I sat down with John Johnson, founding CEO of Patmos Hosting, to unpack how he bootstrapped a data center company with no outside capital after a big tech host threatened to cancel a client, leading him to pursue independence by “controlling the dirt.” We talk about his rule “he who pays, rules,” why he believes VC money erodes a founder’s mission, and how owning infrastructure and going upstream helps you control your destiny and protect your data. John also explores AI’s risks—counterfeit human personhood and reducing humans to artifacts—while arguing for a renaissance where authentic humanity becomes a premium. He shares how Patmos is redeveloping the old Kansas City Star printing press into a C-PACE financed, assessed clean-energy data center plus a downtown tech hub to create jobs and urban renewal. We also discuss his Albertus Magnus Institute and his North Star: human freedom.

03:25 Counterfeit Humanity & the Organic Human Market

08:07 AI and the Technocratic Paradigm

11:21 The Human Renaissance: Practical Counter-Revolution

14:08 Owning the Dirt: Freedom Through Education & Independence

14:20 John's Journey: From Ministry to Data Centers

22:55 Patmos: Free Speech Hosting & AI Infrastructure

27:23 Data Centers, Sustainability & The Kansas City Story

32:20 Owning Your Own Data & Patmos Phase Two

Connect with John: 

• https://patmos.tech/

• https://magnusinstitute.org/

Connect with Raul: 

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

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

• Connect with Raul on LinkedIn (DMs open): https://www.linkedin.com/in/dogoodwork/ 

In this podcast, I sat down with Ronan Leonard, founder of Intelligent Resourcing, to break down how he helps sales teams eliminate the 40% of their day spent manually researching companies and instead deliver real buying signals directly into the CRM so reps can take action. We talked about how fast AI is reshaping business models—Ronan compared it to waking up and finding the snow gone overnight—and why he refuses to build SaaS right now. He explained “dark data” hidden in sales call transcripts, how he enriches CRMs into “evergreen” systems, and how those insights feed content and GEO/answer optimization. We also covered tool-stack volatility, internal tooling vs productizing, structuring teams around learning speed, value-based pricing and price elasticity, and we had an honest disagreement on co-risking and revenue-share deals.

01:36 AI Overwhelm and Pace

03:19 Snowstorm Business Models

04:13 No SaaS Moat Strategy

05:09 Signals Into the CRM

06:45 Dark Data and Transcripts

08:55 Scaling Clients and LTV

15:49 Team Structure and Learning

19:41 Agents vs SOP Iteration

24:39 Standardize Custom Work

24:59 Value Based Pricing Framework

27:48 Cost Savings Case Study

30:16 Pricing as Perception

31:49 Why Upside Deals Fail

35:36 Confidence and Client Execution

36:34 Staying Ahead of AI Curve

39:37 Creativity and Feedback Loops

Connect with Ronan: 

• https://www.linkedin.com/in/ronan-leonard/

https://intelligentresourcing.co/

Connect with Raul: 

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

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

• Connect with Raul on LinkedIn (DMs open): https://www.linkedin.com/in/dogoodwork/ 

How AI Will Evolve the 7 Parts of Your Business Model: 
Leverage AI to enable you to solely focus on client relationships, belief and the nuances of your work

00:00 The Bold Pricing Vow
01:42 AI Labs Enter Services
03:29 Your Offer Is Not The Moat
05:00 Seven Model Ingredients
06:47 How AI Changes Each Part
15:25 The Evolved Model Blueprint
25:26 Three Breakthrough Signals
29:49 Four Steps To Redesign
36:53 Closing Call To Evolve

Substack Version of this podcast: 
https://dogoodwork.substack.com/p/how-ai-will-evolve-the-7-parts-of

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
• [email protected]
• 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

In this episode, I’m joined by Thaddius Ruszkowski, CTO at Fuzati, who’s built software platforms for over a decade, from 25-person internal workflow systems to consumer platforms with millions of users. Thadd isn’t just theorizing about AI agents; he’s running a fleet of them in parallel every day across multiple projects. We dig into what happens to value, intellectual property, and labor as production costs collapse, and why efficiency is only the opening round, not the real competitive advantage of AI. We also talk about managing parallel agents without losing your mind, the comeback of hardware, why on-prem/local will matter more than most people think, and why IP is actually more valuable now in a world flooded with slop. Plus, how he built an early chatbot trained on the writings of St. Thomas Aquinas.

03:12 Working With Agents

04:44 Aquinas Chat Origins

10:28 Beyond Efficiency

20:49 Net New Value

24:28 Create Net New Value

25:02 Decouple Labor From Value

27:58 Outcome Value Shift

30:26 Future Digital Assets

34:35 Three AI Futures

37:58 IP And Distribution

40:56 Local Hardware Shift

42:42 Client AI Disclosure

Connect with Thadd:

https://www.linkedin.com/in/thad-ruszkowski/

Connect with Raul: 

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

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

• Connect with Raul on LinkedIn (DMs open): https://www.linkedin.com/in/dogoodwork/ 

What AI Native Consulting Actually Looks Like: The 4 Stages

00:00 Why AI Native Matters
02:26 Four Stage Roadmap
03:10 Stage One Enhancement
06:39 Stage Two Augmentation
14:15 Stage Three Parallel Ops
18:43 Agentic Workflow Examples
24:17 Stage Four Autonomy
33:10 Autonomy In Practice
37:36 Self Diagnostic Recap
41:52 Firm Of The Future

Link to the full Substack: 
https://dogoodwork.substack.com/p/what-ai-native-consulting-actually

In this episode, I sit down with Dr. Tracy Christopherson and Michelle Troseth —co-founders of Missing Logic and authors of Polarity Intelligence—to talk about a major pivot they made from serving healthcare leaders to serving entrepreneurs, and how they had to use their own polarity framework when tension rose in their partnership. We unpack how to tell the difference between solvable problems and ongoing polarities, why “problems” that keep returning are often tensions to manage, and how to map polarities by identifying the benefits and risks of each pole, defining actions, and watching early warning signs. We also discuss identity shifts for thought leaders, the role of healthy relationships and meaningful dialogue on teams, and practical ways to measure and monitor tensions. 

01:15 Why They Pivoted

03:36 Alignment and Identity

06:39 Walking Polarity Pair

09:02 Problem vs Polarity

11:05 Mapping and Warning Signs

14:04 Tech and Dynamic Balance

17:46 Working Through Tension

22:39 Dialogue and Resistance

Connect with Dr Tracy Christopherson and Michelle Troseth.: 

• www.polarityintelligence.com 

www.missinglogic.com

https://www.linkedin.com/in/michelletroseth/

https://www.linkedin.com/in/tracy-christopherson/

https://www.instagram.com/polaritypowerpair/

https://www.facebook.com/polaritypowerpair/

Connect with Raul: 

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

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

Most conversations about AI in consulting stop at productivity. Doing the same thing faster. That's the wrong frame entirely.

In this episode, I break down the three form factors for digital products: information products, software products, and what I'm calling actors. An actor is an AI agent or multi-agent orchestration that deploys your expertise on your behalf without requiring your ongoing presence at every step. The distinction that matters: an actor becomes a producer when it generates real market value, not just activity.

I walk through how I built this into my own content pipeline (four hours down to fifteen minutes), how clients are using actors to overdeliver on their initial offer at 2-3x the going rate, and why "efficiency" is the wrong frame for what's actually happening. This is the next piece in a series. The Services Stack covers the thesis. The New Value Quadrant covers pricing. The Post Scope Era covers practical sales and delivery changes. This episode covers the new form factor that makes all of it possible.

Key topics covered:

• What a "form factor" actually means and why it matters for how you price and package
• Form Factor 1: Information products (ebooks, courses, coaching, communities) and the key person problem
• Form Factor 2: Software products and why they require a team and capital
• Form Factor 3: Actors, AI agents that produce outcomes on your behalf
• The distinction between an agent that acts and a producer that creates net new market value
• Why your methodology, positioning, and expertise dictate the value an actor creates
• Real example: content scheduling from four hours to fifteen minutes
• How clients are using actors to deliver 2-3x more value at standard pricing
• Efficiency vs. production: doing the same thing faster vs. creating something new
• Why speed to value creation might be the real variable
• Human flourishing as the real goal
• Three steps to build your first actor

CHAPTERS

• Website: https://dogoodwork.io
• Newsletter: https://dogoodwork.substack.com
• https://dogoodwork.substack.com/p/how-ai-is-creating-the-3rd-form-factor