SuperMarketers.ai: Your Roadmap to AI-Driven Marketing

While Stack Overflow, G2, and most B2B directories watched their organic traffic collapse under AI Overviews and LLMs, SoftwareFinder did the opposite. Adnan Malik, co-founder and CEO, grew organic traffic more than 300% over the last two years. Today, roughly 30% of their traffic comes through LLMs and AI Overviews, up from under 5% a couple years ago.

In this episode, Adnan breaks down the exact strategy shift that made it happen. We get into the semantic and JSON-level changes on the front end, the content map approach that pushes articles in both LLMs and traditional search, and why user-generated reviews became one of his biggest visibility drivers. If you publish content and want it consumed by AI, this is the tactical breakdown.

We also cover the part most SaaS founders get wrong: the human element. Adnan turned down VC money and built an AI system on the back end that recommends software in real time, while keeping human consultants on the phone. He explains why a fully autonomous AI agent failed in software discovery, and where the human touch still wins.

Finally, Adnan gives his prediction for the next 12 to 18 months. He expects 60-70% of searches to run through AI, a sharp compression in speed to demo and speed to close, and downward pressure on pricing as buyers get more sophisticated. If you sell software or buy it, this is the shift to prepare for now.

Five takeaways

• Prepare your content for LLMs before the traffic shifts, not after. SoftwareFinder moved early on semantic markup and LLM-friendly structure, which is why they captured AI demand instead of losing to it.
• Optimize at the passage level, not just the page. Surround your money pages with category, alternatives, and comparison content so you own the full cluster of buyer prompts.
• Reviews and user-generated content are AI fuel. LLMs favor authentic UGC, so a real review collection engine is now a visibility moat.
• Directory leads convert better than PPC because they match your true ICP. Lower churn and higher LTV justify paying the premium.
• AI handles initial discovery, humans close the complex decision. Keep a human in the loop for high-stakes B2B purchases where buyers need to open up.

00:37 Founding SoftwareFinder and 300% growth through the AI shift

01:48 The revenue model: pay per lead 

02:34 The AI strategy shift: semantic markup, content maps, and reviews 

07:42 How buyer behavior changed with LLMs as the initial search layer 

11:16 Turning down VC money and the AI-plus-human system 

14:38 Why directory leads beat PPC on ICP fit, churn, and LTV 

18:09 Prediction: 60-70% of search through AI and the speed-to-close shift

Learn more at https://softwarefinder.com/

Connect with Gen: www.linkedin.com/in/genfurukawa

Learn more at https://supermarketers.ai

Rand Fishkin ran 2,961 prompts across ChatGPT, Claude, and Google AI. 

Fewer than 1 in 100 produced the same brand list. In this conversation, he breaks down why the entire "AI ranking" category is built on probability, what actually moves your brand into LLM answers, and how he's marketing his new product AlertMouse with zero AI search visibility on day one.

Rand Fishkin is the cofounder and CEO of three companies: SparkToro, software that makes audience research accessible to everyone, indie game maker Snackbar Studio, and the the superior alternative to Google Alerts: Alertmouse.

What you'll learn:

→ Why AI answers are probabilistic, not deterministic, and what to track instead of rankings

→ The two real mechanisms that influence LLM visibility (RAG and training data)

→ How user prompts actually behave when 150 people are asked the same question

→ Why PR and positioning are now 100x more important than tactical SEO

→ The white hat playbook for showing up in ChatGPT, Gemini, and Google AI Overviews

→ How to use AlertMouse and SparkToro together to find prompts your buyers actually use

→ What "audience intelligence" vs. "audience research" teaches you about brand positioning lag

→ The Seattle Ultrasonics knife story: how one founder turned mentions into product roadmap

→ Where Rand says marketers should spend attention in 2026 (and what to ignore)

Chapters:

0:02 Intro

0:38 The Datos prompt study and why AI answers are random

4:13 The two mechanisms that move LLM visibility

8:47 How Rand is marketing AlertMouse from zero visibility

13:42 Why positioning is the biggest AEO lever

17:08 AlertMouse use cases and the Seattle Ultrasonics story

22:11 Where to actually invest attention in 2026

Connect with Rand:

SparkToro: https://sparktoro.com

AlertMouse: https://alertmouse.com

LinkedIn: https://www.linkedin.com/in/randfishkinWhat are other ideas that we can add for the YouTube thumbnail? I don't really love "AI rankings don't exist." What are some of the main takeaways from the episode?

Ryan Doser shows the exact Claude Code workflow he uses to turn one YouTube video into a fully SEO-optimized WordPress blog post — screenshots, internal links, affiliate links, and meta data included — in about five minutes.

Ryan runs ryandoser.com, has a 33K-subscriber YouTube channel, and has been building production SEO systems with Claude Code. In this episode he screen-shares the whole pipeline: the skill markdown file that holds his SEO playbook, the Taddy MCP that scrapes the YouTube transcript, the Python script that pulls screenshots from the video, and the WordPress MCP that posts the draft. The result ranks on Google with a fresh domain and zero backlinks.

We also cover why "AI content" gets confused with "AI slop," how to organize skills so they don't collide, and why YouTube should be the foundation of any content strategy in 2026.

Key Takeaways

• A skill markdown file is your brain dump turned into a system. Without it, Claude Code produces slop. With it, you get publishable posts in five minutes.
• Start your content strategy on YouTube. Google ranks YouTube videos in search results, and one long-form video becomes the source material for a blog post, social posts, and an email.
• One task, one skill. Don't build three SEO skills. Build one and iterate. Use a Skill Creator skill plus auto-research to compound improvements.

Connect with Ryan

• Site: https://ryandoser.com
• YouTube: https://www.youtube.com/@RyanDoserAI

Most outbound teams burn through their TAM with the same generic sequences and wonder why CAC keeps climbing. Parthi Loganathan, CEO of LetterDrop, runs a one-person BDR team that books seven-plus meetings a week using a different approach: identify the small slice of buyers actively evaluating competitors, then build campaigns that lead with value instead of pitch.

In this conversation, Parthi breaks down how LetterDrop reverse-engineers competitor pipelines from public online signals - who is commenting on whose posts, who is connecting with which AEs, who is engaging with which content. He explains the highest-converting outbound campaign he has ever run (giving competitors' qualified leads away for free to VPs of Sales), why agentic systems will absorb the brittle Zapier-style workflows BDRs run today, and what part of the BDR job is permanent: face-to-face channels, creative campaign design, and intelligence work that requires actually understanding the buyer.

He also shares why he believes SaaS is entering a compression cycle - where the software that survives exposes itself as APIs and MCPs for agents instead of dashboards for humans - and why his team just shipped 70% of LetterDrop as MCP endpoints for Claude Code users.

Key Takeaways

1. Only 5% of your TAM is in-market at any time. Reaching out to the other 95% is what is killing BDR economics. Parthi's whole thesis is that outbound stops working when you spray and starts working when you read public signals to find the active 5% before competitors do.
2. You can reverse-engineer your competitor's CRM from public data. LetterDrop builds a graph of who is talking to whom online - comments, connections, engagement patterns - and uses that to guess which prospects are actively talking to your competitors. Without scraping anything proprietary.
3. The "free lead" campaign is LetterDrop's highest-converting outbound play. Identify someone evaluating a competitor, send the lead directly to that competitor's CMO or VP of Sales as a free gift. Perceived value is hundreds to thousands per qualified lead, so it gets opened, replied to, and converts at rates a pitch sequence cannot touch.
4. BDRs keep the face channels and the creativity. Agents take everything else. Account intelligence, sequencing, decisioning, and brittle Zapier-style workflows go to agents. BDRs own calls, video DMs, in-person, and the campaign creativity that requires understanding why your buyers actually buy.
5. SaaS is compressing toward APIs and MCPs. The software that survives the agent era will not be dashboards. It will be hard-to-replicate data exposed as MCP endpoints. LetterDrop already shipped 70% of its product as MCPs for Claude Code users - a preview of where B2B SaaS pricing power is heading.

LetterDrop - Signal-based outbound and competitive intelligence platform

SuperMarketers: Build your AI visibility system at https://supermarketers.ai

Follow Gen on LinkedIn: https://www.linkedin.com/in/genfurukawa

Tamara Ceman breaks down the exact AI workflow she uses to synthesize customer interviews, prioritize insights, and build interactive marketing tools - without exposing client data.Tamara is a B2B SaaS marketing consultant who's run marketing at Markup (now part of Eros) and U-Screen, and now helps founders break through growth plateaus as an interim head of marketing. In this episode, she walks through her four-tool stack: Granola for AI note-taking, Miro AI for clustering customer conversations into stickies, Claude Projects for synthesis with memory and context, and Lovable for shipping interactive workshop tools and team utilities. She also covers the data privacy reality that shapes every consultant's AI workflow, and why the handover between AI output and human judgment is where most marketers fail.Chapters00:00 Welcome and the "practical marketer" positioning01:00 Tamara's background and consulting methodology04:00 How AI changed marketing work (and what to stay skeptical about)08:00 The consultant's reality: data privacy and disconnected tools09:30 Granola: AI note-taking for customer interviews14:00 Miro AI: turning conversations into prioritized stickies17:00 The human-AI handover for judgment calls21:00 Claude Projects: setting up memory, context, and instructions25:00 Lovable: building no-code marketing tools (live demo)30:00 Where to find TamaraKey TakeawaysMost marketers fail at the handover between AI synthesis and human judgment. AI gets you to a draft fast - your job is the polish, the validation, and the call on what's actually accurate.Spend five minutes setting up a Claude Project with custom instructions, memory, and uploaded context (PDFs, screenshots, brand docs) before any real work. The output gets dramatically better and stays consistent across chats.Lovable lets you build interactive workshop tools, customer-facing exercises, and team utilities (Tamara demoed a YouTube script generator) without writing code. The use case: anywhere your team is doing repetitive work that doesn't need a human in the loop.Find TamaraPractical Marketer: https://practical-marketer.comSubscribeSuperMarketers Podcast on YouTubeMore from Gen: https://supermarketers.aiFollow me 👇https://www.linkedin.com/in/genfurukawa/===============================Who am I, and why should you listen to me?I’m Gen Furukawa — founder, operator, and marketing systems builder. I’ve built and sold a SaaS company, led marketing at a high-growth startup, and now help B2B SaaS teams scale content and demand with automation, AI, and strategy.At SuperMarketers, we don’t just give you content - we create powerful inbound growth engines that generate qualified leads without hiring too many people. I’ll share the exact strategies, processes, and automated systems we use with clients to help you turn your ideas into action, faster.Turn Your Team into a LinkedIn Growth Engine. Learn more: https://supermarketers.ai

Andréa Jones has published 400 podcast episodes while raising two kids under five. Her system is not hustle - it is infrastructure built from necessity.

The podcast is her content hub. Every idea starts as a voice note in Google Notes, gets structured in a ChatGPT project trained on her proprietary Mindful Marketing framework, audience personas, and email voice. She records in Riverside, then runs the audio through Cast Magic - a repurposing tool that generates 20 pieces of content per episode: show notes, social quotes, one-liners, newsletter drafts, and thread-style posts trained on her voice.

She layers these outputs into two Airtable calendars - an editorial calendar for signature content and a social media calendar she populates months in advance during high-energy windows. Content from episode 399 might not hit social until two months later.

Andréa also runs Uncommon Marketing Agency, where she builds AI-powered interactive web experiences for brands - including an 8-bit style game for Niagara Falls tourism that replaces the generic results you get from ChatGPT or Claude with a curated, closed-loop brand experience.

Her strongest take: marketers need to get "elegant" with prompting. The inputs need to be as detailed as the outputs you expect - audience personas, objection handling, awareness levels. Most people skip this and get generic results.

1. One podcast episode produces 20+ content pieces through Cast Magic - Andréa uploads each episode's audio and gets titles, timestamps, social quotes, one-liners, newsletter drafts, and thread-style posts - all trained on her voice. She edits but never starts from blank.

2. Content batching on energy cycles beats daily consistency - She populates her Airtable social calendar months in advance during high-energy windows, then coasts during low-energy periods. Her calendar currently runs through June with repurposed podcast content.

3. ChatGPT projects trained on your framework cut outline time from 2 hours to 20 minutes - She uploaded her Mindful Marketing framework transcripts, audience personas, offer positioning, and email voice into a single ChatGPT project she uses for every episode.

4. Your AI inputs need to be as long as your expected outputs - Her client's sales page read like generic ChatGPT because the prompt didn't include audience personas, objection handling, or awareness levels. Context engineering is the differentiator.

5. Closed-loop AI experiences beat open web for brand marketing - Uncommon Marketing Agency builds interactive web games that surface only the brand's curated content, avoiding the noise and dated information that LLMs pull from the open internet.

• OnlineDrea - Andréa's personal brand: courses, podcast, and the Do Less Market Better Kit (free course)
• Uncommon Marketing Agency - Gamified and interactive AI-powered marketing experiences
• Mindful Marketing Podcast - 400 episodes on anti-burnout marketing strategies
• Cast Magic - AI podcast repurposing tool (generates 20+ content pieces per episode)
• Riverside - Podcast recording and transcript-based editing

Key TakeawaysLearn More

Six to nine targeted bottom-of-funnel pages will outperform 50 top-of-funnel blog posts for pipeline - and Lashay Lewis has the client data to prove it.

Lashay breaks down the exact four-element framework she uses to build bottom-of-funnel content: pain points, features, benefits, and capabilities - stacked like Legos in a specific order. Pain leads because high-intent buyers need to connect immediately. Features follow because they solve the pain directly. Capabilities prove the features actually work.

She walks through live examples from clients like Teal and Conveyor, showing how she reverse-engineers sales call transcripts to extract the exact language buyers use - then maps that language to contextual AI search queries. The distinction matters: Google search is keyword-based ("best resume builder"), but AI search is context-based and persona-driven ("I'm a job seeker struggling to show impact across multiple resumes").

Lashay explains why she's skeptical of prompt volume tracking tools - if queries are essentially one-of-one, traditional volume metrics break down. Instead, she expands surface area by mapping synonyms and predicting before/after queries around a core topic.

She also shares her three-year founder journey from consultancy to failed product pivot and back again - including how muddied positioning nearly killed her business before BOFU.ai found its footing.

1. Pain points must lead every bottom-of-funnel page - High-intent buyers need to feel understood in the first few seconds. Leading with product history or "what is" definitions is a top-of-funnel mistake that kills conversion on BOFU pages. Lashay sees 8-minute read times on articles that lead with pain.

2. AI search is context-based, not keyword-based - Someone typing into ChatGPT writes a paragraph about their situation, not three keywords. Your content needs to match that contextual query by including persona, category, pain points, and capabilities - not just keyword-stuffed headers.

3. Sales call transcripts are the most underused content asset in SaaS - Your buyers' language is not your internal language. The gap between how your company describes itself and how the market talks about the problem is where positioning breaks. Sales calls close that gap.

4. Your competitors' pages about you shape your AI search presence - Lashay shows how Perplexity pulled a competitor's two-out-of-five rating of Teal into a citation. What you have public-facing matters because AI pulls from competitor alternative pages to describe you.

5. Prompt volume tracking is likely broken - If AI search queries are essentially one-of-one natural language strings, traditional search volume metrics don't apply. Expand surface area by topic, not by keyword. Map synonyms and before/after queries around a core topic instead.

• BOFU.ai - B2B SaaS content marketing consultancy focused on bottom-of-funnel content and attributable pipeline
• Built from the Bottom (Substack) - Lashay's deep dives on content strategy, AI search, and building in public
• Teal - Resume builder used as a live case study for the BOFU framework
• Conveyor - Security questionnaire automation platform, client example for AI search results
• Perplexity - AI search engine referenced for competitor citation behavior
• Fletch (Anthony Perry), Rob Kaminsky - Product marketers whose homepage positioning frameworks inspired Lashay's content approach

SuperMarketers: Build your AI search visibility system at https://supermarketers.ai

Connect with Gen: Follow for weekly breakdowns on AI visibility and content systems -> https://www.linkedin.com/in/genfurukawa

Connect with Lashay Lewis: Follow her for deep dives on bottom-of-funnel content and AI search strategy -> https://www.linkedin.com/in/lashaylewis

Key TakeawaysLearn MoreCTAs

Steve Toth has spent 15 years in SEO and now runs one of the sharpest AEO/GEO consultancies in B2B. His core argument: stop tracking where your brand ranks in LLM responses. Start measuring whether LLMs represent you accurately.

His Trust Alignment Framework scores how well ChatGPT, Perplexity, and Gemini answer questions about your product across six pillars - vertical, company size, comparisons, pricing, integrations, and features. The gap between your "sales-grade answer" and the LLM's answer is your visibility problem.

Steve walks through live demos showing how ChatGPT Deep Research and Perplexity surface follow-up refinements - and how collecting those refinements across 5-8 runs reveals which deal-breaker topics matter most in your category. He also shares a Claude project that clusters Google Search Console keywords by intent, giving B2B teams a proxy for LLM search demand when no reliable prompt volume data exists.

The conversation covers how each model cites differently - ChatGPT prefers general pages, Google AI Mode pulls specific passages from case studies and UGC - and why passage-level optimization matters more than page-level. Steve closes with his Spellbook case study: 90% non-branded organic traffic growth by targeting emerging keywords in the legal AI space and capitalizing on competitor sentiment gaps.

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Key Takeaways

1. LLM leads convert 4-5x higher than Google traffic - ChatGPT referral visitors spend 4-5x more time on site and convert at 4-5x the rate. These buyers arrive pre-educated with specific deal-breakers already defined. Your sales team closes them faster.

2. Stop tracking brand mentions in LLMs - measure representation accuracy instead - The Trust Alignment Framework compares your ideal sales answer against what the LLM actually says across six pillars (vertical, company size, comparisons, pricing, integrations, features). The delta is your real visibility gap.

3. LLM prompt volume tools are unreliable - use intent clustering as a proxy - Every word added to a prompt makes it less likely to be searched twice. Steve built a Claude project that clusters Google keyword data by intent and aggregates volume across the entire cluster, giving directional demand signals for AEO prioritization.

4. Each AI model cites sources differently - ChatGPT favors first-party "ultimate guide" pages. Google AI Mode pulls specific passages from case studies and UGC. Claude uses the Brave search index. Optimizing for one model does not guarantee visibility in others.

5. Passage-level optimization beats page-level for AI Mode - Google AI Mode uses a passage ranking index, not a page ranking index. It looks for 100-300 token excerpts that support its reasoning chain. You can pepper relevant content across case studies, homepages, and comparison pages rather than building one monolithic page per topic.

Learn More

- SEO Notebook - https://seonotebook.com - Steve's weekly SEO newsletter, running since 2019

- AI Notebook - https://ainotebook.com - Weekly newsletter focused on AEO/GEO strategies

Connect with Gen:

- www.supermarketers.ai

- www.linkedin.com/in/genfurukawa

I am creating an AI-run company that operates without a single employee. This is what it actually looks like inside.Sam Altman predicted the billion-dollar one-person company. Nat Eliason built Felix Craft. I wanted to test whether it was real -- so I built vibemarketers.ai: an AI marketing tools directory run entirely by an AI agent named Viv.Viv operates autonomously through OpenClaw. She communicates via Telegram. I spend 30 minutes a week reviewing her output. That's it.In this video I break down:- What Viv actually does (and what she can't do yet)- How the system is structured for autonomous operation- Why I built content for LLMs, not just search engines- What this means for the future of solo founder companiesResources:vibemarketers.ai: https://vibemarketers.aiSuperMarketers: https://supermarketers.aiConnect:LinkedIn: https://linkedin.com/in/genfurukawa#ZeroEmployeeCompany #AIAgents #FutureOfWork===============================Who am I, and why should you listen to me?I’m Gen Furukawa — founder, operator, and marketing systems builder. I’ve built and sold a SaaS company, led marketing at a high-growth startup, and now help B2B SaaS teams scale content and demand with automation, AI, and strategy.At SuperMarketers, we don’t just give you content - we create powerful inbound growth engines that generate qualified leads without hiring too many people. I’ll share the exact strategies, processes, and automated systems we use with clients to help you turn your ideas into action, faster.Turn Your Team into a LinkedIn Growth Engine. Learn more: https://supermarketers.ai

Derek Iwasiuk, co-founder of SearchTides, breaks down his 5-layer "AI Search Undercurrent" framework for getting brands recommended by ChatGPT, Google AI Mode, and Perplexity. We dig into how he tracks 500+ prompts per client, why fan-out queries are the new keyword research, and the free tactics any founder can use today to boost their AI visibility, from Reddit AMAs to $80 press releases.Derek has 18 years of SEO experience and now runs operations and strategy at Search Tides, an AI search agency focused on financial services and beyond.Connect with Derek: www.searchtides.com⚡️ Grow 10x Faster with AI-Powered Growth System. No Team, No Agency, No Stress. Go here: https://supermarketers.ai/Follow me 👇https://www.linkedin.com/in/genfurukawa/===============================Who am I, and why should you listen to me?I’m Gen Furukawa — founder, operator, and marketing systems builder. I’ve built and sold a SaaS company, led marketing at a high-growth startup, and now help B2B SaaS teams scale content and demand with automation, AI, and strategy.At SuperMarketers, we don’t just give you content - we create powerful inbound growth engines that generate qualified leads without hiring too many people. I’ll share the exact strategies, processes, and automated systems we use with clients to help you turn your ideas into action, faster.Turn Your Team into a LinkedIn Growth Engine. Learn more: https://supermarketers.ai