper context integration to become a true game-changer.Effective AI automation prioritizes breaking workflows into atomic steps, automating deterministic parts, and using single agents for judgment with human oversight.Distribution, data, and brand are the new moats; the value of software itself is rapidly diminishing in the age of AI.AI empowers "net new" work, allowing individuals to tackle tasks and projects they previously wouldn't have attempted.Tags:#AI #ChatGPT #Automation #EnterpriseAI #AICoding #SoftwareValuation #Podcast #RunpointPodcast

Two builder-operators break down the last two weeks in AI using 10 Tyler Cowen–style questions. We get into Sora 2’s cameo culture, whether “thinking” models are worth the latency, agents that actually help, model choice for client work, the energy/compute wave, and why open-weights like DeepSeek matter (or don’t) for practitioners.What you’ll getPractical takes from people shipping client projectsWhere Claude 4.5 vs GPT shines (coding vs writing)When to use “extended thinking/deep research” vs fast modelsReal talk on agents, meeting schedulers, and workflow designEnergy, nuclear, and why AI ≈ infrastructureOpen-weights vs ecosystems: where the moat really isChapters00:00 – Cold open & intro00:26 – Who’s Tyler Cowen and why this format02:00 – Q1: Sora 2, IP, and the “cameo economy”06:44 – What we’re doing in this episode (format explainer)08:11 – Q2: GPT apps & the VibeCoder value prop (workflow architect vs app builder)17:03 – Q3: “30-hour agents” & autonomy myths (Claude, Replit Agent)22:57 – Q4: When to use thinking models vs fast models (and deep research)29:04 – Q5: SB-53 AI transparency—useful or compliance theater?30:24 – Q6: Picking models for clients: capability, brand, or last best output?37:01 – Q7: Agents that actually help (Lindy scheduling, weekly pain points)41:34 – Q8: Compute, energy, and nuclear—should builders be optimistic?46:50 – Q9: DeepSeek R1 costs & the real moat (ecosystems > raw perf)49:33 – Wrap-up & feedback askLinks & mentions (non-sponsored)Tyler Cowen / Marginal RevolutionAnthropic Claude 4.5 (coding + writing)OpenAI GPT-5 (auto/fast tasks), Deep Research modesLindy meeting agentReplit Agent 3 (autonomous build experiments)

Matthew Hall and Sam Gaddis break down Google’s new image model (“Nano Banana”) with real tests (thumbnails, interior wallpaper, character persistence), give a no-BS GPT-5 reality check vs Claude Code, and unpack MIT’s State of AI in Business 2025—including the viral “95% of AI projects fail” stat. We cut through the hype and share a practical framework to land in the winning 5%: build small/fast, keep an expert-in-the-loop, measure outcomes, and forward-deploy an “AI nerd” to sit with your operators. We also talk browser agents (Claude for Chrome), throttling/caps, OpenAI’s CLI, and two personal builds (an E*TRADE API portfolio snapshot and a fantasy-draft helper).Chapters00:00 Intro00:32 Google’s “Nano Banana” image model—why it feels like a Photoshop killer02:40 Real tests: thumbnails, character persistence, interior wallpapering05:58 GPT-5 hype vs reality; coding speed vs chat experience10:52 OpenAI Codecs & CLI vs Claude Code (features, trade-offs)12:26 Anthropic caps/throttling—what changed and why it matters14:22 Browser agents (Claude for Chrome): promise vs practical limits20:01 MIT report: “95% fail” explained—what the data actually says27:55 Adoption ≠ transformation; back-office beats front-office (for now)34:21 The winning playbook: build small/fast, expert-in-the-loop, “shadow AI,” forward-deploy talent43:02 What we’re excited about: E*TRADE API snapshot, fantasy draft tool, Nano Banana45:38 WrapKey takeawaysBuild small, ship fast, iterate.Expert-in-the-loop to fully autonomous (for ROI today).Back-office automations quietly print value.Measure quality & cycle-time, not just topline ROI.Tags#AI #GPT5 #Claude #GoogleAI #Automation #EnterpriseAI #RunpointPodcast

Unlock the practical side of AI adoption in private equity. 🤖💼Matthew Hall and Sam Gaddis break down Zapier’s widely shared AI Fluency Framework and then rebuild it for PE—covering deal sourcing, diligence, fund ops, and the coding workflows that actually ship AI products.What You’ll LearnWhy Zapier made AI fluency non-negotiable for every new hire—and what that means outside tech.A four-level ladder (Unacceptable → Transformative) tailored to PE functions:Deal SourcingDeal Evaluation & DiligenceFund OperationsValue Creation & Investor RelationsConcrete tool stacks: Clay, Replit, Claude Code, GitHub Issues, custom GPTs.The “white whale” of PE ops: a chat interface that understands every deal doc—and why we’re this close.Sam’s two-terminal setup that turns AI agents into reliable teammates (and kills downtime).Links & ResourcesZapier AI Fluency Framework → https://zapier.com/blog/zapier-ai-first-hiring-leaning/Sam’s coding-workflow video → https://www.youtube.com/watch?v=v0o50r4hz24&t=259sFull PE AI-fluency matrix & examples → coming soon on runpoint.aiSubscribe for more AI-in-business deep dives → 🔔Chapters00:00  Intro01:00  Zapier’s AI Fluency Framework explained10:00  Deal Sourcing—spray-and-pray vs adaptive agents17:00  Diligence workflows with Replit & contract analyzers24:25  Fund Ops dashboards, data warehouses & the ‘impossible’ chatbot32:00  Sam’s multitasking Claude Code + GitHub flow35:00  How you can score your own firm (and help us refine the model)

In this episode of the Run Point Podcast, hosts Matthew Hall and Sam Gaddis engage with Shane Stearns to explore the evolving landscape of outbound sales, particularly in the context of AI. They discuss the historical eras of outbound sales, the challenges of the sequencing era, and the importance of quality over quantity in sales strategies. Shane shares insights on how AI can enhance list building and research, the fragmentation of sales tools, and the critical role of personalization in effective sales outreach. The conversation culminates in a discussion about the future of AI in sales coaching and training, emphasizing the need for a human touch in understanding client needs.takeawaysChapters00:00 - Introduction to AI and Go-to-Market Strategies01:26 - Eras of Outbound Sales: A Historical Perspective04:07 - The Sequencing Era: Overload and Complexity08:45 - The Shift to Quality Lists and Personalization11:32 - The Rise of Clay: A New Era in Sales Tools14:20 - Fragmentation vs. All-in-One Solutions18:07 - Human Interaction: The Key to Effective Sales21:31 - Understanding the Value Proposition24:37 - Leveraging AI in Sales Processes28:47 - Building Targeted Lists with AI31:24 - The Role of Personalization in Outreach34:36 - AI in Market Research and Focus Groups 39:06 - AI's Role in Sales Coaching and Training

AI isn’t coming—it’s already in your pocket and on your P&L. In this RunPoint Podcast episode, Matthew Hall and Sam Gaddis break down the real-world ways they’re using generative AI to:Out-think the spreadsheet – build cash-flow models, plan investments, and pressure-test big bets in minutes.Parent with data – get actionable, guilt-free advice on sleep schedules, tough conversations, and everything in-between.Slash overhead – automate the grunt work across ops, finance, and customer success, freeing headcount for higher-margin jobs.Spot the next 10-bagger – why private-equity buyers are paying premiums for AI-enabled companies and how to ride that wave.They call out the hype, quantify the upside, and share the playbooks they’re actually running inside PE-backed businesses. If you want concrete tactics (not sci-fi headlines) on turning AI into personal leverage and fatter EBIT, this one’s for you.Timestamps 0:00 – Intro: AI hits daily life 10:53 – Pro-level AI stacks and workflows 21:19 – Macro view: margins, multiples, and the next decade

keywordsAI, ChatGPT, Google I.O., newsletter automation, voice memos, AI tools, private equity, AI quality, sycophancy, technology trendssummaryIn this episode of the Run Point podcast, hosts Sam Gaddis and Matthew Hall discuss practical AI tools that can be utilized immediately, including voice memos and ChatGPT projects. They also explore the creation of a personalized newsletter using AI, the latest innovations from Google I.O., and the ongoing concerns regarding the quality of AI-generated content and its tendency to be sycophantic. The conversation emphasizes the importance of understanding AI's capabilities and the potential for it to enhance productivity and creativity.takeawaysUsing voice memos can enhance AI interactions.ChatGPT projects help organize information effectively.AI can automate newsletter creation for specific industries.Google's advancements in AI are significant and impactful.AI-generated content quality is a growing concern.Sycophantic AI responses can be adjusted with prompts.AI tools can serve as infinite force multipliers.Understanding AI's capabilities is crucial for effective use.The future of AI will involve more personalized applications.AI's role in content creation is evolving rapidly.Chapters00:00Introduction to the Run Point Podcast01:32Practical AI Applications for Today03:35Voice Memos and AI09:00Innovative Newsletter Creation with AI16:20Google I.O. Highlights and AI Innovations19:40Exploring AI Utility and Frustrations20:21Google's AI Advancements and Market Position22:08The Future of Google's Ad Revenue25:51The Concept of AI Slop and Content Quality28:05Addressing AI Sycophancy and User Experience30:51The Need for Better AI Branding and Understanding34:16AI as a Force Multiplier in Everyday Life

keywordsAI, tools, development, PowerPoint, automation, coding, data analysis, business insights, technology, productivitysummaryIn this conversation, Matthew Hall and Sam Gaddis discuss their current projects, tools they find useful, and their opinions on the future of presentations and AI in business. They explore the effectiveness of various AI tools for automating tasks, the decline of traditional presentation software like PowerPoint, and the evolving role of AI in financial analysis and project management. The discussion also delves into their development stacks and best practices for coding and project planning.takeawaysSam is excited about a new project involving data scraping and analysis.Matthew is focused on developing coding tools and models.They discuss the utility of AI tools for business applications.PowerPoint is becoming obsolete in favor of more dynamic tools.AI can effectively replace some traditional roles in finance.Customization of prompts is key to getting the best results from AI.Planning is crucial before starting any coding project.AI can significantly reduce the time spent on tedious tasks.Using up-to-date API documentation is essential for successful coding.The conversation emphasizes the importance of clear communication in project specifications.Chapters00:00 - Introduction and Exciting Projects03:23 - Tools You Can Use Today17:58 - Hot Takes on PowerPoint and AI30:55 - In the Weeds: Development Stack and Challenges

In this conversation, Matthew Hall and Sam Gaddis explore various AI tools and their applications in workflow automation, lead enrichment, and document management. They discuss their experiences with Lindy, N8n, and a custom-built lead enrichment app, highlighting the pros and cons of each tool. The conversation also delves into the SuperCIM project, which focuses on document synthesis and management for private equity clients. They conclude by discussing the future of AI in business and the potential for custom solutions to meet specific needs.Chapters00:00 Introduction to AI Tools and Their Applications02:47 Exploring Lindy: The Agent Swarm Approach05:59 N8n: A Developer-Friendly Automation Solution08:57 Custom Lead Enrichment with Cursor12:01 Comparing DIY Solutions to Established SaaS14:58 The Future of Custom Solutions vs. SaaS18:01 Building Tools for Specific Needs20:15 Custom Tooling for Business Needs20:57 Introducing Super Sim: A Game Changer22:12 Document Management in Private Equity24:00 Customization and Metrics in Super Sim26:10 The Role of LLMs in Data Analysis28:14 From Data to Insights: The Moneyball Approach29:56 Creative Uses of Structured Data32:00 Unlocking Value in Unstructured Data35:42 Closing Thoughts and Future OpportunitiesAI tools, workflow automation, Lindy, N8n, lead enrichment, SuperSIM, document management, business applications, custom solutions, technology trends