In this episode, I talk with Jeffrey Meade, founding director of entrepreneurship at Paul Quinn College, about the school’s “Every Quinnite is an Entrepreneur” program, where by 2028 every student must launch a real business that generates revenue before graduating. Jeff explains why traditional schools optimize for employment but the job market is changing, making entrepreneurial thinking—creativity, adaptability, and problem-solving—more essential. We dig into what qualifies as a “real business” (no fixed revenue threshold, but real customers, pivots, and an MVP mindset), why they focus on solving a core problem before layering in AI, and the mindset shift first-generation and Pell Grant students must make from “don’t fail” to “fail fast” in a safe learning environment. Jeff also shares how he’s open sourcing the model through case studies and a podcast, plus where to learn more.

00:45 Why Entrepreneurship Now

03:38 Mandatory Student Ventures

04:35 MVPs And First Sales

06:52 AI Tools Problem First

10:31 From Scarcity To Confidence

13:06 Safe Space To Fail

15:18 Open Sourcing The Model

17:29 Where To Learn More

Connect with Jeffrey:

• https://paulquinn.edu/entrepreneurship
• jeff-meade.com
• jmeade@pqc.edu
• https://www.linkedin.com/in/jeffmeade

Connect with Raul:

• Work with Raul: https://dogoodwork.io

• Free Growth Resources: https://dogoodwork.io/resources

In this episode, I interviewed Patrick Rooney, a cognitive science–trained AI practitioner and founder of Leonis Strategy, about how founders mischaracterize AI by collapsing “scripted autonomy” (agents doing tasks while you step away) into personhood autonomy (will, rights, interiority). Patrick argues this isn’t just sloppy language but a leadership issue that shapes how teams relate to technology. They discuss why LLMs are plausibility engines rather than truth-seekers, how humans can pursue truth, beauty, and goodness for their own sake, and why leaders must own inputs, outputs, and responsibility instead of outsourcing judgment. We explored why LLM training is text-bound and disconnected from lived experience, the appearance-versus-reality problem behind Turing-test thinking, practical cautions around anthropomorphizing AI, and why doubling down on in-person human connection is a strategic response to AI at scale.

01:53 LLMs Are Plausibility Engines

05:10 Leadership And Culture Values

07:34 Why LLMs Aren't Intelligent

08:54 Turing Test And Training Limits

12:42 Language Detached From Reality

14:48 Personhood Rights And Ethics

19:01 Anthropomorphism Risks

19:34 Human Ownership Mindset

20:25 Outsourcing Your Thinking

22:24 IP Training Fears

24:34 Responsibility Still Human

28:41 Leading In AGI Hype

29:38 Grounding In Real Life

Connect with Patrick:

• https://leonisstrategy.com/

• https://www.linkedin.com/in/prooney1/

Connect with Raul:

• Work with Raul: https://dogoodwork.io/apply

• Free Growth Resources: https://dogoodwork.io/free-growth-resources

In this episode, I’m joined by Jim Ristuccia, a former US Naval officer, founder/CEO, and Vistage Chair in San Diego who leads peer groups for CEOs of $5M+ companies and small business owners in the $1–10M range. We break down what actually happens inside a Vistage meeting—especially the “issue processing” that surfaces blind spots through uncomfortable but growth-producing questions. Jim shares how AI started showing up consistently after a 2024 speaker session, including a case where an IP law firm put its team through AI bootcamps, tripled personal productivity, and grew revenue 50–60% in a year. We discuss the adoption gap (CEOs vs. leadership teams vs. employees), why employees often feel threatened, and how leaders can create psychological safety. We also dig into practical implementation via AI champions, committees, process mapping, low-hanging-fruit use cases, and measuring ROI through time savings, productivity, and revenue.

01:56 What Vistage Really Is

02:45 Issue Processing Secret Sauce

04:46 When AI Hit the Groups

06:43 Why Some CEOs Lag on AI

08:44 Use Cases vs Business Model Shifts

09:45 AI Readiness and Industry Friction

11:20 Leading Change and Finding Use Cases

13:25 Prompts Training and Psychological Safety

15:01 AI Champion and Process Mapping

16:04 Why People Quit

16:32 Build AI Champions

17:58 Pick Use Cases Fast

18:33 Measuring AI Impact

20:00 Beyond Time Savings

21:02 Culture and Safety

23:48 Compliance and Knowledge

24:32 Jobs and Business Models

27:38 Adoption Takes Time

Connect with Jim:

• https://jimristuccia.com/

Connect with Raul:

• Work with Raul: https://dogoodwork.io

• Free Growth Resources: https://dogoodwork.io/resources