From LinkedIn posts about abandoned pilots to earnings calls where CEOs walking back their AI promises on earnings calls, are we witnessing the crash after the hype? Welcome to the Trough of Disillusionment – and it might be the best thing that's happened to AI.

This isn't the death of artificial intelligence. It's the birth of something sustainable. While media headlines turn negative and executives panic, the real builders are taking advantage of the chaos. History doesn't repeat, but it rhymes: today's AI trough is creating the similar opportunity for businesses willing to play the long game as the Dot Com boom.

This episode unpacks why the AI trough is an opportunity: it's a chance to build with intention rather than FOMO. We discuss the five tactical strategies that will position you to dominate when AI reaches its next growth phase, and why the current trust crisis requires a fundamental reset in how we communicate AI's capabilities.

We also cover the shocking Pew Research poll showing Americans deeply distrust tech companies to develop AI responsibly, Claude's new "Skills" infrastructure that enables specialized tool use, and the hilariously practical AI that solves our collective avocado ripeness problem. Plus, we break down the $290,000 Deloitte Australia scandal where AI hallucinations in a government report led to fabricated court cases and non-existent academic papers - an important reminder of why the human oversight layer still matters.

• The strategies that separate AI winners from losers during the trough
• How the same pattern that made Amazon dominant after the dot-com crash is happening now
• The growing employee revolt against force-fed AI tools and how to overcome it
• Why trust has become the critical competitive advantage in AI implementation
• How Pinterest and Spotify are fighting back against the flood of AI-generated content

00:00 What We've Been Up to This Week

04:27 The Backlash Against AI Slop Begins

06:59 Welcome to the Trough of Disillusionment

07:31 The Trough of Disillusionment: There's the Opportunity

10:05 The Gartner Hype Cycle: A Lord of the Rings Journey Through Technology

17:33 Dot-Com Déjà Vu: The Tech Crash Pattern Playing Out in AI

22:53 Skepticism in AI: When Promises Meet Reality

27:58 User Adoption vs. Hype: The Reality of AI Usage

30:06 The Trough of Disillusionment: Opportunities for Growth

31:57 Understanding AI's Real Capabilities and Limitations

33:53 The Importance of Those Little Wins in AI Implementation

37:15 The AI Trust Crisis: Consumer Skepticism and Expectations

44:05 Trust Repair: Building Credibility in AI Solutions

44:58 How to benefit from the Trough

45:08 Tactical Strategies: Navigating the Trough of Disillusionment

49:36 AI News of the Week: Pew Research, Avocado AI and Claude Skills

55:02 AI Gone Wrong: Deloitte's Very Bad Week

58:40 That's a Wrap: Where to Find Us 

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The internet is drowning in AI-generated garbage. From Meta's Vibes to Sora 2 to that clearly ChatGPT generated email from your colleague, businesses are losing an estimated $9 million per 1,000 employees annually to low-quality AI content cluttering their workflows.

This isn't about AI being bad. It's about automation without oversight creating an exponential pollution problem that's actively degrading the internet, tanking workplace productivity, and threatening the future training data for AI models themselves. There's no doubt that AI can generate impressive content. The quality of text, images, and video from tools like Sora is legitimately good. So why is there so much slop? Because quality isn't the problem - quality control is. When humans skip the editing, validation, and value-add steps, you get automation without judgment. And at scale, that's disastrous.

Welcome to the world of "AI Slop" - and if you're using AI tools (which you should be), you need to understand the difference between AI-assisted content that adds value and automated garbage that's making everything worse. We also cover a major update from Google that flew under the radar (and why it matters for AEO), a major AI scam running on Spotify, and (even more) big news from OpenAI's Dev Day 2025.

What You'll Learn:

• What AI Slop actually is (and why it's different from good AI-assisted content)
• Why Google killed the 100-result URL parameter and what it reveals about bot traffic
• The $9 million problem: How WorkSlop is destroying productivity at scale
• Model collapse explained: Why AI trained on AI content degrades into uselessness
• Sora 2's explosive launch and the TikTok-ification of synthetic media
• Why AI detection tools don't work (and the 6,000 students falsely accused)

Timestamps:

00:00 Intro: Poker Face & Getting Our Heads Straight

06:05 AEO Update: Google Just Changed Everything (Again)

16:04 AI Slop Defined

19:53 Sora 2 & Meta's Vibes: The TikTok-ification of Synthetic Media

24:17 Three Ways AI Slop is Breaking the Internet (And Future AI Models)

30:02 WorkSlop: The $9 Million Productivity Black Hole

37:55 The Anti-Slop Framework

44:29 AI News of the Week: OpenAI Dev Day Breakdown

48:03 AI News: What AI Hardware Could Look Like

49:31 AI News: Democratizing AI Fine-Tuning for SMBs

53:23 AI Gone Wrong: When 6,000 Students Got Falsely Accused by Turnitin

57:27 Key Takeaways: Navigating AI Challenges

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OpenAI just launched "Buy It in ChatGPT" - letting users complete purchases without ever visiting your website. If you're not cited by AI engines, you don't just lose traffic. You lose the entire sale.

This is Part 2 of our Answer Engine Optimization series. Last week we covered why AEO matters. This week, we're getting tactical. We break down the exact three-pillar framework you need: on-site optimization, off-site citation building, and measurement strategies that actually work.

The timing couldn't be more urgent. With OpenAI's September 29th announcement, conversational commerce is here. Etsy sellers can now sell directly in ChatGPT. Shopify merchants are next. The entire discovery-to-purchase journey happens in one AI conversation - and if you're not in that conversation, you're invisible.

What You'll Learn:

• The complete 3-pillar AEO framework (On-Site, Off-Site, Measurement)
• Question mining: How to find the 50-100 queries your customers actually ask
• Reddit strategy that works (without getting banned for spam)
• Why YouTube is a goldmine for B2B AEO
• How to track AEO when Google Analytics can't see AI traffic

We also cover Claude's new Slack integration, Sora 2's TikTok-style feed, and why AI-generated music almost broke Spotify's royalty system.

Timestamps:

• 00:00 - Intro: Welcome to Spooky Szn
• 03:25 - Why AEO is No Longer Optional
• 04:04 - Quick Recap: How AI Search Changed Everything (In Case You Missed Part 1)
• 06:57 - The Three Pillars of AEO: Your Complete Framework
• 08:43 - Pillar 1: On-Site Optimization - Making Your Website AI-Readable
• 20:27 - Pillar 2: Off-Site Citation Building - Getting Mentioned Where It Matters
• 22:14 - The Great SEO to AEO Shift: Why Smaller Brands Can Finally Win
• 24:14 - Where to Build Citations: Reddit, YouTube & Beyond
• 31:50 - Pillar 3: Measurement & Tracking - Proving This Actually Works
• 32:07 - Free Tools to Track Your AEO Performance (Yes, Really Free)
• 34:43 - BREAKING: OpenAI Launches "Buy It in ChatGPT" - The Game Just Changed
• 40:15 - The New Rules: What Actually Works in 2025
• 41:29 - Three Frameworks Every Business Needs Right Now
• 44:00 - AI News: Sora 2 Launches & The TikTok-ification of AI
• 47:21 - Claude in Slack: Your Meeting Notes Just Got Smarter
• 49:11 - AI Gone Wrong: How 75 Million Fake Songs Almost Broke Spotify
• 54:17 - Your 4-Step Action Plan (Start Today, Not Tomorrow)
• 56:20 - Wrap Up: Find Us on Social & Keep Iterating

Key Resources:

• https://linktr.ee/early_adoptr

The businesses that master AEO in 2025 will have an unfair advantage. The ones that wait will be fighting for scraps. Which side do you want to be on?

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60% of searches now end without a click. If you're still optimizing for Google rankings while your competitors are getting cited by ChatGPT, you're already behind.

Answer Engine Optimization (AEO) - also known as GEO, GSO, AIO - is fundamentally changing how customers find businesses. Instead of ranking #1 on Google, you need to become the source that AI engines quote when someone asks a question. This isn't SEO 2.0. It's a complete shift in how search works.

What You'll Learn:

• Why "getting cited" matters more than "ranking high"
• How LLMs actually decides which sources to quote
• Target's billion-dollar bet on generative commerce (and what it means for small businesses)
• WHY you need to learn how to optimize for answer engines

Kyle and Jess break down the technical mechanics behind AI search, explain why conversational queries are killing traditional keywords, and reveal the end game: purchases happening entirely inside ChatGPT with zero website visits.

We also cover this week's AI infrastructure news - OpenAI's massive Nvidia partnership and what it means for tool accessibility - plus product launches from Amazon, Google, and Perplexity that signal where commerce is heading.

Part 1 of 2. Next week, we get tactical with implementation frameworks, measurement strategies, and specific actions you can take immediately.

Timestamps:

00:00 What We've Been Up To This Week

04:05 What is AEO and How is it Different from SEO?

08:20 The Zero-Click Era: How AI Answers Killed the Blue Link

12:36 Getting Cited, Not Ranked: The New Rules of Visibility

17:17 Behind the Algorithm: What Makes Content Citation-Worthy to AI

21:22 Spam Meets AI: Why Answer Engines Are About to Get Messy

22:13 How People Actually Talk to AI (And Why It Matters for Your Business)

23:14 The Shift to Conversational Search

26:22 Why AI Search Skips the Top of Your Funnel

28:59 Target vs. Traditional SEO: A Real-World AEO Success Story

32:32 The End Game: When Purchases Happen Inside ChatGPT

34:55 If You're Not in the AI Conversation, You're Invisible

36:04 Tracking the Untrackable: Measuring Citation Optimization

40:03 AI News: The OpenAI-Nvidia Partnership & What It Means for You

44:43 AI News of the Week: Amazon Seller Assistant, Google AI Summaries & Perplexity's Email Agent

49:10 AI Gone Wrong: How ChatGPT Leaked Private Email Data

53:37 Key Takeaways: Why Early AEO Adopters Will Dominate

57:35 Wrap Up & Next Week's Tactical Deep Dive

The search landscape is shifting faster than most businesses realize. Share this with founders and marketers who need to understand where traffic is actually coming from now.

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In this episode, Kyle and Jess tackle the elephant in the room that's sabotaging AI implementations everywhere: AI hallucinations. If you've ever wondered why ChatGPT confidently tells you complete nonsense, or why that "perfect" AI-generated content turned into a business nightmare, this episode breaks down exactly what's happening under the hood and gives you tips and strategies to help minimise the risk of hallucinations.

We also cover YouTube's new AI creator tools, a new movie studio lawsuits, how people are actually using ChatGPT, Italy's groundbreaking AI legislation, and Meta's spectacular demo failure where they accidentally crashed their own presentation.

Key Takeaways:

• The Confidence Trap: AI models are trained to always give answers, even when they should say "I don't know" - leading to authoritative-sounding fiction
• Chain-of-Thought Prompting: Force AI to show its work by asking for step-by-step reasoning instead of direct answers
• RAG Implementation: Feed AI specific documents instead of relying on training data to eliminate fake citations and statistics
• The 5-Day Safety Plan: Risk-assess your current AI usage, rewrite high-stakes prompts, and build verification workflows before disasters strike

Glossary:

• AI Hallucination: When AI confidently generates false information, statistics, or citations that sound authoritative but are completely fabricated
• Chain-of-Thought Prompting: Asking AI to explain its reasoning step-by-step rather than jumping to conclusions, dramatically reducing errors
• RAG (Retrieval-Augmented Generation): Providing AI with specific documents to reference instead of relying on potentially outdated training data
• Confidence Scoring: Advanced prompting technique where you ask AI to rate its certainty about answers on a 1-10 scale

Get in touch with Early Adoptr: [email protected]

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In this episode, Kyle and Jess continue their deep dive with data veteran Eric Callahan, exposing the truth about how most businesses actually handle their data. It's messier than you think, and that's exactly why your AI initiatives keep failing.

Eric talks us through his "duct tape analogy" - a brutally honest take on why most companies are digital Frankensteins, we get real about technical debt, data hygiene nightmares, and why throwing AI at bad data is like putting premium gas in a broken engine.

We also discuss how Spotify turned simple data modeling into viral marketing gold with Spotify Wrapped, why Albania just made history by swearing in an AI advisor, and we have an update to our Anthropic lawsuit settlement from last week.

What You'll Learn:

• Identify Your Duct Tape: Manual processes, constant Excel exports, and "no human should do this" tasks are screaming red flags for technical debt that'll sabotage AI adoption
• The John Smith Problem: Data hygiene isn't sexy, but when John Smith, J. Smith, and John S. are all the same person in your system, your AI will confidently give you wrong answers
• AI Reality Check: Stop believing the magic. AI is just another output layer - if your data foundation is garbage, your AI insights will be too
• The Spotify Strategy: Their Wrapped phenomenon wasn't AI wizardry, it was smart data architecture from day one.

Chapters:

00:00 Intro, Tube Strikes, Furniture Nightmares, Camping and Missing Fingers

03:10 You Have More Data Than You Think: Destroying the "Not Enough Data" Myth

07:26 Welcome back to Eric Callahan

08:44 Why Many Businesses are Held Together with Digital Duct Tape (And How Netflix Avoided This)

13:32 Red Flags: How to Spot When Your Data Processes Are Actually Broken

16:08 Why Your Data is Messier Than You Realize

26:02 Sorry to Break It to You: AI is Not Magic (And Why That's Actually Good News)

29:04 The Secret Behind Spotify Wrapped: How Good Data Creates Viral Moments

34:12 Your Homework: Create a "Wrapped" Experience That'll Make Customers Obsessed

35:53 McDonald's AI Gone Rogue: The Job Application Bot That Exposed Everyone's Data

39:52 Why Spotify Can Launch AI Features Overnight (While Your Competitors Can't)

42:39 AI News of the Week: Anthropic's Settlement Rejected & Albanian AI Advisor

50:03 Your 5-Minute Data Audit: Find the Duct Tape Before It Breaks Everything

53:43 Wrapping Up for the Week!

Glossary:

• Technical Debt: Quick-fix solutions that create bigger problems later - like using duct tape to "temporarily" fix something that becomes permanent
• Data Hygiene: Keeping data clean and consistent (ensuring John Smith doesn't appear as five different people in your database)
• Shift Left: Fixing problems at the source instead of constantly patching them downstream
• Data Observability: Monitoring systems that catch data problems before they break your dashboards (or AI models)

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In this episode of Early Adoptr, Kyle and Jess bust the biggest AI myth of all: “We don’t have enough data.” The truth? You’re already swimming in it. From emails to spreadsheets to customer conversations, every business has more than enough information to start building AI-powered insights.

To prove it, they bring in data veteran Eric, who’s spent 15+ years cleaning up messy data for startups and enterprises alike. Eric shares why even Fortune 500s are struggling with data quality, how to avoid AI hype traps, and why the “hot dog story” from his kid’s PTA is the perfect metaphor for every business owner’s data nightmare.

You’ll also hear why dashboards break, what “Shift Left” really means, and how to spot untapped data hiding in plain sight. Plus: the latest AI news, from Anthropic’s billion-dollar copyright settlement to sneaky YouTube AI editing.

04:00 Welcoming Eric Callahan

07:52 Why Everyone’s Data Is a Mess and Why It Matters for AI

14:10 Big Data, AI FOMO, and Business Reality Checks

20:28 Emails, Funnels, and the Hidden Data You Already Own

23:56 The Hot Dog Example: Data Engineering for Normal People

32:42 Shift Left: Fixing Data Quality Before It Breaks Everything

42:21 Following the Data Trail: How Actions Become Insights

44:55 Key Takeaways to Make Your Data Work for AI

47:30 AI News of the Week: Anthropic Lawsuit & Data Training, ChatGPT's Parental Controls

59:43 AI Gone Wrong: YouTube Changes Content Without Consent

• Why every business already has enough data for AI (and what to do with it).
• The parallels between today’s AI hype and yesterday’s “big data” craze.
• How emails, customer interactions, and even paper forms can turn into insights.
• Why dashboards keep breaking and how “Shift Left” thinking fixes it.
• The single most important question to ask before you start collecting any data.

• BI (Business Intelligence): Dashboards and reports that turn raw numbers into insights for decision-makers.
• Data Contracts: Agreements between developers and data teams that keep data clean, consistent, and usable.
• Data Lifecycle / Funnel Tracking: Following each step a customer or user takes — from first click to final purchase.
• Event Tracking: Recording specific user actions (like opens, clicks, or purchases) so you can measure and optimize behavior.

Get in touch: [email protected]

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In this episode, Kyle and Jess tackle the biggest myth in AI adoption: "I don't have enough data." The reality? Every business owner is sitting on a goldmine of untapped information - they just don't know how to see it or use it. From email threads to customer support tickets to that "Bible spreadsheet" you've been updating for three years, your business is generating incredible amounts of data every single day.

We break down the three types of data your business already has, explain how to actually extract it from your systems without needing a computer science degree, and show you exactly what AI does with all that information once it gets its hands on it. Plus, we dive into the practical side - how connecting different data types can finally prove whether your marketing campaigns actually drive sales, not just open rates.

Whether you're drowning in spreadsheets, wondering how to make sense of years of customer emails, or ready to turn your data into your competitive advantage, this episode will change how you think about every business interaction you have.

What You'll Learn:

• The three types of data every business has (and why 80% of yours is probably untapped)
• How to extract data from your systems: CSV exports vs APIs vs the new Model Context Protocol (MCP)
• The four-step process AI uses to transform messy business information into actionable insights
• Why combining operational data with customer experience data reveals game-changing patterns

Data Terminology:

API (Application Programming Interface) - A documented doorway that lets different software systems ask each other for specific information securely and automatically.

CSV (Comma Separated Values) - A simple file format that stores data in rows and columns, like a basic spreadsheet that any system can read.

ETL (Extract, Transform, Load) - The three-step process of pulling data from different places, cleaning it up, and putting it somewhere useful.

JSON - A standardized format that systems use to exchange data in a way that's both human-readable and computer-friendly.

LLM (Large Language Model) - AI systems like ChatGPT or Claude that can read, understand, and generate human-like text.

Mapping - The process of matching data fields from one system to another (like making sure "email" doesn't end up in the "first name" column).

MCP (Model Context Protocol) - An emerging standard that lets AI systems directly access live data from your business tools without manual exports.

Metadata - "Data about data" - information like timestamps, file sizes, who created something, or when it was last modified.

RAG (Retrieval-Augmented Generation) - How AI finds the right pieces of information from a large collection of documents to answer your questions.

Semi-Structured Data - Information that has some organization but isn't perfectly clean - like that messy spreadsheet with random notes and merged cells.

Structured Data - Information organized in neat rows and columns, like your CRM records or accounting software.

Unstructured Data - Messy information like emails, documents, audio recordings, and PDFs that doesn't fit into neat categories.

Webhooks - Event triggers that automatically push data from one system to another when something specific happens (like a new customer signup).

If this episode helped you realize you're not data-poor after all, share it with another business owner still thinking they need enterprise-level infrastructure to compete with AI.

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In this episode, Kyle and Jess are completely overhaul their most popular episode on prompt engineering, because GPT-5 isn't just faster than GPT-4 - it's fundamentally different. It follows instructions with "surgical precision," handles 800 pages of context at once, and will get confused if you give it contradictory prompts that older models would just ignore. Not to mention there's been a whole host of other updates to prompt engineering that deserve attention too.

We dive deep into why role prompting (like "act as a marketing expert") is largely ineffective according to new research, introduce the game-changing 4C Framework that actually works with GPT-5's precision, and show you how few-shot prompting can boost accuracy from 0% to 90%.

Plus, we break down the shocking MIT report showing 95% of AI pilots are failing - and why that's actually great news for small businesses.

Whether you're frustrated with generic AI responses, wondering why your old prompts don't work as well anymore, or ready to master the communication skills that'll give you a massive competitive advantage, this episode is your roadmap to prompt engineering mastery in 2025.

What You'll Learn:

• Why GPT-5's "surgical precision" requires completely different prompting strategies than GPT-4
• The 4C Framework: Clear, Context, Constraints, and Calibration for consistent AI results
• Why "act like an expert" prompts fail and how few-shot examples boost accuracy by 90%
• How to leverage GPT-5's massive 400,000 token context window for complex business analysis
• The difference between GPT-5's fast mode and deep reasoning mode - and when to use each
• Real business scenarios: analyzing sales data, communicating project delays, and investor presentations

00:00 Intro and a special surprise from CatBus

05:00 Revisiting Prompt Engineering - Why It Matters

08:16 Prompt Engineering: How Is GPT-5 Different?

17:53 Updating Commonly Held Beliefs About Prompt Engineering

19:58 WTF is Shot Prompting and How Does It Help Write Better Prompts?

21:45 Why You Need to Prioritze Your Context in Prompt Engineering

22:36 Decomposition and Self-Criticism in Prompt Engineering

25:32 Introducing the 4Cs Framework (+P) of Prompt Engineering

34:23 Applying the 4C Framework in the Real World

42:34 AI News of the Week: Bad News for Enterprise AI Projects

50:19 AI Gone Wrong: A Cautionary Tale

56:09 Quick Wins for Effective Prompt Engineering: Updated

If this episode helped you finally get consistent results from ChatGPT-5, share it with other business owners still struggling with generic AI responses.

Get in touch with Early Adoptr: [email protected]

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In this episode, Kyle and Jess get hands-on with Small Language Models (SLMs) - showing you exactly how to set up your own private AI that runs entirely on your laptop. If last week was the "why," this week is all about the "how," complete with real business use cases and a step-by-step quick win you can try today.

We dive deep into LM Studio, walk through analyzing business documents without sending data to the cloud, and show you how to create content that actually sounds like you wrote it. Plus, we cover ChatGPT's inevitable move to ads, Perplexity's audacious Chrome acquisition bid, and Meta's absolutely inexcusable chatbot scandal that every parent needs to know about.

Whether you're tired of hitting Claude's rate limits, worried about data privacy, or just want an AI assistant that doesn't cost you per query, this episode gives you the practical roadmap to your own local AI setup.

What You'll Learn:

* How to set up LM Studio and download your first small language model in 15 minutes

* Why local AI might actually be faster and more reliable than cloud-based solutions

* The privacy advantages that make SLMs perfect for sensitive business data

* Why ChatGPT's upcoming ads change everything for business decision-making

* Critical red-teaming questions every business owner should ask before launching AI features

Chapters:

00:00 Intro, What We've Been Up to This Week and Chipmunk Cheeks

09:01 Recap: WTF are Small Language Models (SLMs)?

13:16 What Are the Benefits of a Small Language Model for a Small Business?

16:54 Small Language Models: Real World Use Cases

31:17 Comparing Small Language Models to Large Language Models

37:29 Jargon Busting: Why You Should Consider a SLM

40:10 AI News of the Week: ChatGPT Ads, Perplexity Wants to Buy Chrome, Illinois & AI Therapy Regulation

48:14 AI Gone Wrong: Meta's "How Did This Get Approved?" Moment

53:33 Quick Wins: How to Build a Local AI

Get in touch with Early Adoptr: [email protected]

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If this episode convinced you to try building your own local AI, share it with other business owners who are tired of AI subscriptions and rate limits.

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