The Ross Simmonds Show

In this episode of The Ross Simmonds Show, Ross sits down with Brittany Muller, AI educator and founder of Orange Labs, to unpack what marketers are getting wrong about large language models, why reverse engineering ChatGPT is a dead end, and how to build real leverage in a probabilistic world. From practical AI workflows to the ethical risks shaping the future of the industry, this is a first-principles breakdown of what actually matters next.

Key Takeaways and Insights:

1. AI is not search, it is a different machine entirely

- LLMs are probabilistic word prediction systems, not ranking engines. There are no ranking factors inside ChatGPT and no URLs in its training data.

- Most marketers are forcing AI into an outdated SEO mental model, and new technology requires a new framework.

2. Understanding RAG and how visibility actually works

- LLMs are often paired with real-time search to stay current, but the core model and the retrieval layer are two separate systems.

- Visibility in AI requires influence across both training data and search ecosystems, and SEO still matters even as the mechanics are shifting.

3. Brand mentions over backlinks

- LLMs magnify what appears most frequently in training data, which means contextual brand mentions are becoming leverage.

- One startup paid for brand mentions on commonly retrieved URLs rather than links and it worked. Distribution across relevant conversations increases the probability of surfacing.

4. Why you cannot reverse engineer LLMs

- There is no deterministic ranking system to hack. Outputs vary across identical prompts because of probabilistic modeling.

- Most AI tracking tools rely on synthetic prompts and crude metrics. Guarantees in GEO are dangerous and honesty builds trust.

5. Build your own AI tracking stack

- Internal tools are now cheaper and more powerful than off-the-shelf platforms. Running prompts multiple times per day allows teams to measure probability ranges.

- APIs allow thousands of queries at minimal cost. Control your data and do not outsource your intelligence.

6. Real AI workflows built by marketers

- Competitive engagement scraping combined with AI-personalized outreach is producing 80 percent response rates. HARO filtering systems can now auto-draft responses inside Slack in real time.

- The common thread across every workflow that works is the same: start with a clear problem, then layer in AI.

7. AI as personal leverage

- Brittany used ChatGPT to win a home bidding war with a personalized letter and reframed a payment dispute email as a lawyer, which resulted in payment within 30 minutes.

- AI is not just marketing leverage. It is life leverage. Literacy creates power.

8. Is SEO dead? Not quite.

- Google patents suggest AI-first interfaces may replace traditional SERPs, and organic traffic levels will likely not return to pre-AI highs.

- The pie may shrink but search will not disappear. Off-site distribution and social proof will matter more than ever.

9. The ethical risks of AI power

- A small group of decision-makers controls foundational AI systems, and the incentives in place favor hype cycles and growth over accountability.

- Reinforcement learning optimizes for pleasing users, not truth. AI literacy must include understanding bias and power structures.

10. The rise of AI agents

- Early agents were mostly hype, but new iterations like Claude Chrome integrations can now visually interpret and act inside browsers using screenshot-based reasoning.

-The future of marketing may involve AI transacting on behalf of users entirely, and execution changes workflows.

Resources & Tools:

🔗 Orange Labs
🔗 ChatGPT
🔗 Claude

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— 🎙️ Let's stay connected with Brittany —

╰ Twitter / X: @BritneyMuller
╰ LinkedIn: linkedin.com/britneymuller

Stop scrolling through fantasies about moving to big cities... this episode will hit different. Matt Paulson joined me to break down how he created roughly $200 million in wealth through MarketBeat while based in Sioux Falls (population ~200k). We go in on the non-negotiable principles that drove his 20-year compounding success... why location independence + community roots beat the coastal grind... exactly how he'd start over in today's world... and the inspiring ways he's poured that success back into his local ecosystem. If you're building something meaningful and want real, grounded inspiration instead of hype, drop everything and listen to this one. Trust me... you don't want to miss it.

Key Takeaways and Insights:

1. Distribution Is the Real Moat

- Great content loses to average content with better distribution.

- Google algorithm updates forced MarketBeat to diversify early.

- Matt dominated the Google Finance tab when everyone else fought over blue links.

- Lesson: Find underpriced attention. Capture it. Convert it to owned channels.

2. Email as the Core Asset (Not Social)

- 200,000+ daily pageviews were converted into email subscribers via smart opt-ins.

- Daily emails for engaged users. Weekly for cooling segments.

- Reactivation campaigns target 30–270 day inactive subscribers.

- Engagement is measured by purchases, not just opens and clicks.

3. Scaling to $60M with a 19-Person Team

-$50M in revenue with 19 employees (40 including contractors).

-Media is leverage-heavy — subscriber growth doesn’t require proportional headcount.

-Belief: $100M revenue with ~30 people is realistic.

-Systems > staffing.

4. Paid Acquisition as the Growth Engine

- 80% of new leads now come from paid channels.

- $1.4M/month in ad spend with plans to test up to 10 new channels this year.

- Each channel has a profitability ceiling ,you find it by testing.

- Three-month lag to break even on new paid cohorts.

5. Backend Data > Cheap Leads

- Cheap leads are often unprofitable leads.

- Channel-level tracking determines which subscribers buy, not just open.

- SparkLoop drove engagement but not purchase intent. It was cut.

- Principle: Optimize for lifetime value, not cost per subscriber.

6. AI as Leverage, Not Strategy

- Three content types: human-written, templated automation, pure generative AI.

- AI summarizes earnings transcripts into publishable articles.

- “Molti” (Claude workflows) writes daily tweets, manages calendar buffers, flags performance anomalies.

- AI augments operators. It doesn’t replace judgment.

7. Why YouTube Is the Next Growth Bet

- 620K subscribers in ~3 years.

- Built around a professional host and expert interviews.

- Investing in a full studio buildout to scale production quality.

- Organic is stable. Paid drives scale. Video builds future-proof attention.

8. Building a $50M Company from South Dakota

- Sioux Falls. Population ~250K metro.

- No VC distractions. No “next hot thing” syndrome.

- Fewer peers. Fewer temptations. More focus.

- Bootstrapped. 100% ownership retained.

9. Venture Investing Lessons (What Fails)

- Every idea-stage investment with zero revenue failed.

- Now requires ~$20–25K MRR before investing.

- Avoids biotech/FDA-heavy businesses due to capital intensity.

- Watches burn rate closely: $500K/month burn kills startups fast.

10. Success Redefined: Enjoyable Days in a Row

- No desire to sell MarketBeat.

- Cash flow over exit multiples.

- Defines success by how many enjoyable days he stacks consecutively.

- Business as leverage for impact: philanthropy, community, and ownership.

Resources & Tools:

🔗 MarketBeat.com
🔗 Distribution.ai
🔗 SparkLoop

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— Connect with Matt —

╰ Twitter / X: @MediaKing
╰ Instagram: @MattPaulsonOfficial
╰ LinkedIn: linkedin.com/in/matthewpaulson

In this episode of The Ross Simmonds Show, Ross sits down with Mike King, founder of iPullRank, to unpack the seismic shift from traditional SEO to AI search, AEO, and GEO, and why framing it as "just SEO" is quietly costing teams budget, influence, and growth. Together, they break down the Google leak, retrieval-augmented generation (RAG), content ecosystems, and what separates operators from spectators in the next era of search.

Key Takeaways and Insights:

1. SEO vs. AEO vs. GEO: why it's not "just SEO"

-The tactics SEOs talked about for years are now mandatory in AI search,and AI platforms evaluate your entire content ecosystem, not just your website.

-Calling AI search "just SEO" limits budget, authority, and strategic ownership before the conversation even starts.

2. The C-suite perspective most SEOs miss

-Executives are already asking why their brand doesn't appear in ChatGPT, and AI search carries trillion-dollar narratives that traditional SEO never did.

-Teams that frame this as a new growth channel are the ones unlocking real investment.

3. Why video is a high-leverage AI search play

- YouTube is one of the most cited sources in AI-generated answers,and AI search rewards consensus across formats, from video and Reddit to PR and editorial.

- Starting with five strategic videos in an underserved topic cluster, then repurposing aggressively, is one of the highest-ROI moves available right now.

4. How AI search actually works: RAG and query fan-out explained

- AI search uses retrieval-augmented generation: prompts expand into synthetic sub-queries, each with their own format expectations.

- The more relevant passages a brand owns across formats, the more chances it has to be cited, think of it as accumulating raffle tickets.

5. Measuring AI search performance the right way

- There are three metric buckets that matter, performance, channel, and input. Most teams are only tracking one.

- Input metrics like synthetic query rankings, passage relevance, entity salience, and bot activity are where the real diagnostic power lives.

6. Real AI workflows inside iPullRank

- The team is building internal tools with Gemini and AI Studio, including automating internal linking through vectorization combined with human business rules.

- AI handles the minutiae, humans make the strategic calls, and that efficiency is the hedge against client scrutiny over the next two years.

7. Programmatic SEO, why most sites tank

- Google is indexing less and testing content performance faster, and high bounce rates signal UX failure, not an AI penalty.

- Recovery demands tight topical authority and, in many cases, new URL structures and full content audits.

8. Building a career that survives the next five years

- Technical AI fluency is no longer optional, and content alone is now a free commodity, the leverage is in systems and engineering.

- Operators beat theorists. The next generation of SEOs must ship, not just strategize.

9. Creativity, code, and AI as an artist

- Writing rhymes and writing code pull from the same creative muscles,and AI works best as a feedback loop, not a ghostwriter.

- The real risk isn't AI,  it's lazy implementation. Tools expand creative possibility; they don't replace taste.

10. Relevance engineering, building a new category

- AI search needs new frameworks, not retrofitted SEO tactics, and creating a named methodology positions a brand above commodity vendors.

- Owning a concept, building authority around it, and ranking for your own category is a long game worth playing.

Resources & Tools:

🔗 Reddit.com

🔗 iPullRank.com

🔗 ChatGPT

🔗 YouTube 

🔗Google AI Studio 

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— Connect with Mike  —

╰ Instagram: @ipullrank

╰ Twitter / X: @iPullRank

╰ LinkedIn: linkedin.com/in/michaelkingphilly/

In this episode of The Ross Simmonds Show, Ross breaks down five underrated career strategies that quietly separate high performers from everyone else. From investing in yourself without permission to thinking in decades instead of quarters, this is a tactical blueprint for anyone serious about long-term growth. If you're playing the long game in your career, this episode gives you the mindset and structure to win it.

Key Takeaways and Insights:

1. Invest in Yourself (Without Waiting for Permission)

- Stop waiting for HR or leadership to approve your growth.

Identify your skill gaps and proactively close them.

Books, courses, and communities offer massive ROI over time.

Treat self-education as an investment, not an expense.

2. Take On the Projects No One Else Wants

- Volunteer for high-visibility, low-competition initiatives.

- Align yourself with projects leadership cares about.

- “Messy” projects often create the biggest breakthroughs.

- Growth lives where others hesitate.

3. Close Skill Gaps Before They Cost You Opportunities

- Be honest about where you're weak (public speaking, strategy, tools, etc.).

- Build deliberate practice into your routine.

- Don’t stay passive while others outpace you.

- Small improvements compound into major career leverage.

4. Build a Body of Work Outside Your Job

- Your employer doesn’t own your expertise.

- Publish ideas on LinkedIn, newsletters, GitHub, podcasts, or blogs.

- Contribute to communities and become known for value.

- Visibility creates opportunity especially in uncertain markets.

5. Find a Mentor Who Tells You the Truth

- You don’t need a cheerleader, you need critique.

- Ask for blunt, honest feedback about your blind spots.

- Growth accelerates when your thinking is challenged.

- Seek mentors internally, externally, or both.

6. Join Rooms Where Serious People Talk About Real Problems

- Surround yourself with ambitious peers.

- Learn by observing how others solve complex challenges.

- Communities can act as informal coaching ecosystems.

- Exposure to higher standards raises your own.

7. Think in Decades, Not Quarters

- Define the skills, reputation, and life you want in 10 years.

- Reverse-engineer what you need to invest in today.

- Systems beat short-term hustle.

- Long-term clarity drives better short-term decisions.

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In this episode of The Ross Simmonds Show, Ross breaks down the so-called “SaaSpocalypse” after $1 trillion in SaaS market cap vanished in a single week. While headlines scream that “AI will replace SaaS,” Ross argues the reality is far more nuanced. He introduces a three-part framework: Exposed, Embedded, Evolved, and outlines the strategic shifts founders and marketers must make to survive and compound in the age of AI agents.

Key Takeaways and Insights:



1. The $1 Trillion Wake-Up Call

-SaaS stocks were crushed in early 2026, triggering fear across markets.

-AI agents, LLM advancements, and disappointing earnings accelerated the correction.

-The dominant narrative says AI will replace SaaS, but the situation is more complex.

-Market fear is loud. Structural change is quieter, but very real.

2. AI Agents, Vibe Coding & the Death of Per-Seat Pricing?

-AI agents interacting directly with APIs challenge traditional SaaS interfaces.

-“Vibe coding” demonstrates how quickly software can now be replicated.

-Per-seat pricing models are under pressure as automation scales output.

-The interface is shifting from dashboards to conversations.

3. The Data Reality Most People Ignore

-Global SaaS spending is projected to grow from $318B (2025) to $500B+ (2028).

-Enterprise contracts and deep dependencies don’t disappear overnight.

-Pricing models may change. Market leaders may change.

-Software demand isn’t vanishing, it’s evolving.

4. The Extinction Stack: Exposed, Embedded, Evolved

-SaaS companies fall into three survival tiers.

-Not all SaaS companies face equal risk.

-Your future depends on depth of integration and data moat.

-Operators must identify where they sit, now.

5. Type 1: The Exposed

-Horizontal point solutions with weak moats and low switching costs.

-Easily replicated with AI tools in days or weeks.

-Rely on habit rather than proprietary advantage.

-Most vulnerable to margin compression and churn.

6. Type 2: The Embedded

-Deeply integrated systems of record inside enterprises.

-Painful and complex to replace due to migration risk.

-The risk isn’t extinction, it’s interface disruption.

-Must become AI-first before agents abstract them away.

7. Type 3: The Evolved

-AI-native or aggressively AI-integrated platforms.

-Built on proprietary data, regulatory moats, and deep user memory.

-AI increases the value of their data advantage.

-Positioned not just to survive, but accelerate.

8. Distribution Is the New Defensive Moat

-AI can replicate features. It cannot replicate trust.

-Brand equity, audience relationships, and distribution compound.

-As product development gets cheaper, distribution becomes the advantage.

-This is the moment to double down on quality and amplification.

9. From Time-Based to Outcome-Based Thinking

-Per-seat and time-based pricing models face structural pressure.

-The future favors outcome-driven pricing and accountability.

-Buyers will demand measurable impact, not access.

-Service businesses must shift from hours sold to results delivered.

10. Intentional AI vs Fear-Based AI

-Two types of teams are emerging: intentional adopters and reactive adopters.

-AI without process creates noise, not leverage.

-10,000 mediocre AI assets won’t move the needle.

-10 strategic, AI-enabled assets can change a business trajectory.

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