The Dave Gerhardt Show – Exit Five

Episode: "How Marketers Are Creating High Quality Content with AI (That's Not Slop!)"

Date: March 9, 2026
Host: (Guest Host) Dan (CEO, Exit Five)
Guests:

• Owen (VP Growth, Air Ops)
• Connor (Senior SEO Manager, LegalZoom)
• Adina (Head of AI, WeFlow)

Overview

This episode, guest-hosted by Dan (Exit Five’s CEO), dives into how leading marketers are leveraging AI to create genuinely high-quality content—avoiding the all-too-common pitfall of AI-generated “slop.” Drawing from their own workflows in companies like Air Ops, LegalZoom, and WeFlow, the guests offer concrete strategies for maintaining content quality, leveraging authentic subject matter expertise, and meeting the shifting expectations of both audiences and search engines in the age of LLMs (Large Language Models).

Main Theme:
The discussion centers on strategic, tactical approaches for producing content with AI that’s unique, authoritative, and valuable—rather than generic or spammy—by bringing in real human expertise and robust internal data.

Key Discussion Points & Insights

1. The Problem with AI-Generated “Slop” (00:00–06:00)

• Dan and Dave frame the modern challenge:
  • The glut of low-quality, generic AI content (“slop”) is raising the bar for what meaningful content looks like.
  • AI slop undermines trust, brand equity, and conversion rates.
  • New tools are appearing (e.g., Air Ops) to enable content marketers to produce authoritative, expert-informed content at scale.

Notable Quote:

“AI generated slop...it raises the bar, right? AI slop is going to kill deals, kill brand, and kill trust...It’s on us to create things that actually matter, that have meaning and impact, things that are educational, entertaining, funny, useful, specific and relevant.” – Dave Gerhardt (00:00)

2. Why Brands Are Struggling to Connect via Content (06:00–11:45)

Owen (Air Ops):

• Quality, volume, and velocity are now in constant tradeoff; quality is most often neglected—at great cost.
• Effective AI content relies on prioritization, not just tooling.

Notable Quote:

“What’s actually difficult is the prioritization of what actually matters...content quality is the hardest attribute to get right. It’s also the attribute most teams are skimping on, and it’s costing them results today.” – Owen (06:19)

Connor (LegalZoom):

• Marketers are experiencing an “identity crisis”: formerly, their value-add was informational content, but now AI answers meet much of that demand.
• The new challenge: deliver content that adds incremental (new) value beyond what LLMs/self-serve AI tools can provide.

Notable Quote:

“...if all we're doing is paraphrasing what previously existed, we're never going to beat the engines. They set the rules, they have the tech. They’re just better at it than we are.” – Connor (13:45)

Adina (WeFlow):

• Many marketers rely on outdated SEO playbooks—cranking out volume with little differentiation.
• The mindset shift: Move from “How do we rank in Google?” to “How do we teach and help LLMs recommend us when we’re the best choice?”

3. Real Workflows: High-Quality AI Content in Action

A. LegalZoom’s “Project Penny” – Content Ops for SME Quotes (13:23–24:51)

Connor’s Process Highlights:

• Identify true differentiators—e.g., LegalZoom’s attorney network as a source of unique quotes and expertise.
• Project Penny automates:
  1. Scrape articles (input: URL).
  2. LLM finds 5 best content locations for SME quotes.
  3. Select most relevant SME(s) from knowledge base.
  4. Auto-ping SMEs via Slack; collect and post their quotes back in the content.
• Outcome: Can update 1,000+ legacy articles with fresh expertise rapidly.

Impact:

• Builds trust and directly addresses E-E-A-T quality signals (Expertise, Experience, Authoritativeness, Trustworthiness).
• Human quotes drive up conversions and help content outperform both competitors and generic LLM outputs.

Memorable Use Case:

In a content section on “How to name your business,” the LLM’s advice was “pick a flashy, memorable name.” But LegalZoom’s attorney advised the opposite—“use a generic legal name, and file a DBA for something flashy.” Real, counterintuitive value only a human SME could provide. (19:02)

B. WeFlow’s “AI-Assembled” Content Engineering (28:16–44:08)

Adina’s Process Highlights:

• Shift from 150 articles/month (volume) to fewer, deeper pieces (quality).
• “AI-assembled, not AI-generated”: Breaks work into granular sections, collects internal intelligence (sales calls, competitor data, reviews, product docs, etc.).
• Builds dynamic, multi-source comparison pages with updatable modules (not static, not one-off).
• Continuously refreshes competitor data and page content based on both LLM output and new business intel.

Notable Quotes:

"Good content isn't AI generated…it's about AI assembled." – Adina (28:53) “The new strategy isn’t just pushing volume; it’s granular, updating competitor comparisons, pulling facts from sales calls and real customer conversations.” – Paraphrased from Adina (39:20–44:08)

C. Tools & Infrastructure

• Both teams use robust knowledge bases, modular content templates, Slack, Webflow, and Air Ops (or similar platforms) for integration and scale.
• Brand kits and sales conversation intelligence (not just documentation) are critical to feeding AI robust, relevant context.

4. Research Insights & Performance Data (44:08–54:57)

Owen’s Takeaways & Research Findings:

• Structure Matters: H1s, lists, clear blocks, proper schema are more important than ever for both Google and LLMs.
• Frequent Updates = More Citations: Pages refreshed quarterly get 3x the AI citations.
• Off-site Authority: 85% of AI citations reference third-party content—not just your own site (so get your brand in relevant discussions elsewhere).
• “Information Gain” is King: New knowledge, proprietary data, true first-person or SME-driven insights drive ranking and LLM recommendations.

Notable Quote:

“You want quality plus performance…Now it’s: can you find frontier knowledge from your internal experts and put that into your content to actually get results.” – Owen (44:40)

• Even without a big data operation, every company has some unique edge—SMEs, customer questions, product data—the trick is to harness and format it for AI and engines.

Notable Quotes & Memorable Moments

• On the new bar for content:

  “The bar for getting cited [by AI] is much higher than just getting listed in your 10 blue links. Structure matters.” – Owen (44:08)

• On moving from quantity to quality:

  “Right now we just need better content. So it’s not the volume, it’s about the quality we put in there.” – Adina (28:16)

• On human expertise as differentiator:

  “Having the actual human with an actual quote is just way more valuable…especially in a trust-sensitive space like legal.” – Dan, summarizing Connor’s approach (23:24)

Timestamps for Important Segments

• 00:00–06:00: Cold open; the “AI slop” problem and why it raises the bar
• 06:00–11:45: Why brands are failing to connect through content (panel intros and opinions)
• 13:23–24:51: Connor’s workflow breakdown: Project Penny, SME integration, automation
• 28:16–44:08: Adina’s engineering approach: granularity, data sources, dynamic content, “AI assembled”
• 44:08–54:57: Owen’s research, actionable performance metrics, and why “information gain” rules
• 54:57–56:09: Closing remarks, resources, and attendee Q&A

Actionable Takeaways

• Leverage Real Expertise: Integrate SME quotes, internal sales intelligence, and proprietary product data—this creates content that LLMs and humans both trust.
• Go Granular, Not Generic: Break content down into modular sections, continually refresh with new data.
• Update Existing Content: Quick wins come from improving legacy assets with new expert insights—don’t just chase new content for new’s sake.
• Embrace ‘AI Assembly’: Use AI for organization, workflow, and surfacing opportunities—not just word generation.
• Mind the Ecosystem: Off-site mentions and third-party citations are critical for AI search—boost your presence beyond your own website.

Closing Thoughts

This episode offers a masterclass in 2026-era content marketing—where AI is simply the starting line, and the true winners are those leveraging unique human insight, robust data, and intelligent workflows to influence both humans and algorithms. Marketing is (still) a craft, and those who treat it as such—supporting, not replacing, human expertise with the right AI infrastructure—stand to win in this new landscape.

Resources Mentioned:

• AirOps AI Content Guide
• [LinkedIn–Adina, Connor] (links shared in chat)
• Exit Five Community