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 Failures
04:00 ChatGPT's New Browser: A Game Changer?
07:33 Best Practices for AI Workflow Automation
12:00 Navigating the AI Coding Assistant Market
16:55 New Opportunities: AI Empowering New Work
21:28 Valuing Software in the Age of AI
Key Takeaways:
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 get
Practical takes from people shipping client projects
Where Claude 4.5 vs GPT shines (coding vs writing)
When to use “extended thinking/deep research” vs fast models
Real talk on agents, meeting schedulers, and workflow design
Energy, nuclear, and why AI ≈ infrastructure
Open-weights vs ecosystems: where the moat really is
Chapters
00:00 – Cold open & intro
00:26 – Who’s Tyler Cowen and why this format
02:00 – 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 ask
Links & mentions (non-sponsored)
Tyler Cowen / Marginal Revolution
Anthropic Claude 4.5 (coding + writing)
OpenAI GPT-5 (auto/fast tasks), Deep Research modes
Lindy meeting agent
Replit 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).
Chapters
00:00 Intro
00:32 Google’s “Nano Banana” image model—why it feels like a Photoshop killer
02:40 Real tests: thumbnails, character persistence, interior wallpapering
05:58 GPT-5 hype vs reality; coding speed vs chat experience
10:52 OpenAI Codecs & CLI vs Claude Code (features, trade-offs)
12:26 Anthropic caps/throttling—what changed and why it matters
14:22 Browser agents (Claude for Chrome): promise vs practical limits
20:01 MIT report: “95% fail” explained—what the data actually says
27:55 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 talent
43:02 What we’re excited about: E*TRADE API snapshot, fantasy draft tool, Nano Banana
45:38 Wrap
Key takeaways
Build 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 Learn
Why 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 Sourcing
Deal Evaluation & Diligence
Fund Operations
Value Creation & Investor Relations
Concrete 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 & Resources
Zapier 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=259s
Full PE AI-fluency matrix & examples → coming soon on runpoint.ai
Subscribe for more AI-in-business deep dives → 🔔
Chapters
00:00 Intro
01:00 Zapier’s AI Fluency Framework explained
10:00 Deal Sourcing—spray-and-pray vs adaptive agents
17:00 Diligence workflows with Replit & contract analyzers
24:25 Fund Ops dashboards, data warehouses & the ‘impossible’ chatbot
32:00 Sam’s multitasking Claude Code + GitHub flow
35:00 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.
takeaways
Chapters
00:00 - Introduction to AI and Go-to-Market Strategies
01:26 - Eras of Outbound Sales: A Historical Perspective
04:07 - The Sequencing Era: Overload and Complexity
08:45 - The Shift to Quality Lists and Personalization
11:32 - The Rise of Clay: A New Era in Sales Tools
14:20 - Fragmentation vs. All-in-One Solutions
18:07 - Human Interaction: The Key to Effective Sales
21:31 - Understanding the Value Proposition
24:37 - Leveraging AI in Sales Processes
28:47 - Building Targeted Lists with AI
31:24 - The Role of Personalization in Outreach
34:36 - 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:
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
keywords
AI, ChatGPT, Google I.O., newsletter automation, voice memos, AI tools, private equity, AI quality, sycophancy, technology trends
summary
In 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.
takeaways
Chapters
00:00
Introduction to the Run Point Podcast
01:32
Practical AI Applications for Today
03:35
Voice Memos and AI
09:00
Innovative Newsletter Creation with AI
16:20
Google I.O. Highlights and AI Innovations
19:40
Exploring AI Utility and Frustrations
20:21
Google's AI Advancements and Market Position
22:08
The Future of Google's Ad Revenue
25:51
The Concept of AI Slop and Content Quality
28:05
Addressing AI Sycophancy and User Experience
30:51
The Need for Better AI Branding and Understanding
34:16
AI as a Force Multiplier in Everyday Life
keywords
AI, tools, development, PowerPoint, automation, coding, data analysis, business insights, technology, productivity
summary
In 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.
takeaways
Chapters
00:00 - Introduction and Exciting Projects
03:23 - Tools You Can Use Today
17:58 - Hot Takes on PowerPoint and AI
30:55 - 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.
Chapters
00:00 Introduction to AI Tools and Their Applications
02:47 Exploring Lindy: The Agent Swarm Approach
05:59 N8n: A Developer-Friendly Automation Solution
08:57 Custom Lead Enrichment with Cursor
12:01 Comparing DIY Solutions to Established SaaS
14:58 The Future of Custom Solutions vs. SaaS
18:01 Building Tools for Specific Needs
20:15 Custom Tooling for Business Needs
20:57 Introducing Super Sim: A Game Changer
22:12 Document Management in Private Equity
24:00 Customization and Metrics in Super Sim
26:10 The Role of LLMs in Data Analysis
28:14 From Data to Insights: The Moneyball Approach
29:56 Creative Uses of Structured Data
32:00 Unlocking Value in Unstructured Data
35:42 Closing Thoughts and Future Opportunities
AI tools, workflow automation, Lindy, N8n, lead enrichment, SuperSIM, document management, business applications, custom solutions, technology trends