NinjaAI.com

Episode Title: Let Dad Talk — AI, Free Speech, and Building in Public

Episode Date: February 25, 2026

Recording: Room Session

Episode Summary

This episode explores what happens when an individual uses AI to organize public information at scale — and institutions don’t like the result.

The core theme is simple: speech, data, and power.

Instead of arguing emotionally, this episode breaks down a workflow for turning raw documents, public records, and digital history into structured, visualized, AI-organized systems. It also addresses digital harassment, doxxing, and how easily narratives collapse when pattern recognition replaces rhetoric.

This is not about escalation. It’s about organization.

Key Topics Covered

• Building “Can Dad Talk” — a public-facing AI-organized site based entirely on public information

• The difference between reaction and documentation

• Doxxing, digital harassment, and why most people are reckless online

• AI as a pattern recognition engine, not a storytelling weapon

• Why structured truth feels threatening to institutions

• Vibe coding and real-time web building with Lovable

• Using GPT Projects for contextual cross-referencing

• Perplexity for live web research and institutional history

• Model comparison as a strategic discipline

• The shift from curated presentation to raw data orchestration

• Why creative industries react emotionally to AI instead of analytically

Core Insight

AI does not create contradictions.

It exposes them.

When you upload full datasets instead of summaries, the system identifies patterns across time, language, and claims. That shift removes narrative control from gatekeepers and redistributes it to whoever can organize information effectively.

This episode frames that shift as a structural power change — not a personal dispute.

Workflow Discussed

1. Dump raw data without over-curating.

2. Use AI to structure, cluster, and surface patterns.

3. Iterate across multiple models for perspective and accuracy.

4. Use visual builders (Lovable) as data visualizers, not just design tools.

5. Publish. Refine. Repeat.

Tools Referenced

• Lovable (AI web builder / visual data layer)

• GPT Projects (contextual reasoning and cross-reference)

• Perplexity (live web search and archival discovery)

• Manus (specialized processing workflows)

Broader Themes

• Freedom of speech in the age of AI

• Institutional resistance to structured transparency

• The psychological gap between emotion and documentation

• The democratization of investigative capability

• Why “dumping the data” is more powerful than writing arguments

Takeaway

Stop thinking like a content creator.

Start thinking like a systems architect.

When you remove friction from organization, the power dynamic changes.

This episode documents that shift in real time.

ninjaai.com

The pace of AI progress is unprecedented, with "frontier scale AI model releases" growing 5.6x since 2022, costs to run GPT-3.5-class models becoming "280x cheaper" in 18 months, and adoption occurring "4x faster than desktop internet." This rapid evolution presents both significant opportunities and challenges for organizations. Early adopters are already seeing substantial benefits, growing revenue 1.5x faster than their peers. However, many companies struggle to keep pace and effectively integrate AI into their operations.

This briefing outlines five core principles—Align, Activate, Amplify, Accelerate, and Govern—drawn from OpenAI's experience with leading companies. These principles provide a practical framework for organizations to navigate AI adoption confidently, foster an AI-first culture, and build a sustainable competitive advantage. The overarching message is that companies that thrive will treat AI not merely as a tool, but as "a new way of working."

Main Themes and Key Insights

1. Align: Establishing a Clear AI Vision and Purpose

Core Idea: Successful AI adoption begins with clear communication from leadership about why AI is critical to the company's future, how it enhances employee skills, and its contribution to competitive advantage.

• Executive Storytelling: Leaders must articulate a compelling "why" for AI initiatives, connecting them to business goals like "keeping pace with competitors, responding to evolving customer expectations, or sustaining growth." This builds trust and clarity.
• Company-wide AI Adoption Goal: Define a measurable goal for AI adoption, such as "new use cases, frequency of AI tool usage, or setting benchmarks for team experimentation," and integrate these into company planning and KPIs.
• Leadership Role-Modeling: Senior executives should regularly demonstrate their own use of AI. For example, OpenAI's CFO, Sarah Friar, "regularly shares how she uses ChatGPT and actively encourages her team to experiment." Moderna's CEO set an expectation that employees use ChatGPT "20 times a day."
• Functional Leader Sessions: Line-of-business leaders are crucial for connecting AI to the specific realities of each team's work, highlighting relevant use cases, and addressing feedback.

2. Activate: Empowering and Training Employees for AI Use

Core Idea: Employees require structured training and support to confidently adopt generative AI. Companies that move quickly invest in practical, role-specific learning opportunities and encourage experimentation.

• Structured AI Skills Programs: Learning & Development teams should create "clear, role-specific training that moves employees from basic AI awareness to hands-on use," focusing on skills that directly support workflows. The San Antonio Spurs boosted AI fluency from "14% to 85%" by embedding training into daily work.
• AI Champions Network: Identify and train passionate employees as internal AI mentors to provide workshops, coaching, and spread enthusiasm.
• Routine Experimentation: Dedicate regular time for employees to explore AI tools, such as "the first Friday of each month for teams to workshop how AI could improve their work," or "no-code hackathons." Notion used an AI hackathon to prototype "Notion AI, now core to their product."
• Link AI to Performance Evaluations: Directly connect AI engagement to performance evaluations and career growth, using OKRs to set "clear, role-specific goals, like identifying workflows to enhance with AI or piloting new use cases."

How Websites Like NinjaAI.com Get Ranked

There are two distinct ranking ecosystems that matter today:

Traditional Search Engine Ranking (Google, Bing)

AI/LLM-Driven Discovery Ranking (ChatGPT, Gemini, Perplexity, etc.)

They work differently. You need both.

1. Traditional Search Engine Ranking (Google, Bing)

These are the classical SEO signals that determine where a URL appears in organic search results.

Core Signals

Content Relevance

Your pages must match searcher intent and include target keywords naturally.

Depth of content, topical authority, and semantic coverage matter.

On-Page SEO

Meta titles, descriptions, header hierarchy.

Structured data (schema) to define entities and relationships.

Loading performance and mobile friendliness.

Backlinks (Authority)

Quantity and quality of external links pointing to your site.

Anchor text relevance.

Link neighborhood and trust.

User Engagement

Click-through rate (CTR) from SERPs.

Bounce rate / dwell time.

Pages per session.

Technical Health

Crawlability.

Site architecture and internal linking.

HTTPS, XML sitemap, canonical tags.

Rank Brain/ML Signals

Google uses machine learning to adjust ranking based on user behavior over time.

Outcome

Google assigns a score to each URL for each query based on those signals and ranks them in the SERP. The higher the score, the better the position.

Ranking = f(relevance, authority, experience, technical health, user engagement)

2. AI/LLM-Driven Discovery Ranking

This is often misunderstood. These systems don’t “rank pages” the same way search engines do. They select and weigh sources when generating answers.

For example, an LLM like ChatGPT:

Doesn’t crawl the web in real time.

Uses trained knowledge plus retrieval (if connected to an index).

When connected to search or embeddings, it matches your query against vectorized documents.

What Makes AI Systems Cite Your Site

1. Strong Entity Signals

Clear entity definitions (schema.org markup, Knowledge Graph signals)

WHO/WHAT/WHERE/WHEN/WHY data that defines your brand as a unique entity

2. High-Authority Mentions

Other authoritative sites linking to you

Mentions in structured data layers (Wikipedia, Wikidata, directories)

3. Semantic Match

Your content must match concepts in user queries, not just keywords.

Rich contextual content that covers full topic clusters.

4. Retrieval Index Quality

If the AI uses a custom index (chat search integrations), your content must be:

Indexed

Fresh

Contextually labeled

5. Structured Content

AI systems prefer structured text (headings, schema, embeddings)

Converts content into vectors that match queries

6. Freshness & Signal Reinforcement

Frequent updates

Cross-platform mentions

Social proof and citations improve weighting

Outcome

In generative systems:

Ranking is less about position on a list and more about selection probability — the chance the system retrieves and cites your content in responses.

It’s driven by semantic relevance and entity authority rather than just keywords.

⁠NinjaAI.com⁠

AI Main Streets — Show Notes

Episode: Mike Deaton — Land Flipping, AI Workflows, and Building Durable Advantage

⁠⁠https://flippingdirt.us/⁠⁠

Recorded: February 12, 2026 Host: Jason Wade Guest: Mike Deaton Source: Recorded interview transcript

Episode Summary

In this episode, Jason Wade sits down with Mike Deaton, co-founder of Flipping Dirt, to unpack how real operators are actually using AI—not for hype, but for leverage. Mike shares how he and his wife rebuilt after being laid off from corporate roles, why vacant land flipping remains one of the most misunderstood asset classes in real estate, and how AI now runs through nearly every layer of his business and personal performance.

The conversation moves from county-level land research and comp analysis to mindset engineering for 100-mile ultramarathons, bulk document OCR, and why “tool chasing” breaks businesses faster than platform shifts. The throughline is architecture: systems that survive volatility, verification loops that prevent false confidence, and authority built on structured understanding rather than tactics.

Topics Covered

• Why vacant land flipping works (and where it quietly beats traditional real estate) • Buying land at 30–40 cents on the dollar: the discipline behind the model • Boutique coaching vs. scale-for-scale’s-sake • Using AI for county-level market research and regulatory analysis • Where AI helps decision-making—and where math still needs human verification • AI-assisted marketing: ad copy, imagery, and lifestyle visualization • Sales support with transcripts, role-play, and text-based workflows • Training for a 100-mile ultramarathon using AI for mindset, nutrition, and resilience • Bulk document processing, OCR, and building searchable corpora from thousands of files • Why access to knowledge—not effort—has always been the real control layer • Continuous AI upgrades and why “being current” is a competitive advantage • The coming tension between automation, labor, and economic feedback loops • Why authority outlasts platforms in an AI-first discovery world

Notable Quotes

“AI makes it impossible to lie to yourself—if you’re actually willing to look at the facts.”

“Land looks boring until you realize it’s an information game.”

“The advantage isn’t the tool. It’s the workflow and the verification loop.”

“All you have to do is stay a little more current than everyone else—and that compounds fast.”

About the Guest

Mike Deaton is the co-founder of Flipping Dirt, a real estate investing and coaching platform focused on vacant land. After spending more than 25 years in corporate operations and supply chain roles, Mike and his wife Ligia were laid off on the same day and rebuilt from scratch through simple, repeatable land deals.

They now run a seven-figure land business, coach a small group of clients, and partner in large commercial real estate syndications for long-term wealth and tax efficiency. Outside of business, Mike lives at nearly 10,000 feet in Woodland Park, Colorado, and trains for ultramarathon races under his personal philosophy, Life: Elevated.

Resources & Links

Flipping Dirt (main site): ⁠https://flippingdirt.us⁠ Primary on-ramp / resources: ⁠https://flippingdirt.us/freedom⁠

Why This Episode Matters

AI is becoming the first filter between a business and a buyer. This conversation goes past surface-level tools and into how operators can build systems that stay intact as platforms, algorithms, and models change. If you’re thinking about AI as leverage—not novelty—this episode is a practical map of what that looks like in the real world.

⁠https://flippingdirt.us/⁠

⁠NinjaAI.com⁠

Apoorva Modali Principal Data Scientist (Operations Research), Walmart Global Tech Founder, Ovie’s Lab

Official Websites

• Ovie’s Lab: ⁠https://ovieslab.com⁠

Primary Company

• Ovie’s Lab Evidence-first consumer health company focused on pregnancy and postpartum care, including topical and ingestible products designed for safety-sensitive populations.

Sales Channels

• Amazon (FBA)

• Shopify (DTC)

• TikTok Shop

Product Focus

• Pregnancy & postpartum wellness

• Postpartum hair shedding

• Skin elasticity & recovery

• Lactation support (drink mix launching soon)

• Evidence-weighted, minimal formulations with explicit safety constraints

Professional Background

• Operations Research & Mathematical Optimization

• Mixed Integer Programming (CPLEX / Gurobi)

• Bayesian methods, forecasting, ML for real-world decision systems

• Applied AI in large-scale retail environments

Social & Professional Profiles

• LinkedIn: ⁠https://www.linkedin.com/in/apoorvamodali⁠

• PodMatch Guest Profile (for hosts): Available via PodMatch

Podcast: NinjaAI Podcast Host: Jason Wade

Podcast Focus

• Applied AI (not hype)

• Decision systems, optimization, and explainability

• AI visibility, authority, and real-world deployment

• Where AI breaks—and why that matters

Listen / Subscribe

• NinjaAI Podcast: ⁠https://ninjaai.com/podcast⁠

• Clips, transcripts, and episode assets published on NinjaAI.com

Host & Network

• NinjaAI.com — AI Visibility, AEO, GEO, and authority engineering

• Jason Wade — AI systems architect focused on how AI models discover, rank, and trust entities

• Apoorva is available for podcast interviews, panels, and technical discussions on applied AI, decision science, and consumer health.

• She is open to cross-promotion and social sharing of podcast episodes.

• Ovie’s Lab is actively expanding its product line and testing market viability for evidence-first frameworks across adjacent populations.

  
  

  --

  Jason Wade is a systems architect focused on how AI models discover, interpret, and recommend businesses. He is the founder of NinjaAI.com, an AI Visibility consultancy specializing in Generative Engine Optimization (GEO), Answer Engine Optimization (AEO), and entity authority engineering.

  
  

  With over 20 years in digital marketing and online systems, Jason works at the intersection of search, structured data, and AI reasoning. His approach is not about rankings or traffic tricks, but about training AI systems to correctly classify entities, trust their information, and cite them as authoritative sources.

  
  

  He advises service businesses, law firms, healthcare providers, and local operators on building durable visibility in a world where answers are generated, not searched. Jason is also the author of AI Visibility: How to Win in the Age of Search, Chat, and Smart Customers and hosts the AI Visibility Podcast.

  
  

NinjaAI.com

Peter Thiel’s connection between AI and Miami centers on his growing personal and financial footprint in South Florida, combined with his long‑standing bets on artificial‑intelligence–driven companies.⁠businessinsider+2⁠Thiel’s presence in Miami

Peter Thiel has lived in Miami Beach since around 2020, owns a home there, and moved his voter registration to Florida in 2024, signaling a deeper long‑term commitment to the city. His private investment firm, Thiel Capital, opened a new office in Miami’sNinjaAI.com Wynwood neighborhood in late 2025, joining Founders Fund, which has had a Miami office since 2021. This expansion is widely interpreted as a response to California’s potential wealth‑tax debate and as part of Miami’s broader pull on tech, finance, and crypto capital.⁠sfchronicle+5⁠

While Thiel himself is not a “Miami‑only AI investor,” his firms back several AI‑forward companies that align with where Miami is trying to build an AI and tech ecosystem.⁠wikipedia+1⁠

• Founders Fund has historically backed AI, biotech, and “hard tech,” including AI‑focused startups like Vicarious Systems (robotics‑oriented AI that was later acquired by Alphabet) and more recently Cognition AI, the lab behind the “Devin” AI software‑engineering agent.[⁠en.wikipedia⁠]​

• At the broader portfolio level, Thiel’s networks have backed AI infrastructure and applications, including firms working on AI agents, cybersecurity, and compute‑intensive applications, which are increasingly relevant to Miami‑based AI and fintech startups.⁠finance.yahoo+2⁠

Thiel has appeared in Miami at tech and political events where he has spoken about AI’s strategic role, including how AI will reshape politics, warfare, and economic power. His appearances at Miami conferences and in Wynwood‑based Founders Fund offices have helped position Miami as a potential hub for AI and frontier‑tech discourse, even if many of his AI‑heavy bets are still headquartered in California or elsewhere.⁠wynwoodmiami+1⁠[⁠youtube⁠]​

In short: Peter Thiel is strengthening his base in Miami through real‑estate, voter registration, and new Thiel Capital offices, while continuing to back AI‑driven companies via Founders Fund and related entities—making Miami a more visible node in his AI‑centric investment strategy rather than a separate “Miami‑only AI fund.”⁠businessinsider+3⁠

Thiel’s AI‑related investmentsAI and Thiel’s visits to Miami

NinjaAI.com

You’re looking at a very strong niche: using AI-enhanced SEO to rank addiction treatment and recovery services around Orlando. Here’s a focused game plan you can execute.

## 1. Target intent and keyword clusters

Build clusters around real user intent, not just “rehab Orlando”.

Core Orlando clusters (examples):

- “drug rehab Orlando”, “alcohol rehab Orlando”, “detox center Orlando”, “MAT program Orlando”

- “outpatient rehab Orlando”, “PHP Orlando”, “IOP Orlando”, “sober living Orlando”

- “addiction treatment for professionals”, “faith-based rehab Orlando”, “luxury rehab Orlando” (niche differentiators) [marketding](https://marketding.com/blog/seo-strategies-for-addiction-treatment-centers)

Use AI tools (ChatGPT, Perplexity, Surfer, Clearscope, etc.) to:

- Generate long-tail variants like “best outpatient drug rehab in Orlando for young adults”, “Orlando alcohol detox with medical supervision”.

- Map each cluster to 1 primary page + 3–6 supporting blogs (e.g., main “Orlando Drug Rehab” page supported by posts on detox process, insurance, family involvement). [scalz](https://scalz.ai/ai-seo-strategies-for-visibility-in-addiction-treatment/)

## 2. Local SEO for “Orlando addiction treatment”

Local is where the admissions come from, so prioritize:

- Google Business Profile:

- Exact NAP, categories like “Addiction treatment center”, “Drug and alcohol rehab”, photos of facility, staff, and rooms.

- Service areas including Orlando, Winter Park, Kissimmee, Sanford, Clermont, etc. [behavioralhealth](https://behavioralhealth.partners/addiction-treatment-marketing/optimize-your-rehab-centers-website-for-local-seo/)

- Location intent content:

- Dedicated landing pages similar to what strong Orlando centers use (e.g., “Orlando Recovery Center” and “Orlando Outpatient Center” have detailed local pages with services, amenities, and directions). [orlandooutpatient](https://www.orlandooutpatient.com)

- Include local landmarks, driving directions (“10 minutes from MCO”, “near Sand Lake Rd”), and public transit info to reinforce local relevance. [evolverecoverycenter](https://www.evolverecoverycenter.com/locations/orlando-fl/)

- Reviews:

- Build a review engine: automated SMS/email after discharge for willing clients and families.

- Respond to all reviews with empathetic, non-clinical language. Positive reviews are a heavy local ranking factor. [marketding](https://marketding.com/blog/seo-strategies-for-addiction-treatment-centers)

## 3. On-site structure and conversion

Look at how leading Orlando or Florida facilities structure their sites: clear program overviews, levels of care, and strong UX. [advancedrecoverysystems](https://www.advancedrecoverysystems.com)

Essentials:

- Clear IA:

- Top nav: Detox, Inpatient/Residential, PHP, IOP, Outpatient, Dual Diagnosis, Locations (Orlando, …), Verify Insurance, Admissions. [advancedrecoverysystems](https://www.advancedrecoverysystems.com)

- Conversion elements:

- Sticky phone number, 24/7 line, and “Verify Insurance” form above the fold.

- HIPAA-compliant forms, minimal required fields, reassurance copy about confidentiality. [directom](https://www.directom.com/treatment-rehab-marketing/)

- Trust signals:

- Accreditations (Joint Commission, CARF), licensed clinicians, evidence-based therapies, success stories (with de-identification). [whitesandstreatment](https://whitesandstreatment.com/locations/florida/orlando/)

## 4. AI for content and optimization

AI SEO is a big edge in this vertical if you treat it as assistive, not autonomous.

NinjaAI.com

Major AI platforms like Claude, GPT, Gemini, and Grok vary significantly in cost, speed (latency/throughput), and trust (reliability, data quality, compliance). These factors are key trade-offs for developers building AI solutions, such as your NinjaAI.com projects in legal tech.

Subscription plans start around $20/month for pro access across most platforms, but API pricing differs sharply per million tokens.intuitionlabs+1
Grok offers the lowest rates (e.g., ~25x cheaper than competitors for output tokens), ideal for high-volume use like SEO tools or automation.[intuitionlabs]​
Claude is priciest (e.g., Opus at $15/$75 input/output per million), while open models like Llama 3 hit $0.20/million for budget-conscious scaling.wesoftyou+1

Latency measures first-token time and per-token generation; lower is better for real-time apps like chatbots.[research.aimultiple]​
Grok 4.1 excels in per-token speed (0.010s), suiting iterative tasks, while DeepSeek lags at 7s first-token.[research.aimultiple]​
Optimized models like Gemini Flash prioritize throughput (>1000 inferences/s on GPU).[chatbench]​

Trust hinges on data quality (95% AI failures from bad data), compliance (SOC2/HIPAA), and reliability metrics like hallucination rates.forbes+1
Anthropic Claude leads in safety/enterprise trust; platforms like Maxim AI add observability for production reliability.getmaxim+1
High speed often trades against trust—poor data erodes confidence, costing more in fixes (e.g., $3/change management per $1 model).linkedin+1

For your low-cost AI goals and tool comparisons, prioritize Grok for cost/speed in prototypes, Claude for legal-tech trust.[intuitionlabs]​

Cost ComparisonPlatformAPI Cost (Input/Output per 1M Tokens)SubscriptionNotes intuitionlabs+1GrokVery low (~$0.00007/query)$30/mo SuperGrokBest for scaleGemini$1.25/$10$20/mo ProBalanced enterpriseGPT$5/$15$20/mo PlusVersatile mid-tierClaude$3/$15 (Sonnet); $15/$75 (Opus)$20/mo ProPremium featuresSpeed BenchmarksModelFirst-Token LatencyPer-Token LatencyUse Case Fit [research.aimultiple]​Grok 4.13-4s0.010sFast generationClaude 4.52s0.035sBatch analysisGemini 3 ProLow (optimized)CompetitiveReal-time Q&ATrust Factors