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/⁠