AI Visibility - SEO, GEO, AEO, Vibe Coding and all things AI

Jason Wade, Founder NinjaAI

NinjaAI.com 🎙️ AI Visibility Podcast by NinjaAI helps you with SEO, AEO, GEO, PR & branding. HQ in Lakeland Florida & serving businesses everywhere, NinjaAI uses search everywhere optimization (SEO), generative engine optimization (GEO), AI prompt engineering, branding , domains & AI PR. Learn how to boost your AI Visibility to get found in ChatGPT, Claude, Grok, Perplexity, etc. and dominate online search. From startups to law firms, we help you scale and win Jason Wade Phone/WhatsApp: 1-321-946-5569 Jason@NinjaAI.com WeChat: NinjaAI_ Teams: ThingsPro.com

  1. قبل يوم واحد

    The Trillion-Dollar AI Funding Gap

    NinjaAI.com This briefing document summarizes the key themes and important facts surrounding the immense capital expenditure in AI compute infrastructure, drawing from the provided excerpts of "The Trillion-Dollar AI Funding Gap." Main Themes: Unprecedented Capital Expenditure: The AI industry, particularly "AI hyperscalers," is embarking on one of the largest capital expenditure cycles in modern history, driven by the compute-intensive nature of AI models.Significant Funding Gap: Despite Big Tech's substantial planned investments, there's a projected $1.5 trillion funding gap for AI data centers through 2029.Reliance on Debt Financing: Debt financing is rapidly becoming the primary method to bridge this funding gap, with private capital firms actively competing to provide loans.Emerging Risks and Concerns: Industry watchers are raising alarms about potential issues such as overcapacity, long-term profitability, energy demands, and rapid obsolescence of data center infrastructure.Most Important Ideas/Facts: Staggering Projected Spending: Morgan Stanley analysts project "AI ‘hyperscalers’" to spend $2.9 trillion on data centers through to 2029. This highlights the unprecedented scale of investment.Major Funding Shortfall: While Big Tech is expected to contribute approximately $1.4 trillion, a $1.5 trillion funding gap remains. This gap underscores the need for alternative financing mechanisms.Drivers of the Spending Spree: The primary reason for this massive investment is the "compute-hungry" nature of AI models, which "requires exponentially more processing power than traditional cloud services." The pursuit of "superintelligent AI" makes falling behind "not an option for the big tech players."Individual Project Scale: Major AI initiatives like Meta's "Prometheus," xAI's "Colossus," and OpenAI's "Stargate" each represent "$100B+ investments in next-gen supercomputing power." This illustrates the individual scale of these ambitious projects.Accelerated Near-Term Investment: Google, Amazon, Microsoft, and Meta are collectively preparing to spend "over $400B on data centers in 2026 alone," indicating an intensification of investment in the very near future.Debt as the Preferred Solution: "Debt financing is emerging as the preferred solution." The amount of loans going into data center projects is rapidly increasing, with "$60B of loans... roughly $440B of data center projects this year — twice as much debt as in 2024." This demonstrates a clear shift towards leveraging debt.Aggressive Competition Among Private Capital: Private capital firms such as Blackstone, Apollo, and KKR are "competing aggressively to drum up cash for AI companies." This suggests a robust appetite from the financial sector to participate in this investment wave.Example of Debt Financing: Meta recently secured "$29B ($26B in debt) to fund data centers in Ohio and Louisiana," providing a concrete example of a major tech company utilizing significant debt for AI infrastructure.Key Concerns Raised by Industry Watchers: Concerns are mounting regarding "overcapacity, long-term profitability, and energy demands." A significant risk highlighted is that "data centers may become obsolete far quicker than we think, requiring new investment that decreases returns for owners or forces them to sell at a discount." These concerns point to potential instability or challenges in the long-term viability of these investments.

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    Microsoft Excel COPILOT Function

    NinjaAI.com I. Executive Summary Microsoft is introducing a new COPILOT function in Excel for Windows and Mac, a significant advancement that integrates large language model (LLM) capabilities directly into the spreadsheet grid. This function allows users to leverage AI for data analysis, content generation, and workflow automation using natural language prompts. Key benefits include automatic result updates with data changes, seamless integration with existing Excel formulas, and a focus on user data privacy. The COPILOT function is designed to streamline time-consuming tasks like data wrangling, summarization, categorization, and brainstorming, making AI accessible for everyday data analytics use cases. II. Main Themes and Most Important Ideas/Facts A. Core Functionality and Integration AI within the Grid: The COPILOT function brings "the power of large language models directly into the grid," allowing users to generate AI-powered results by entering "a natural language prompt" and referencing cell values.Automatic Updates: A critical feature is that "every time your data changes, your results automatically update, too. No need to re-run scripts or refresh add-ins." This ensures analyses are "always current and relevant."Seamless Integration with Existing Formulas: The COPILOT function "works naturally alongside your existing Excel functions," meaning it "can be used inside formulas like IF, SWITCH, LAMBDA, or WRAPROWS," or its results can be used in other formulas.Simple Syntax: The function uses the syntax =COPILOT(prompt_part1, [context1], [prompt_part2], [context2], ...) where Prompt_part is text describing the task and Context (optional) is a cell or range reference providing data for the AI model.B. Use Cases and Scenarios The COPILOT function aims to "save time and supercharge your workflows" by addressing common pain points: Spur Ideas: Facilitates "brainstorming directly in the Excel grid" for tasks like generating SEO keywords, rewriting messaging, or changing tone.Generate Summaries: Can "distill large data ranges or long passages into concise narratives, highlight trends, or produce plain language explanations for complex calculations."Classify Data: Enables categorization of "text data, such as customer feedback, support tickets, or survey responses, right in your spreadsheet," eliminating the need to export data to other tools for tagging or sentiment analysis.Create Lists or Tables: Generates "multi-row, multi-column outputs that spill directly into the grid" for quick dataset creation, industry examples, or project plan outlines.C. Data Privacy and Limitations Data Confidentiality: Microsoft explicitly states, "Your data sent through the COPILOT function is never used to train or improve the AI models. The information you input remains confidential and is used solely to generate your requested output."Current Data Access Limitations: The function "uses data available within the large language model itself, meaning it cannot directly access live web data or internal business documents." Users must "first import that data into your workbook, and then reference it directly." However, "Support for live web data and internal business documents will be added in the future."Validation Required: Outputs "should be reviewed and validated for accuracy, especially for critical business decisions or reports."Usage Limits: Currently supports "100 calls every 10 minutes, and up to 300 calls per hour." To optimize usage, "consider passing arrays – a single call that includes a larger range of data counts only as one usage." These limits "will expand over time."

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    Unlocking ChatGPT's Hidden Potential

    NinjaAI.com I. Executive Summary This briefing document summarizes key insights from Hamza M.'s article "99% of Users Don’t Know About These 10 ChatGPT Secret Codes," which posits that most ChatGPT users only leverage a fraction of its capabilities. The article introduces "secret codes"—human-readable prompts, phrases, and structures—that act as "verbal cheat codes" to unlock advanced functionalities, mimicking personalities, extending memory, and influencing core system logic without requiring technical expertise. These codes aim to transform ChatGPT from a basic Q&A machine into a versatile "shapeshifting genius" or "creative partner," enhancing content creation, learning, and productivity. II. Main Themes and Core Arguments The central theme of the article is that ChatGPT possesses significant untapped potential accessible through specific, non-technical prompting techniques. Hamza M. asserts that "99% of Users Don’t Know About These 10 ChatGPT Secret Codes," highlighting a gap between common usage and the AI's full capabilities. The article argues that these "secret codes" are not complex programming syntax but rather "crafted prompt[s] that triggers a very specific type of output," allowing users to "influence these systems, without needing a computer science degree." Key arguments include: ChatGPT is more than a simple chatbot: It's described as a "shapeshifting genius" that "only reveals its full potential when you ask the right way."Accessibility of advanced features: Users don't need to be coders or prompt engineers to access these advanced features; "curiosity and a willingness to experiment" are sufficient.Transformative impact on user experience: The codes are presented as "superpowers in disguise," capable of making "ChatGPT feel less like a chatbot and more like a creative partner."Empowerment of the average user: By learning these codes, users can move into the "1%" who understand and utilize ChatGPT's deeper functionalities.III. Most Important Ideas/Facts (The "Secret Codes") The article outlines ten specific "secret codes" designed to dramatically enhance ChatGPT's utility across various applications: ELI5 (Explain Like I’m 5): Simplifies complex topics. "Just type 'ELI5' before any topic. Gets you simple explanations for complex stuff in seconds."TL;DR (Too Long; Didn’t Read) — Instant Summaries: Condenses long texts. "Copy and paste any long text after TLDR. Boom. Perfect summary without the fluff."Jargonize (Professional Mode): Elevates language for professional contexts. "Makes your text sound smart and polished. Perfect for LinkedIn posts, emails, and presentations."Humanize (Remove AI Voice): Eliminates robotic or generic AI phrasing. "Add 'Humanize' before your prompt. No more revolutionary, game-changing, or introducing nonsense."Feynman Technique (Deep Learning): Facilitates profound understanding by breaking down topics into four steps: "Teach it, find gaps, simplify, repeat."Socratic Method (Interactive Learning): Transforms ChatGPT into a personal tutor by asking questions first, then teaching based on user answers. "Say Teach me about [topic] using the Socratic method. It asks you questions first, then teaches based on your answers."Rewrite Like [Specific Person]: Allows for highly customized content tone and style. Examples include "Rewrite like a sarcastic Redditor" or "Rewrite like Alex Hormozi and Steve Jobs."Inverse Prompt (Reverse Engineering): Helps users understand what prompts generated existing content. "Paste it and ask, 'What prompt would generate this response?' Perfect for studying viral posts and great copy."Temperature Control (Creativity Levels): Manages the AI's output creativity vs. precision. Users can "Respond with high creativity for bold ideas" or "Respond with low randomness for precise answers."

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    Strategic Multi-Tool AI Workflows for Marketers

    NinjaAI.com Excerpts from "Combining AI Tools for Better Results : Social Media Examiner" by Michael Stelzner (July 22, 2025) This briefing document summarizes key strategies and insights from Michael Stelzner's "Combining AI Tools for Better Results" article, co-created with Grace Leung, focusing on optimizing AI usage for marketing. The central theme revolves around a strategic, multi-model approach to AI, moving beyond reliance on a single tool like ChatGPT to leverage the distinct strengths of various platforms for enhanced performance and capabilities. 1. The Imperative of a Multi-Tool AI Approach Key Idea: Limiting oneself to a single AI tool, even a popular one like ChatGPT, means "missing significant opportunities for enhanced performance and capabilities." Different AI models are trained differently, leading to fundamentally distinct capabilities and strengths. ChatGPT: Primarily a text-based model.Gemini: Trained multimodally from inception, capable of handling "text, images, audio, and video inputs simultaneously." Excels at data-heavy analysis and deep research.Claude: Focused on "linguistic sophistication and human-centered ethical considerations." Strong for refined analysis, strategic interpretation, and storytelling.Perplexity: Excels at rapid, real-time internet research with cited sources.NotebookLM: Unique for internal research, focusing exclusively on user-provided sources, minimizing hallucination, and maximizing relevance.Actionable Insight: Marketers need "hands-on experience to determine which tools work best for your recurring tasks" rather than relying solely on general recommendations. 2. Establishing a Strategic Framework for Multi-Tool AI Workflows Key Idea: A structured approach is essential for testing and implementing multiple AI platforms effectively. a. AI Tool Testing Framework: Identify Recurring Tasks: Focus on "your most frequent, recurring tasks" (e.g., strategy development, content creation, data analysis, research).Standardized Prompts: Design a "standardized prompt that you can test across different models simultaneously" for objective comparison.Evaluation Factors:Speed: How quickly each tool responds.Accuracy: Checking for "bias or inaccurate information."Tone and Presentation Style: Significant variations exist.b. Testing for Bias and Accuracy: Factual Questions: Ask questions about topics you know well to "spot potential biases or knowledge gaps."Brand/Industry Overviews: Request a comprehensive overview of your brand or industry to see "which model provides the most accurate and complete information."Chain-of-Thought Analysis: Examine the model's "reasoning process" to understand how it breaks down complex tasks.c. Mapping Workflows: Strategic Leverage: After testing, "map each part of your systematic workflow to the tool that strategically leverages these capabilities."Example Workflow: "Perplexity for initial research, Gemini for data-heavy analysis, and Claude for refined strategic thinking and presentation."Goal: Develop a clear understanding of "which tool serves each part of your process most effectively and then build repeatable workflows around these insights."

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    NotebookLM Analysis - Charlie Kirk: Crime, Immigration, and the Decline of the West - Last Podcast

    NinjaAI.com Source: The Charlie Kirk Show Episode "What Does "Phillies Karen" Say About American Men? https://www.charliekirk.com/podcasts/what-does-phillies-karen-say-about-american-men TL;DR The memo shows how Charlie Kirk’s podcast used repetition and bad statistics to push a false race–crime narrative. Core points The words “black/blacks” appear 23 times, nearly all tied to crime, priming listeners to link Blackness with criminality.Major stats are wrong or warped: Disinformation tactics Thematic priming – relentless racial labeling before the stats.Context-stripping – using old or incomplete numbers without explanation.Scope inflation – turning narrow data (e.g., arrests for any offense) into sweeping claims about violent crime.Outright fabrication – inventing figures like the “one in 22” claim. Bottom line The podcast builds an emotional story first, then props it up with distorted or invented statistics to inflame racial bias and create a false picture of crime in the U.S. Summary From NotebookLM: The source material consists of "The Charlie Kirk Show" podcast, featuring host Charlie Kirk and guests Trish Mclofflin and Jack Posobiec, which primarily focuses on conservative political commentary and current events. Key topics addressed include Operation Midway Blitz, a Department of Homeland Security (DHS) action targeting illegal immigrants in sanctuary cities like Chicago, with Mclofflin emphasizing the focus on violent criminals and criticizing local politicians for non-cooperation. Posobiec and Kirk further discuss a viral incident involving a woman dubbed "Phillies Karen" at a baseball game, interpreting the father's yielding of a baseball to the woman as a metaphor for the "decline of the West" and the surrender of men in society. Finally, the show analyzes several violent crime incidents, using FBI data to assert a high rate of black-on-white violent crime and criticize media coverage, calling for tougher law enforcement measures against criminals and uncooperative city officials. Use of "Black": Based on the sources provided, the words "black" or "blacks" appear 23 times in the transcript of Charlie Kirk's podcast. The sources state that almost all of these mentions are connected to claims about crime.  Here is a breakdown of how the term was used: • One mention was used to introduce a segment about “the ever-increasing amount of black crime”. • One mention referred to football player Caleb Williams and “his black nails”. • Two mentions described alleged killers as “another black criminal” and “a random black criminal”. • Nineteen mentions were part of claims about crime statistics. Examples of the crime-related claims include: • Democrats “pretend to care about black Americans”. • Statements about “white on black crimes” and “black-on-white crimes”. • An assertion that “black Americans would benefit most from a war on crime”. • The repeated claim that “one in 22 black men will be a murderer in their lifetime”. • A claim that in Minnesota, “blacks are 6.4 % of the population and yet blacks account for 62 % of all the violent crime”. • Assertions that “most of black crime” and “half of all black murders” are unsolved. • The statement that “half of all black males have been arrested by 23”. • A claim that “three times as many blacks commit crimes against whites despite being only 13 % of the population”. It is important to note that the sources emphasize that while the transcript accurately recorded these claims, the claims themselves are described as "unverified and largely inaccurate," "wrong or unsupported," and "exaggerated or false" when compared against official data from the FBI, Bureau of Justice Statistics (BJS), and other agencies. For instance, no credible study supports the "one in 22 black men will be a murderer" claim, and the statistic about arrests of Black men by age 23 is a misrepresentation of a 2014 study that included minor offenses.

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    Reddit Threads as a Hidden SEO Engine

    NinjaAI.com Welcome to today’s episode. We’re unpacking a live experiment that turned a handful of Reddit posts into a long-term SEO engine. This isn’t marketing theory. It’s a real test that multiplied search impressions twelve-fold and tripled click-through rates in just three months. Setting the Stage When I started, my site was getting roughly twelve hundred Google impressions a month and a click-through rate of about two and a half percent. It was steady but flat. Instead of cranking out more blog posts or chasing backlinks, I looked for a place where people were already asking the questions I could answer. That place was Reddit. Finding the Right Communities I skipped the giant noisy subreddits like r/Entrepreneur. Instead I went to smaller, focused communities—r/SideProject, r/Wordpress, and r/EntrepreneurRideAlong. These are niche back alleys of the internet where thoughtful conversations survive longer and competition for attention is lighter. Crafting the Content Each thread followed a simple but deliberate formula. The title read like a real search query, the kind someone would actually type into Google. The body delivered genuine help in clear language. I added examples and only linked when a link truly solved the problem. No bait. No sales pitch. The Engagement Loop After posting, I stayed active in the comments. Every reply refreshed the page with new text, and Google treats that as fresh content. The more the conversation grew, the more frequently Google crawled the page. That activity pushed the thread higher in search results and attracted more readers who left more comments. The cycle fed itself. Results Three months later the numbers told the story. Google impressions jumped from twelve hundred to twelve thousand per month. Click-through rate climbed from two and a half percent to eight percent. Reddit engagement rose from about fifteen total upvotes and comments to around one hundred and twenty. Referral traffic back to my site increased twelve times over. Why It Works Reddit threads are hybrids: part community forum, part evergreen article. Engagement inside Reddit pushes a post to the top of the subreddit. Google notices the sustained activity and rewards it with better placement. Higher placement drives more searchers to the thread, which triggers more comments, which keeps the post fresh. It’s a classic positive feedback loop. Key Takeaways Smaller subreddits often outperform the giant ones for qualified traffic. A keyword-rich title functions like a powerful H1 tag. Comment engagement acts as free on-page SEO. And authenticity is the non-negotiable ingredient. Spam dies quickly; genuine help compounds. Scaling the Method If you want to replicate the playbook, start by mapping niche communities in your field. Write answers that stand alone as mini-guides. Revisit your posts to reply to new comments. Track each thread in Google Search Console so you can see which ones develop a long tail of traffic. It takes consistency in the beginning, but each successful thread becomes an SEO micro-asset that pays dividends for months or even years. Closing Reddit is more than a social platform. It’s an overlooked publishing network hiding in plain sight. Treat every post like a flagship article and it will send you compounding traffic long after the initial wave of upvotes fades.

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    How YouTube's AI Will Change Everything!

    NinjaAI.com At its "Made on YouTube" 2025 event, YouTube unveiled a sweeping series of updates fundamentally transforming its platform through the deep integration of Artificial Intelligence. The announcements center on three core pillars: empowering creators with advanced generative AI tools, expanding monetization opportunities through innovative brand partnership models, and deepening community engagement across YouTube Music and Live. This strategy is underscored by the platform's significant economic impact, having paid out over $100 billion to creators, artists, and media companies in the last four years. Key takeaways include the rollout of Google DeepMind's Veo 3 video generator in Shorts, the introduction of the Lyria 2 music AI model, a comprehensive overhaul of the YouTube Live experience, and new dynamic sponsorship formats designed to increase creator revenue. These initiatives aim to streamline the creative process, forge stronger connections between creators and their audiences, and solidify YouTube's position as a dominant force in the creator economy. "We didn't just create a platform. We built an economy." — Neal Mohan, Chief Executive Officer, YouTube 1. The Integration of Generative AI into the YouTube Ecosystem YouTube is positioning AI as a core creative partner for its users. The new suite of tools, leveraging Google DeepMind's most advanced models, is designed to lower the barrier to entry for creation, streamline complex workflows, and enhance content accessibility while implementing new safety measures. AI-Powered Content Creation A suite of new generative AI tools enables users to create video and music content directly within the YouTube platform.

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    An AI-Powered Content Workflow

    NinjaAI.com This document outlines a five-step, AI-powered content workflow developed by Kyle Atwater and his team. The system is designed to streamline and scale marketing efforts by managing the entire content lifecycle, from initial keyword research to post-publication optimization. Managed through a front-end interface built in Monday.com, the workflow integrates directly with WordPress for content delivery. The process begins with automated keyword clustering and proceeds through outline generation, drafting, and publishing, culminating in a final phase dedicated to Search Engine Optimization (SEO) and Conversion Rate Optimization (CRO). The stated philosophy behind the system is to provide a "no-BS" approach to scalable and effective marketing. Overview of the Workflow The AI-powered content workflow is a structured process created to systematize blog content production. Developed by Kyle Atwater and his team, the system's explicit goal is to scale marketing that is effective, with a specific focus on SEO and CRO. The entire process is operated from a custom front-end interface built within the Monday.com platform. Core Components and Technology • Workflow Management Platform: The user-facing "front end" of the system is hosted in Monday.com, which serves as the central hub for initiating and managing the content creation steps. • AI-Driven Research and Generation: The workflow leverages artificial intelligence for key creative and analytical tasks, including keyword research, outline generation, and draft writing. • CMS Integration: The system features a direct integration with WordPress, allowing a completed draft to be pushed seamlessly to the content management system for publication. The Five-Step Process The workflow is broken down into five distinct and sequential steps, guiding the user from ideation to post-publication analysis. Step 1: Blog Outline Generation The process is initiated when a user submits a keyword into the Monday.com interface. It is also possible to begin the process without providing an initial keyword. This triggers a research phase designed to supply a foundational keyword strategy for the article. • Key Output: The system generates 3 clusters of 5 keywords each, forming the strategic basis for the blog outline. Step 2: Blog Draft Generation Following the creation of the outline, the system proceeds to generate a full draft of the blog post. The user interface includes prompts such as "Keep going" and "Almost there," suggesting an interactive or guided generation process. Step 3: Push Draft to WordPress Once the draft is finalized within the workflow tool, this step facilitates the direct transfer of the content to a WordPress site, streamlining the handoff from content creation to content management. Step 4: Publish Article This step represents the action of making the article live on the website after it has been pushed to WordPress. Step 5: SEO / CRO The final stage of the workflow is dedicated to the ongoing optimization of the published content. This phase focuses on activities related to Search Engine Optimization (SEO) and Conversion Rate Optimization (CRO) to maximize the article's performance and impact.

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NinjaAI.com 🎙️ AI Visibility Podcast by NinjaAI helps you with SEO, AEO, GEO, PR & branding. HQ in Lakeland Florida & serving businesses everywhere, NinjaAI uses search everywhere optimization (SEO), generative engine optimization (GEO), AI prompt engineering, branding , domains & AI PR. Learn how to boost your AI Visibility to get found in ChatGPT, Claude, Grok, Perplexity, etc. and dominate online search. From startups to law firms, we help you scale and win Jason Wade Phone/WhatsApp: 1-321-946-5569 Jason@NinjaAI.com WeChat: NinjaAI_ Teams: ThingsPro.com

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