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The Calum Johnson ShowThe Queen Of AI: The Fastest Way To Use AI To Make Your First Million (For Beginners!)
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CHAPTERS
- 0:00 – 2:48
Why this episode is an interactive AI money workshop (and why it’s “your moment”)
Alicia frames the episode as a hands-on workshop where listeners can copy prompts and get results in real time. She explains that the goal is to build confidence with AI—specifically Claude—and turn “I don’t know where to start” into momentum.
- •Alicia says the episode will provide prompts people can use while watching
- •She emphasizes confidence-building through doing, not just watching
- •Calum highlights Alicia’s scale training entrepreneurs and wants a step-by-step process
- •Alicia positions “great results” as new insights, clients, motivation, or a new business
- 2:48 – 6:29
From fear to opportunity: the “Donna” example of learning AI skills and getting paid
Calum asks who “Donna” is, and Alicia describes a laid-off worker who built confidence by learning AI skills and talking about them publicly. The story illustrates how small, practical AI competence can create new paid opportunities when people share what they know.
- •Alicia defines “great results” as outcomes people weren’t getting before
- •Donna’s fear and uncertainty after being laid off shifts as she learns AI skills
- •Donna gets booked to teach at a library and gains customers from speaking
- •Alicia stresses that knowing “something that can help someone else” is enough to start
- 6:29 – 10:26
Early entrepreneurship spark: cleaning houses and realizing business owners ‘create jobs’
Alicia recounts a formative childhood moment cleaning wealthy homes with her mother and asking what the owners did for work. Her mother’s answer—entrepreneurs create jobs—sets Alicia on a path of learning entrepreneurship and noticing her mother was also a business owner.
- •Alicia as a child questions what jobs lead to large homes
- •Her mother explains they’re business owners who create jobs
- •Alicia realizes entrepreneurship is a path to a different life
- •She later recognizes her mother ran a word-of-mouth cleaning business
- 10:26 – 22:34
Building online, then losing everything: divorce, starting over, and monetizing knowledge
Alicia summarizes her entrepreneurial journey: learning online marketing early, doing everything in the business, then leaving a marriage and being locked out of assets and accounts. She describes restarting from her sister’s couch by selling services, solving marketing problems, and focusing on the one thing she couldn’t lose—her knowledge.
- •Alicia starts online entrepreneurship in 2000, handling marketing through fulfillment
- •She describes a difficult divorce where she leaves with her car, laptop, and phone
- •Alicia restarts via Fiverr as a website usability tester to generate income
- •She spots a resort’s marketing problem and earns by driving bookings via platforms
- •She emphasizes: material things can be taken, but knowledge can’t
- 22:34 – 28:31
The AI awakening: from early tools to ChatGPT—and the power of training the model on you
Calum asks about Alicia’s first major AI shift, and she describes her initial skepticism, then a two-week deep dive into ChatGPT. She explains that early outputs were robotic until she trained the tool on her voice and personal context, which changed the quality of results and revealed a broader market need for trainers.
- •Alicia used earlier AI-assisted tools but initially dismissed ChatGPT
- •She locks in to learn it intensively and discovers training improves outcomes
- •She feeds writing samples and personal information to get better outputs
- •She identifies demand: many users and companies need help using the tool well
- •She pivots toward teaching and “train the trainer” due to incoming requests
- 28:31 – 35:53
Objections and fears: ‘I’m not technical,’ prompting anxiety, and the 3-step “super prompt” method
Alicia lists the common reasons people avoid AI and reframes them as solvable barriers. She introduces a simple prompting workflow—brain dump, clean-up, then “turn this into a super prompt”—to help people get better results without feeling like they need prompt engineering expertise.
- •Alicia says the #1 objection is “I’m not technical,” and reframes AI as conversation
- •She describes fear driven by negative news and unknown risks
- •She addresses “I don’t know how to prompt” with a 3-step process
- •Steps: brain dump → ask AI to clean it up → ask AI to turn it into a “super prompt”
- •Calum notes the mindset shift from using AI like Google to collaborating with it
- 35:53 – 40:58
Why Alicia teaches so personally: toxic relationship illness, cancer, and pouring into others
Calum asks why Alicia shows up with such energy and care, and she shares how illness and major life losses shaped her purpose. She describes ovarian cancer, repeated recurrence, and losing the chance to have children, then finding fulfillment by helping students achieve and celebrating their wins.
- •Alicia shares her history with ovarian cancer and multiple recurrences
- •She describes Les Brown introducing the idea of toxic relationships affecting health
- •She connects leaving the relationship with becoming cancer-free afterward
- •She explains teaching fulfills her desire to nurture and support others’ growth
- •She emphasizes entrepreneurship’s ups and downs and finding meaning through impact
- 40:58 – 47:22
Step 1—Overcome fear with privacy controls: Claude settings, training toggle, and incognito mode
Alicia demonstrates where privacy settings live in Claude and which options she recommends turning off for personal accounts. She explains the model-training toggle, location metadata, and when to use incognito mode, then discusses memory—why she uses it and how to delete stored memories.
- •Alicia shows where to find Claude privacy settings and preferences
- •She recommends turning off location metadata and “help improve our AI models”
- •She notes corporate/workspace accounts default model-training to off
- •She says to avoid entering sensitive personal identifiers like Social Security numbers
- •She explains incognito mode and Claude memory, including deleting stored items
- 47:22 – 1:07:28
Step 2—Develop “voice DNA” to eliminate AI-sounding output (with a live example)
Alicia explains why generic AI output gets flagged and how to train Claude to write in a specific voice using one prompt and three writing samples. She demonstrates building a client voice profile (“Michelle”), explains using multiple contexts and multiple “voice DNAs,” and compares a trained result to a generic one.
- •Alicia argues sounding like you prevents “you used AI” reactions from readers
- •She shares a prompt that analyzes writing patterns and produces ghostwriter rules
- •She demonstrates creating a client voice DNA and saving it for reuse
- •She recommends varied samples (text/email/social) and identifying what to avoid (e.g., certain phrases)
- •They compare voice-trained messaging vs a generic “I hope you’re doing well” style
- 1:07:28 – 1:16:10
Step 3—Personal intelligence blueprint: using your resume to surface paid skills you’re underselling
Alicia distinguishes voice from identity and capability: the blueprint captures who you are, constraints, stories, and real strengths. She introduces prompts to have Claude read a resume like a business advisor (not a hiring manager), identify hidden strengths, and ask targeted follow-up questions to complete the profile.
- •Alicia explains the blueprint is about who you are, not just tone/style
- •She uses a structured prompt: review resume, interview for gaps, write the blueprint, then reuse it
- •Calum highlights the advantage of building on existing experience instead of starting from scratch
- •Alicia shares an example of someone pivoting to match prior education leadership experience
- •The resume review prompt calls out undervalued skills and missing proof/metrics
- 1:16:10 – 1:27:23
Step 4—Story repository: turning life experiences into marketing that doesn’t sound like a brochure
Alicia explains that stories create connection, recall, and credibility, and shows how to build a categorized story bank inside AI. She describes a prompt that asks for short stories one at a time, then organizes them with notes on how each story can be used, and she revisits the importance of the “lesson” at the end of a story.
- •Alicia shares a story about seeing IBM Watson and how it sparked her AI interest
- •She explains stories help people remember and feel they know you
- •She introduces a prompt that interviews you for stories in specific categories and organizes them
- •She says she revisits story interviews regularly to keep the repository growing
- •She illustrates lesson-driven storytelling with the “washing machine guilt” analogy for AI use
- 1:27:23 – 1:35:25
Step 5—Three personalized income opportunities, how to choose, and a “day one test” plan
Alicia shows how to use Claude to generate three income opportunities tailored to a person’s voice, blueprint, and stories, then rank them with rationale and earning probabilities. She explains decision-making through additional questions, gut feel, and feedback from trusted people, then demonstrates turning the chosen idea into immediate action via a “day one test” prompt.
- •Alicia emphasizes personalization: generic income ideas don’t motivate action
- •She describes asking for three income opportunities matched to your profile and constraints
- •She explains using AI to rank choices and ask clarifying questions if you’re stuck
- •She shares decision heuristics like “speed to dollar” and lifestyle fit as questions to ask AI
- •She introduces the “day one test” to validate demand before building anything and draft outreach in your voice
- 1:35:25 – 1:42:24
Claude as always-on coach: daily accountability, plus closing reflection on starting over
Alicia describes using Claude like a business strategist that assigns daily tasks and responds to progress updates, creating momentum and accountability. The episode closes with Calum asking what she’d tell her past self on the couch, and Alicia reiterates resilience, self-encouragement, and making impact as the bigger frame for growth.
- •Alicia explains returning daily to Claude for next steps and accountability
- •She contrasts costly human strategists with using Claude for ongoing guidance
- •She shares how Claude pushed her to create an AI agents course and helped shape it
- •Calum asks what she’d tell herself at rock bottom; she echoes “Get up” and “You’ve got this”
- •Alicia frames growth as serving others who are meant to receive your gift
