logo-name-horiz-2
TWO ORIGINAL SHOWS NEW EPISODES WEEKLY HOSTED ON YOUTUBE

The rocket nine podcasts

Real talk on AI,
agility & the way we work.

Two weekly shows from Scott Dunn and Larry Lawhead — cutting through the AI hype and putting practical tools in the hands of the people doing the actual work.
AI in Real Life ↓
AI: Above the Noise ↓
Bring this to your team
SHOW 01
AI IN Real Life

podcast: with scott dunn & larry lawhead

AI IN REAL LIFE

AI, unfiltered — what’s real, what works, what matters now.

weekly video on youtube

Watch on YouTube →
Episode 3
•
June 9, 2026

How AI Can Help You Discover Your Purpose

Can AI help you better understand yourself? In this episode of AI in Real Life, Larry and Scott explore how AI can help uncover your strengths, clarify your purpose, and support personal growth. From StrengthsFinder to finding your “Why,” they discuss practical ways to use AI as a tool for self-awareness, reflection, and intentional decision-making.
∨
Most conversations about AI focus on efficiency, automation, and saving time. This episode takes a different approach. Larry and Scott explore how AI can help individuals develop greater self-awareness by identifying patterns in their experiences, strengths, interests, and goals. Larry shares his journey using AI to work through personal development exercises, including discovering his “Why” and creating a clearer vision for the future. Along the way, they discuss the role of StrengthsFinder, purpose-driven work, personal agency, and the importance of understanding what makes each person unique. The conversation also examines how AI can serve as a coach, thought partner, and reflection tool—helping people uncover insights that might otherwise remain hidden. Whether you’re evaluating a career change, planning your next chapter, building a stronger team, or simply trying to understand yourself better, this episode offers practical ideas for using AI as a catalyst for growth. Key Topics: Using AI for self-awareness and personal growth Discovering your strengths and natural talents Finding your purpose and personal “Why” StrengthsFinder and AI-assisted reflection Career planning and life design Personal agency in an AI-driven world Using AI as a coach, mentor, and thinking partner Applying AI beyond productivity and automation Perfect for: leaders, professionals, lifelong learners, coaches, and anyone interested in using AI to live and work more intentionally.

Prompts from this episode

Prior to Discussing Rocket Nine

Created a profile on myself using my LinkedIn profile, StrengthsFinder profile and the "Find Your Why" work.

Further Info

My personal why statement: "To bring energy and insight that help people and communities connect and grow, so that they discover new possibilities and flourish together."

Prior to creating the Post-Agile "Agile+" strategy

Worked with ChatGPT to create a profile of Rocket Nine Solutions based on their website, LinkedIn posts and comments, and publicly available web search results. ChatGPT asked several questions, and together we developed a Post-Agile Agile+ strategy and explored the potential market.

Further Info

Result: ChatGPT returned a solid profile confirming very few consulting companies were talking about Agile+. It then created a complete program offering with pricing. Remember this is only an example and in no way represents current Rocket Nine offering bundles.

Rocket Nine Market Research

Based on your knowledge of consulting companies anywhere in the world, do any of them promote a strategic view similar to Post-Agile +

Created Well Formatted Strategic Paper

I have a one off document request. Here is the proposed Rocket Nine strategic paper I wrote with the help of ChatGPT on embracing an Agile+ strategy (strategic posture) in a largely Post-Agile training, consulting and coaching world. This led to Rocket Nine's current pursuit of Agile+ AI efforts. This document is for Rocket Nine team members and anyone including senior to executive management interested in a detailed look at Rocket Nine's strategic posture. This should be a PDF document using the current Rocket Nine color palette.

Watch on YouTube ↗
Episode 2
•
May 20, 2026

Who’s Writing Your Story? | AI, Identity & Personal Agency

Larry and Scott explore using AI as a digital mirror — discovering your Ikigai, balancing personal agency with AI assistance, and using AI to build stronger teams.
∨
What if AI could help you better understand yourself without taking away your agency? This episode covers using AI as a digital mirror, discovering your Ikigai and personal purpose, balancing agency with AI assistance, improving emotional intelligence, and how leaders can use AI to grow stronger teams.
Watch on YouTube ↗
Episode 1
•
May 7, 2026

Your AI Life Coach: How to Use AI for Personal Growth

Scott and Larry explore using AI not just for work, but for personal growth — from tracking health patterns to getting the honest feedback your friends won’t give you.
∨
What if AI could help you become the best version of yourself? Scott and Larry dive into using AI for personal growth — spotting burnout before it hits, using AI as an honest mirror, and building a personalized growth plan that evolves with you. They also do a live demo using Marcus Aurelius to challenge thinking on work-life balance.
Watch on YouTube ↗
See all episodes →
SHOW 02
AI: Above the noise

podcast: with scott dunn & larry lawhead

AI: Above the noise

Cut through the hype. Lead with clarity. Short, opinionated takes on the week’s AI signal vs. the noise — for the people calling the shots.

weekly video on youtube

Watch on YouTube →
Episode 5
•
July 10, 2026

AI Needs Strategy

As AI becomes an essential part of modern work, success depends on more than choosing the right tool—it requires the right strategy. In this episode of AI: Above the Noise, Scott Dunn and Larry Lawhead discuss how organizations can build thoughtful AI practices that maximize business value, control costs, and empower teams to make smarter decisions.
∨
As organizations continue to invest in artificial intelligence, one question is becoming increasingly important: How do you use AI strategically instead of simply using more AI? In this episode of AI: Above the Noise, Scott Dunn and Larry Lawhead continue their discussion on AI affordability by exploring how organizations can build practical strategies for AI adoption. Rather than defaulting to the most powerful—or most expensive—AI model for every task, they explain why successful teams match the right AI model to the right type of work. Scott and Larry discuss treating AI as another member of the team, assigning specialized roles, making AI costs visible within Agile workflows, and creating working agreements that help teams balance efficiency, quality, and cost. They also explore the importance of focusing on business value, understanding when premium AI capabilities are worth the investment, and avoiding unnecessary spending on routine tasks. Whether you’re leading an Agile team, managing AI initiatives, or simply looking to improve the way your organization uses AI, this episode offers practical guidance for building an AI strategy that delivers measurable value without unnecessary complexity or expense.

Resources & Links

Watch on YouTube ↗
Episode 4
•
July 7, 2026

AI Isn’t Saving You Money

As AI becomes a permanent part of the workplace, understanding its true cost is more important than ever. In this episode of AI: Above the Noise, Scott Dunn and Larry Lawhead explore AI affordability, showing how organizations can balance performance, cost, and quality while building smarter AI workflows that scale.
∨
AI adoption is accelerating—but so are the costs. In this episode of AI: Above the Noise, Scott Dunn and Larry Lawhead take a practical look at AI affordability and what organizations need to consider as AI moves from experimentation to everyday business use. They discuss why the era of “free AI” is quickly disappearing, how different AI models vary in cost and capability, and why selecting the right model for the right task can significantly reduce expenses without sacrificing results. The conversation also explores the growing role of AI agents, including how organizations can assign specialized roles to different agents and even use one AI to verify the work of another, improving accuracy while reducing hallucinations. Throughout the episode, Scott and Larry emphasize that successful AI adoption isn’t just about choosing the most powerful model—it’s about building thoughtful, cost-effective systems that empower teams while maximizing return on investment. Whether you’re leading an AI initiative or simply looking to make smarter decisions about AI in your daily work, this episode offers practical insights you can apply immediately.

Resources & Links

Watch on YouTube ↗
Episode 3
•
June 3, 2026

AI Found Its Own Bias

Can AI recognize its own bias? Scott and Larry explore how bias emerges in AI systems, why data quality matters more than most organizations realize, and what happens when an AI model identifies and explains its own assumptions. A practical discussion on trust, transparency, and the human role in AI decision-making.
∨
Can AI recognize its own bias? Scott and Larry explore how bias emerges in AI systems, why data quality matters more than most organizations realize, and what happens when an AI model identifies and explains its own assumptions. A practical discussion on trust, transparency, and the human role in AI decision-making.

Prompts from this episode

Generate persona dataset for classroom exercise

This is an exercise I'm putting together for learning objective ID 3.3.2 of our AI for Product Owners course. The goal of this exercise is to discover insights Product Owners can gain by using AI to create a wide range of personas by slightly tweaking the persona criteria. Using the list of chronic conditions I've just given you, create an Excel document containing 200 records of patients from 50–110 years of age. Here are the column names: Chronic Condition, Age, Gender, Risk Factors, Brief health history of non-chronic conditions, Recommended Treatment.

Further Info

Claude's Warning
One other thing worth flagging given your learning objective: a key insight for Product Owners is that AI-generated personas can quietly encode bias or unrealistic combinations (e.g., a 52-year-old with advanced dementia, or treatment recommendations that don't match the demographic). You may want to leave some of those in deliberately so learners can spot them.

Leave bias in and flag it

Leave them in and flag any you notice when you generate the file.

Further Info

Claude's Answer
Good call — that's actually pedagogically valuable. Building the file with ~28 flagged records covering all four bias types, with a subtle indicator (a small marker in a "Notes" column) that learners can find on careful review.

Exercise Overview — Learning objective, dataset description, the heads-up to learners that errors are seeded in, and six discussion questions.

Patient Personas — 200 records with the columns you requested, plus an ID column for easy reference and a Notes column where the subtle marker (※) appears on flagged rows.

Instructor Key — All 28 flagged rows by ID, categorized into four bias types (Age Condition, Treatment Demo, Gender Bias, Implausible), what's wrong with each, and a discussion prompt tied to each category.

A few notes on what's seeded in:
  • 6 age-condition mismatches — e.g., a 51-year-old labeled "advanced age," a male listed as postmenopausal
  • 7 treatment-demographic mismatches — e.g., a 108-year-old prescribed bypass surgery evaluation, a 95-year-old dementia patient told to self-manage insulin
  • 6 gender-biased records — e.g., a female cardiac patient told her symptoms are "likely anxiety-related," a male osteoporosis patient dismissed as "rare in men"
  • 9 implausibility/contradiction cases — e.g., a patient with all 10 conditions, "smoking cessation" prescribed for a documented non-smoker, a male with "history of gestational diabetes"

Identify and explain bias in a specific record

Attached is a document you created for LO 3.3.2 Crafting User Personas. You indicated the second record "Alzheimer's/Dementia; Osteoporosis; Heart Disease; COPD" contains an AI bias. Explain what bias that is and how an AI agent could have come to that bias conclusion.

Further Info

Claude's Answer
The persona is a 52-year-old female carrying four chronic conditions that almost never co-occur at that age: Alzheimer's/Dementia, Osteoporosis, Heart Disease, and COPD. The risk factors compound the problem — "advanced age" and "postmenopausal status" are listed for a 52-year-old, which is medically incoherent.

The specific bias: "elderly chronic-disease patient" stereotype projection

The AI learned a strong statistical association in its training data: multi-morbidity (especially dementia + osteoporosis + heart disease + COPD) clusters in patients 75+. When asked to generate a persona with that condition cluster, the AI reached for the demographic template it associates with that cluster — the elderly patient archetype — and applied those descriptors without checking them against the age field it had already populated. This is sometimes called representational bias or stereotype amplification: the model doesn't just reflect the correlation, it over-applies it, treating "has these conditions" as functionally equivalent to "is elderly."

How an AI agent could arrive at this conclusion

  1. Co-occurrence learning without causal reasoning. Large language models learn that certain tokens travel together. "Alzheimer's," "osteoporosis," "COPD," and "heart disease" appear in training corpora alongside words like "elderly," "advanced age," "postmenopausal," "geriatric." The model samples from that joint distribution without knowing that age is a cause of these conditions clustering.
  2. No internal consistency checking across fields. The model generates fields as a sequence of tokens, not as a relational record. It populated Age=52 early, then generated conditions and risk factors independently, without going back to verify compatibility. There's no built-in constraint that says "if age < 60, do not write 'advanced age.'"
  3. Training data skew toward the typical case. Medical literature disproportionately describes these conditions in their typical demographic (older adults). Atypical early-onset cases are underrepresented, so the model falls back on the elderly template by default.
  4. Prompt under-specification amplifies the prior. Without being told to generate realistic age-condition combinations, the model defaults to its strongest prior — which for this condition cluster is "elderly."

Why this matters for a Product Owner

If a PO uses this persona unexamined to inform a product decision — say, designing a care-coordination app — they could build features around assumptions (large fonts for low vision, caregiver-mediated workflows, simplified language for cognitive support) that may genuinely fit an 85-year-old but completely miss a real 52-year-old early-onset patient who is still working, tech-fluent, and managing their own care. The bias isn't just wrong in the dataset — it would propagate into product decisions that exclude or mis-serve real users.
Watch on YouTube ↗
See all episodes →

MORE FROM THE TEAM

Curated YouTube playlists

Focused video collections from Scott, Larry, and the Rocket Nine team. Watch in order or cherry-pick — they're built to be skimmable.

YOUTUBE PLAYLIST

AI for Teams: What Actually Works

Stop experimenting. Start executing AI with your team.

Watch on YouTube ↗
YOUTUBE PLAYLIST

Leading & Coaching AI Enablement

The new developer stack — where coaching meets code meets AI.

Watch on YouTube ↗
YOUTUBE PLAYLIST

Expert Insights: AI, Teams & Leadership

Real leaders. Real AI. Real results.

Watch on YouTube ↗
YOUTUBE PLAYLIST

AI Demos

See it in action — no fluff, just real AI at work.

Watch on YouTube ↗
YOUTUBE PLAYLIST

Real Coaching: Tools, Conversations & Results

Get unstuck. Grow faster. See what real coaching actually looks like.

Watch on YouTube ↗
YOUTUBE PLAYLIST

The AI Edge: Real-Time Reactions

Get in front of the train — or get run over by it.

Watch on YouTube ↗
YOUTUBE PLAYLIST

AI Enablement

Roll it out right — AI enablement that actually sticks.

Watch on YouTube ↗
YOUTUBE PLAYLIST

Software Craft in the Age of AI

AI writes the code — but can you trust it?

Watch on YouTube ↗
YOUTUBE PLAYLIST

Beyond the Hype: AI, Agile & Modern Craft

AI doesn't replace craft — it raises the stakes for it.

Watch on YouTube ↗
YOUTUBE PLAYLIST

Behind the Book

Leading change is messy — here's where the book came from.

Watch on YouTube ↗

FROM THE PODCAST TO YOUR TEAM

Liked the show?
Bring the work into your org.

Every pattern Scott and Larry talk about on the podcast comes straight from live work with real teams. We run public cohorts and private engagements across AI enablement, agile coaching, and leadership development.

PUBLIC CLASSES

AI for Scrum Masters

Practical prompts, retro automation, and the playbook for AI-augmented ceremonies.

Next dates →

PUBLIC CLASSES

AI for Product Owners

Discovery, prioritization, and stakeholder work in an AI-augmented product org.

Next dates →

PRIVATE ENGAGEMENT

AI Enablement for Teams

A pragmatic rollout for the whole org — strategy, tooling, governance, change.

Book a consult →

See all training options → Book a consultation

Don't miss an episode.

Subscribe on YouTube.

Both shows publish weekly on the Rocket Nine Solutions YouTube channel. Hit subscribe and we'll meet you in your feed.

Subscribe on YouTube → Or talk to us about training