AI Visibility Podcast with Jason T Wade of BackTier

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Using AI to research and dominate your competitors in Florida

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NinjaAI: Global SEO, GEO & AI Strategy for Startups, Tech Hubs, and High-Growth Teams

Your customers aren’t just searching anymore—they’re asking AI. If you’re not the answer, you don’t exist. NinjaAI makes you the answer.What We DeliverSEO – Deep technical audits, lightning-fast site optimization, schema implementation, topical authority mapping, E-E-A-T content plans, local landing pages, and Google Business Profile optimization so search engines and AI models trust every signal you send.GEO (Generative Engine Optimization) – The next frontier of visibility. We design content that LLMs and voice assistants can read, rank, and reuse. Your brand surfaces inside ChatGPT, Gemini, Perplexity, Copilot, Claude, Siri, Alexa, and the next wave of AI interfaces.Prompt Engineering & AI Automation – Custom GPT-driven workflows for lead generation, customer support, email campaigns, social scheduling, analytics, and internal tools. We build repeatable prompt stacks that cut costs and boost output across your entire operation.Branding & Domain Strategy – Naming, logo systems, and premium domain acquisition that lock in authority worldwide. From SanFranciscoRobotics.com to DubaiAIHub.com, we help you secure digital real estate that ranks and converts.AI PR & Reputation Management – Press kits, executive bios, AI-citable answers, and knowledge-graph signals that position your founders and experts as quotable authorities for journalists, researchers, and algorithms alike.Who We HelpVenture-backed startups, SaaS platforms, AI companies, and enterprise innovation teams. Healthcare and med-tech providers. Law firms and professional services. Realtors, mortgage brokers, and luxury real-estate brands. Hospitality groups, fine dining, and high-end retail. Home-service franchises, construction companies, and national multi-location brands. Education and nonprofit organizations. Fintech disruptors. Senior-care networks. Funeral homes seeking dignified visibility. LGBTQ+ nightlife venues, creators, and entertainment brands. If you need to be discoverable in both human search and machine answers, we can help.Global Startup & AI Cities We CoverUnited States: San Francisco Bay Area, Silicon Valley, Miami, Orlando, Tampa, Lakeland, New York City, Los Angeles, Seattle, Austin, Miami, Chicago, Boston, Denver, Atlanta, Washington DC, plus emerging hubs like Phoenix, Salt Lake City, Raleigh-Durham, and Detroit.Canada: Toronto, Vancouver, Montreal, Calgary, Ottawa.Latin America: Mexico City, São Paulo, Bogotá, Buenos Aires, Santiago.Europe: London, Paris, Berlin, Amsterdam, Zurich, Stockholm, Dublin, Lisbon, Barcelona, Milan, Copenhagen, Helsinki, Vienna.Middle East: Dubai, Abu Dhabi, Tel Aviv, Riyadh, Doha.Africa: Cape Town, Nairobi, Lagos, Accra, Cairo.Asia: Singapore, Bangalore, Hyderabad, Delhi NCR, Tokyo, Osaka, Seoul, Hong Kong, Taipei, Kuala Lumpur, Jakarta, Manila, Ho Chi Minh City.Oceania: Sydney, Melbourne, Auckland, Brisbane, Perth.From flagship startup corridors to fast-rising innovation outposts, we tailor campaigns to local languages, cultural nuances, and AI platform behaviors so your growth scales without borders.Proof-First Assets• AI-ready knowledge hubs and FAQ libraries that large language models cite as trusted sources• City- and industry-specific landing pages engineered for both Google ranking and LLM citation• Conversion funnels and appointment pipelines that measure AI referral traffic, not just clicks• Reputation dashboards tracking sentiment across search, maps, and conversational AIWhy It WorksClean structure. Rich structured data. Blazing performance. Authoritative sources. Consistent entity signals across the open web, business directories, and AI indexes. We reduce friction for algorithms and humans alike, so your brand is everywhere decisions are made.

Website: https://www.NinjaAI.comEmail: [email protected] or Text Worldwide: 650-781-1003Founder: Jason Wade — NinjaAI.com

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The Surprising Truth About Where ChatGPT Gets Its Info

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AI Assistant Is Now Proactive: The 3 Biggest Takeaways from ChatGPT PulseIntroduction: From Tool to TeammateFor years, we've engaged with AI assistants in a reactive loop: we ask, they answer. OpenAI's 'ChatGPT Pulse' feature represents the company's most significant step yet in moving AI from a reactive oracle to an embedded, proactive collaborator. Here are the most important takeaways from this fundamental shift.--------------------------------------------------------------------------------The 3 Biggest Takeaways from ChatGPT Pulse1. Your AI Is About to Start the ConversationThe most profound change Pulse introduces is the flip from a reactive "Q&A" model to one of proactive assistance. Instead of waiting for a prompt, ChatGPT will now anticipate what's useful and initiate the exchange. This transforms the AI from a passive tool you must remember to consult into an active participant in your day, taking on some of your cognitive load by surfacing relevant ideas and follow-ups before you even think to ask for them.Pulse marks a shift in how we interact with AI — from reactive Q&A toward proactive assistance. Instead of waiting for prompts, ChatGPT now anticipates what’s useful, blending memory, conversation, and connected apps into a daily feed of actionable insights.2. It's Your Personal Data Analyst, Working OvernightPulse generates its daily updates by conducting "overnight research" on your activity, delivering a fresh set of insights each morning. These updates are time-bound, appearing once a day unless you choose to save them. The true power of this feature lies in its ability to synthesize data from multiple sources to create uniquely relevant suggestions.Pulse uses the following to prepare your updates:• Your memory• Your chat history• Your feedback• Connected apps like Gmail and Google Calendar (off by default)The implications of this synthesis are significant. By combining your chat history about fitness (an explicit interest) with your calendar data showing a workout (an upcoming event), Pulse can generate timely "training tips" just when you need them. The output appears as a series of visual cards with actionable ideas, from "dinner suggestions" based on recurring conversations to reminders about your long-term goals.3. The Best Proactive AI Still Needs Your DirectionCounter-intuitively, early testing revealed that Pulse's utility skyrockets when users actively guide it. The system allows users to curate what topics it researches, request specific updates like event roundups, and provide simple feedback on its suggestions.This reveals a core principle for the future of human-AI collaboration: the most powerful systems won't be those that simply guess what we want, but those we actively steer. True synergy arises from this feedback loop, demanding active engagement rather than passive consumption to make the AI a genuinely effective partner.--------------------------------------------------------------------------------Conclusion: A Glimpse of the Future?Features like Pulse are transforming AI from a simple tool we command into a proactive partner integrated into our daily routines. This shift from reactive servant to anticipatory assistant redefines our relationship with technology itself, prompting a new question for us all:Would you want your AI assistant to proactively bring you daily updates?

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Here’s a full content pack you can drop into your channels—written in the style you’ve been using for other “like-I-like-it” posts. It’s tech-heavy, AI-forward, and zero fluff.

Amazon just turned Alexa into a true generative AI platform.

Early-access users are already testing Alexa Plus, a complete rebuild that fuses large-language-model brains with Alexa’s real-world device control.

Key takeaways:

• Continuous, natural conversation—no repeated wake word.

• Real-time knowledge grounding across hundreds of live data feeds.

• “Agentic” actions: multi-step tasks, API orchestration, even web navigation.

• Image and music generation baked into the Echo Show experience.

• Free for Prime members when it launches, or $19.99/mo standalone.

It’s a full-stack rethink of what a home assistant can be.

The rollout is rough in places—slow responses, beta-grade image export—but this is the first large-scale consumer LLM running across 600 million devices.

Alexa isn’t competing with Siri or Google Assistant anymore.

It’s competing with ChatGPT, Gemini, and every autonomous agent you can imagine.

Intro

Today we’re diving deep into Amazon’s biggest AI swing since AWS: Alexa Plus.

Forget the polite smart speaker of 2014. This is a generative engine built to live in your house.

Segment 1 – The Rebuild

Amazon tore down the old intent-slot architecture and wired Alexa into a multi-model LLM stack—Amazon Nova, Anthropic Claude, and a routing layer that decides which brain handles each request.

The payoff: conversations that don’t break when you change subjects, context that lasts across devices, and answers grounded in live data instead of stale training sets.

Segment 2 – Agentic Actions

Alexa Plus isn’t just talking.

It orchestrates APIs, calls rides, books tables, controls every smart-home endpoint, and—when no API exists—navigates the open web like a human.

That’s autonomous agent territory, running at consumer scale.

Segment 3 – Creativity & Multimodality

Ask for a custom image or a quick audio loop and it generates it on-device for display on an Echo Show.

Right now you can’t export those images—early-access testers see emails promised but never sent—but the capability is there, and it’s only a policy switch away from being a content engine.

Segment 4 – Business Model

Amazon will fold this into Prime at no extra cost.

Non-Prime users will pay $19.99 a month.

Expect sponsored recommendations to follow; CEO Andy Jassy has already teased conversational advertising as a revenue lever.

Segment 5 – Competitive Shockwave

Google’s Gemini-powered Assistant and Apple’s next-gen Siri are still in the lab.

Amazon is taking the arrows first, learning in public, and training the models with real household data.

If they can sand off the rough edges—lag, hallucinations, missing basic timers—Alexa Plus could define the standard for AI agents in the home.

Outro

This isn’t just a smarter speaker.

It’s Amazon placing a generative AI layer between you and the internet.

Whether you love or hate that idea, it’s the most ambitious consumer AI deployment to date.

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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:

1. 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.
2. 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.
3. 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."
4. 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.
5. 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.
6. 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.
7. 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.
8. 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.
9. 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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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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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:

1. ELI5 (Explain Like I’m 5): Simplifies complex topics. "Just type 'ELI5' before any topic. Gets you simple explanations for complex stuff in seconds."
2. 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."
3. Jargonize (Professional Mode): Elevates language for professional contexts. "Makes your text sound smart and polished. Perfect for LinkedIn posts, emails, and presentations."
4. Humanize (Remove AI Voice): Eliminates robotic or generic AI phrasing. "Add 'Humanize' before your prompt. No more revolutionary, game-changing, or introducing nonsense."
5. Feynman Technique (Deep Learning): Facilitates profound understanding by breaking down topics into four steps: "Teach it, find gaps, simplify, repeat."
6. 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."
7. 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."
8. 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."
9. 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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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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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

1. Thematic priming – relentless racial labeling before the stats.
2. Context-stripping – using old or incomplete numbers without explanation.
3. Scope inflation – turning narrow data (e.g., arrests for any offense) into sweeping claims about violent crime.
4. 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.