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.

We originally released this as a two-part series, but AEO is more urgent than ever. With Buy in ChatGPT and the rise of conversational commerce (see our previous episodes on this), the end game is clear: search → recommendation → checkout can happen without a single website visit.

That means you don’t just lose the top of funnel or the click, you lose the entire purchase.

That’s why we’re re-releasing both episodes together as one master AEO episode: the “why it matters” + the complete tactical framework.

Answer Engine Optimization (AEO) — also known as GEO / GSO / AIO — is changing how customers find businesses. Instead of fighting for the #1 blue link, you need to become the source AI engines quote when someone asks a question.

And now that purchases can happen inside ChatGPT? It's more important than ever. If you’re not in the AI answer, you don’t just lose traffic, you lose the sale.

• Why “getting cited” matters more than “ranking high” in the AI era
• How LLMs decide what to quote: retrieval, authority signals, and what actually gets surfaced
• Why the zero-click trend changes everything for marketing funnels
• What happens when the entire buyer journey happens inside an AI conversation

• The complete 3-pillar AEO framework: On-Site, Off-Site, Measurement
• Question mining: how to find the real queries your customers ask (sales calls, support tickets, forums, reviews)
• How to write content that’s clear, quotable, and AI-readable (without keyword-stuffing)
• Off-site citation strategy that works: Reddit (without getting banned), YouTube, publishers, original research
• How to measure AEO when Google Analytics can’t see it
• Free tools you can use right now to track visibility in AI answers

00:00 We're Off for the Holidays! 

03:11 SEO vs AEO (or GEO / AIO, etc etc) 

06:43 The Zero-Click Era: How AI Answers Killed the Blue Link

10:51 Getting Cited, Not Ranked: The New Rules of Visibility

15:26 Behind the Algorithm: What Makes Content Citation-Worthy to AI

19:20 Spam Meets AI: Why Answer Engines Are About to Get Messy 

19:51 How People Actually Talk to AI (And Why It Matters for Your Business)

20:52 The Shift to Conversational Search: How People Actually Talk to AI (And Why It Matters for Your Business)

23:54 Why AI Search Skips the Top of Your Funnel

26:27 Target vs. Traditional SEO: A Real-World AEO Success Story

29:52 The End Game: When Purchases Happen Inside ChatGPT

32:11 If You're Not in the AI Conversation, You're Invisible

33:17 Tracking the Untrackable: Measuring Citation Optimization

35:26 Let's Get Tactical! 

35:28 The Three Pillars of AEO: Your Complete Framework

35:38 Pillar 1: On-Site Optimization - Making Your Website AI-Readable

46:41 Pillar 2: Off-Site Citation Building - Getting Mentioned Where It Matters

48:25 The Great SEO to AEO Shift: Why Smaller Brands Can Finally Win

50:21 Where to Build Citations: Reddit, YouTube & Beyond

57:43 Pillar 3: Measurement & Tracking - Proving This Actually Works

57:58 Free Tools to Track Your AEO Performance (Yes, Really Free)

01:00:29 Three Frameworks Every Business Needs Right Now

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We’re off for the holidays, so this is a re-release + stitched double-episode focused on Small Language Models (SLMs) and how to actually use them.

Kyle and Jess get hands-on with SLMs, showing you how to set up a private, local AI that runs on your laptop, and why that can be a smarter move than relying on cloud models for everything. If one episode is the “why” (specialist vs. generalist, cost, privacy), the other is the “how” (LM Studio walkthrough, using your documents locally, and real business workflows you can copy today).

Whether you’re tired of hitting Claude rate limits, worried about privacy, or just want an AI assistant that doesn’t charge per query, this stitched re-release gives you a practical roadmap to going local.

• How to set up LM Studio and download your first small language model in ~15 minutes
• Why local AI can be faster, cheaper, and more reliable than cloud-based tools
• The privacy advantages of keeping sensitive business data off external servers
• Real-world SLM use cases: customer support, internal knowledge bases, content creation, email/sentiment analysis, and onboarding
• The trade-offs: where SLMs struggle and when you should still reach for an LLM
• Pitfalls to watch for: testing, edge cases, hallucinations, guardrails, and launching responsibly
• Quick wins you can try today to build your own “AI intern” that lives on your laptop

• Chatbase.co — https://www.chatbase.co/?via=early-adoptr (affiliate link: we may earn a small commission at no extra cost to you)
• Helpjuice — https://helpjuice.com/
• Slite — https://slite.com/
• LM Studio — https://lmstudio.ai/
• Ollama — https://ollama.com/

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We're off for the holidays so this is a re-release of one of our most popular episodes. Kyle and Jess 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%.

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

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 Prioritize 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

56:09 Quick Wins for Effective Prompt Engineering: Updated

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Last week, Disney announced a billion-dollar deal with OpenAI to license 200+ characters into Sora. Everyone's treating this as Hollywood drama, but what Disney actually did is show every IP-based company, whether you're a global studio or a solo creator, the exact playbook for navigating AI.

This week, we brought in our friend Rich Welsh to break it down from every angle at once. He's a Hollywood veteran, a startup founder, and now a VC investor. That combination means he's seen this industry from the creative side, the founder side, and the investment side, exactly the perspective you need to understand what's really happening here.

We also break down McDonald's Netherlands pulling an AI Christmas ad after three days of backlash. Seven weeks of work. Ten people. Still looked terrible. That story matters more than you think.

If you have any IP, whether that's characters, a brand, a body of work, or just ideas you care about protecting, this episode for you.

We cover:

* Why Disney licensed characters but carved out actor likenesses and voices

* The structure that makes this deal work (scope, exclusivity, equity, distribution)

* Why other studios and IP holders can't ignore this

* User-generated content as engagement strategy

* The guardrails problem: why they don't work, and what actually does

* Rights management becoming a real commercial product

00:00 Introduction and What We've Been Up To

05:48 The Disney-Sora Announcement That Changes Everything

08:08 Welcome Rich Welsh

11:23 The Disney-Sora Deal Breakdown

16:03 Why Disney Wants User-Generated Content

19:09 First-Mover Advantage

26:58 The Guardrails Problem

33:16 Why Licensing Deals Are Winning Over Lawsuits

43:04 Checking In on the Creative Community

48:13 Practical Advice for Founders

51:06 AI Gone Wrong

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Last week, we broke down what vibe coding is. This week, we break down the only question anyone actually cares about: “Okay… so what tool do I use?”

The vibe coding ecosystem has gone crazy in the last 12 months, and the amount of choice is overwhelming.

But here’s the real ones know: The tool matters way less than your structure....And that’s what this episode is really about. In this episode, we take you through a practical roadmap for building real software with AI, without sinking your project.

We walk through the full stack of vibe coding tools, when to use IDE-based systems vs no-code platforms, why Claude Code is becoming the power-user favorite, and how product managers, designers, and marketers are shipping functioning apps in days.

Plus: OpenAI hits a “code red,” Google catches up at an alarming pace, and Amazon face-plants with one of the worst AI dubs we’ve ever heard.

IDE Tools:

• Cursor
• Windsurf
• Claude Code
• Replit

No Code Tools:

• Lovable
• Bolt
• Supabase – backend hosting + auth
• Vercel – frontend deployment
• Figma – UI design tool feeding into vibe coding workflows
• MCP (Model Context Protocol) – Integrations

1. Vague objectives → Fix: a clear Project Overview markdown
2. Schema drift → Fix: lock your Data Model markdown)
3. No shared architecture → Fix: define an Architecture Guide markdown)
4. Inconsistent UI patterns → Fix: a UI Style Guide markdown)
5. Switching tools mid-build → Fix: one tool per build phase

CHAPTERS:

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

04:46 Recapping Vibe Coding

07:15 So… Which Vibe Coding Tool Do I Actually Use?

09:28 IDE-Based Tools vs. No-Code Platforms

12:14 IDE Tools - Cursor vs. Windsurf

14:16 IDE Tools - Claude Code

20:05 No-Code Platforms: Bridging the Gap for Non-Developers

27:34 Choosing the Right Tool for You

31:51 Vibe Coding Best Practices

38:42 Identifying Pitfalls in Vibe Coding

39:00 Quick Primer: What’s a Markdown File?

39:39 Pitfall 1: Be Clear About What You Want

40:54 Pitfall 2: Schema Drift

43:05 Pitfall 3: Shared Architecture

45:38 Pitfall 4: Inconsistent UI Patterns 

47:56 Pitfall 5: Pick One Tool & Stick With It

50:14 AI News of the Week: OpenAI's Code Red

54:49 AI Gone Wrong: Amazon's Dubbed Anime

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Everyone keeps saying AI is transforming software development, but the real story is how vibe coding is quietly rewriting who gets to build in the first place. Over the past year, AI coding tools have gone from cute party tricks to fully agentic systems that can read your entire codebase, plan out multi-step tasks, and scaffold an entire app from a single prompt.

But here’s the part that isn’t getting enough attention: vibe coding isn’t just about replacing developers. It’s changing what development is. And that shift is opening the door for founders, product managers, and designers to build software in ways that simply weren’t possible even 12 months ago.

In this episode, Jess and Kyle break down what vibe coding actually looks like behind the scenes...the moments where it works, the moments where everything breaks spectacularly, and the very real need for human oversight in a world where AI can hallucinate your backend just as confidently as it writes it.

This isn’t a hype episode about “AI building apps for you.” It’s a grounded look at how the workflow is evolving, what becomes defensible when everyone can build at the speed of thought, and how this shift is reshaping everything from MVP timelines to VC expectations.

If you’ve been watching vibe coding from the sidelines, this is your sign to try it yourself. Not because it replaces expertise, but because it expands who gets to use it.

• What vibe coding actually is — beyond “ChatGPT writes my code”
• Why larger context windows and agentic planning changed everything
• The human oversight problem: speed goes up, responsibility goes up too
• Why MVPs now take days, not months — and what that means for startups
• What’s actually defensible when UI becomes trivial to clone
• The looming challenge for junior developers (and how to navigate it)

Plus:

• OpenAI’s newest copyright trouble
• Deloitte’s second AI citation disaster
• Google’s Thanksgiving recipe chaos

TIMESTAMPS:

00:00 Thanksgiving Chaos

04:55 Kicking Off: What Is Vibe Coding Really?

08:43 Why “Vibes” Matter: The Core Idea Behind Vibe Coding

13:03 Inside a Vibe Coding Session: How It Actually Works

16:44 From Idea to App: Building Real Features With AI

18:20 The Human-in-the-Loop: Why Oversight Still Matters

21:10 Early Adventures in Vibe Coding: What Works and What Doesn't

22:44 How AI Coding Tools Leveled Up

25:24 Vibe Coding as a Paradigm Shift in Software Development

28:02 Can You Trust AI Code? 

30:05 What This Means for Startups: Speed, Costs & MVPs

34:49 Innovation for Everyone: How AI Lowers the Barrier to Building

38:42 The New Startup Landscape: Easier to Build, Harder to Defend

41:44 Will Vibe Coding Replace Traditional Devs?

45:03 Big Takeaways: The Future of Building With AI

46:25 AI News of the Week: Big Trouble for OpenAI

51:15 AI Gone Wrong: Deloitte’s Fake Citations & Google’s Burnt Turkey Recipes

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In part two of our deep dive into agentic commerce, Jess brings back her colleague Nathalie Lethbridge to answer the question every SMB is asking: How do I prepare for a world where AI agents are the middlemen between customers and products?

Last week, we explored the power struggle between Amazon's walled garden and OpenAI/Perplexity's open-web vision.

This week, we get tactical. Nathalie walks through the practical framework for making your e-commerce site "agent-legible" - helping your products become visible and accessible to AI agents that will soon be shopping on behalf of customers.

We cover structured data requirements, markdown optimization, compliance considerations, and why smaller businesses actually have an advantage over giants in this transition.

Plus, this week's AI news includes TikTok's new controls for AI-generated content and a fascinating Silicon Valley moment where 300+ AI insiders publicly named the startups they'd short.

00:39 - Intro: Cold Snaps, Desk Chairs & Thanksgiving Prep

04:02 - Guest Welcome: Nathalie Lethbridge on Conversational Commerce

07:15 - The Power Struggle for Discovery: Walled Gardens vs. Open Web

12:40 - The Two-Tier Internet Explained: Why AI Agents Need a Different Web

18:05 - From Scrolling to Structured Data: How Agents Actually Shop

24:30 - Making Your E-Commerce Site "Agent-Legible": The Practical Playbook

31:15 - Compliance, Legal & Liability in the Agentic Era

37:45 - The SMB Advantage: Why You Can Move Faster Than Amazon

43:20 - Quick Wins: Immediate Actions for Your Business Now

48:41 - AI in the News: TikTok's Push Back Against AI Slop

54:19 - AI Gone Wrong: Silicon Valley Eats Its Own

58:43 - Next Week: Vibe Coding (Teaser & Closing)

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The agentic shopping wars are here. And nobody's paying attention yet.

Amazon just sued Perplexity. Google just launched agentic shopping. OpenAI is rewriting how commerce works...all of it is aimed at the holiday season, and all of it means something fundamental about e-commerce is about to shift.

But here's what's getting lost in the headlines: this isn't just about the tech. It's a legal story about who owns your customer relationship, amd a business model story about who controls the data. It's a fork in the road for SMBs between two very different futures - one where they're dependent on platforms, and one where they might finally level the playing field.

The narrative says conversational commerce is the future of shopping. But what the platforms are really fighting about is whether you'll shop within their walls or whether you'll shop with an agent that can see everywhere. The winners won't be decided by technology. They'll be decided by who wins the lawsuit and who controls the infrastructure.

In this episode:

• What conversational commerce actually is (and why "shopping at the point of inspiration" changes everything)
• The Amazon vs. Perplexity lawsuit
• Why Amazon's walled garden strategy is essentially them behaving like a legacy media company
• Google's brilliant hedge: how they're playing both open and closed systems simultaneously
• Attribution, data ownership, and why SMBs have been losing money to platforms for 20 years
• Why merit-based product discovery could finally be possible again
• The legal and regulatory implications of this battle (feat. Nathalie's lawyer brain)
• What SMBs actually need to do to stay relevant across both walled gardens and open systems
• Plus: Yann LeCun leaving Meta, OpenAI finally fixing the em dash problem, and why AI performance reviews are creating a trust crisis

Timestamps:

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

07:53 Welcoming Nathalie to the podcast

09:36 Shopping at the Point of Inspiration: What Conversational Commerce Actually Means

13:52 When AI Becomes Your Personal Shopping Assistant

14:11 Why Now? The Timing of Conversational Commerce

15:34 The Death of Shopping Friction

18:00 Follow the Money: Who Profits When AI Owns the Customer Relationship

19:31 Amazon vs. Perplexity: The Lawsuit That Could Reshape Retail

24:56 Walled Gardens vs. Open Rails: The Fork in the Road for Commerce

31:27 How Products Get Discovered Without Paid Ads

37:23 Google's Hybrid Approach to Shopping

40:53 Understanding the Two-Tier Internet

45:04 Navigating the New Landscape for SMEs

48:26 AI News: Yann LeCun's Departure from Meta

51:47 AI News: OpenAI Finally Fixes the Em Dash

55:52 AI Gone Wrong: When JPMorgan Let AI Judge Your Performance

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The narrative is everywhere: AI is coming for your job. But here's what nobody's talking about. The data shows AI can't even complete 3% of professional tasks yet.

So why are companies laying people off left and right? In this episode, Jess and Kyle untangle the messiest contradiction in tech: the gap between what AI companies claim their systems can do and what actually happens when they hit real work.

This isn't a doomscroll episode about the end of work. We're looking at what's *actually* happening behind the headlines...the failed pilots, the rehiring sprees, the experts being paid record rates to train the AI systems that might replace them. And most importantly, it's about what *you* can do to position yourself for a world where AI is reshaping every role, not eliminating them.

AI isn't ready for complex professional work yet. But companies are behaving as if it is. The winners will be professionals and organizations that understand this gap and use it strategically - investing in people, not cutting them, and building workflows where AI handles the repeatable and humans handle the judgment calls.

If you're worried about AI taking your job, stop. Start worrying about how to work *with* it instead.

**In this episode:**

- The Mercor paradox: Why ex-surgeons and investment bankers are being paid premium rates to train their own replacement

- What the Remote Labor Index actually revealed

- Why Amazon is laying people off despite AI not being ready for prime time

- The incentive misalignment that's tanking AI pilots everywhere

- Why being "the person who verifies AI output" might be the most valuable skill in 2025

- Three actionable quick wins you can implement this week - whether you work for yourself or someone else

- What the Amazon Rufus vs. Perplexity Comet battle reveals about the future of commerce

- How a hockey coach used AI so badly it might explain why his team is 3-14

**Chapters:**

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

04:26 The Question Everyone's Asking: Will AI Take My Job?

08:06 The Paradox: Paying Professionals to Train Their Own Replacement

10:42 The CEO Narrative vs. Reality: Why Companies Lay Off Before AI Is Ready

12:56 The Remote Labor Index: What AI Actually Can't Do (Yet)

14:31 Layoffs Without the Capability to Back Them Up

21:53 When Good Intentions Meet Bad Incentives: Why AI Pilots Fail

22:07 The AI Gap: How Executives' Vision Clashes With Workers' Reality

23:32 How Successful Companies Actually Use AI

28:27 AI as an Advantage, Not Your Replacement

37:22 Building Your Personal AI Stack: The Tech Stack Every Professional Needs

39:40 The Irreplaceable Human: Why Judgment Still Beats Algorithms

41:12 AI News of the Week: Amazon's Rufus v Perplexity's Comet

47:30 AI Gone Wrong: When Sports Data Goes Bad

51:33 Quick Wins for the Week: AI Isn't Going Away - Here's Your Move

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Your entire e-commerce experience is about to change. OpenAI's "Buy with ChatGPT," introduced at the end of September, isn't just a shopping feature. It's the beginning of a complete overhaul of how digital commerce works.

For the past 20 years, discovery, curation, and transaction have been fragmented across platforms, but now all of that is collapsing into one place.

In this episode, Jess and Kyle break down what the two-tier internet actually is, why it won't replace human browsing (yes, you'll still shop for furniture), and why the old rules - "who can pay more" - are being replaced by new ones: "who's structured better." They also unpack why Walmart's participation signals a genuine tipping point for mainstream adoption, and what you need to do right now to prepare for a world where agents handle transactions instead of humans.

In this episode:

- What conversational commerce actually means (and why it's different now than before)

- The two-tier internet explained: Human browsing layer vs. agent transaction layer

- How "Buy with ChatGPT" works technically and where the friction actually is

- Why Walmart joining changes everything

- The agentic commerce protocol (ACP) and what "simple integration" really means

- Discoverability on ChatGPT: The algorithm nobody understands

- The privacy issues around Buy with ChatGPT

Chapters:

00:00 Welcome to the Victory Lap (Yes, the Dodgers Won)

04:23 Happy 25th Episode to Us!

06:30 Conversational Commerce: What It Actually Means (And Why It's Not New)

06:53 Buy with ChatGPT is Here (And Walmart Knows What's Up)

11:22 Introducing the Two-Tier Internet: Your New Reality

15:41 The Agent Internet (What's About to Happen)

16:06 Browsing vs. Buying: Two Different Worlds

20:54 When Agents Take Over Your Shopping Cart

24:01 Under the Hood: How Buy With ChatGPT Actually Works

33:29 The Privacy Trap: Your Entire Life, One Conversation

39:06 What Do You Need to Do (Starting Today)

40:26 The Attribution Problem Nobody's Talking About (But Should Be)

41:52 The Brand Story Problem: Humans Want Drama, Algorithms Want Data

42:22 AI News of the Week: Wharton's New Report & a Pregnant AI?!

47:20 The Real Bottleneck: It's Not the AI, It's Your Team

49:43 AI Gone Wrong: Grok Goes Wrong (Again)

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