The Long Game shares personal and business success stories where long-term thinking triumphs. Join us for discussions about the frameworks, principles, and learnings that drive success in business.
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Three Dinosaurs Talk AEO (with Benji Hyam & Devesh Khanal)
Episode 247
Wednesday, September 23, 2026 • Duration 01:21:40
In this episode of The Long Game Podcast, Alex Birkett sits down with Benji Hyam and Devesh Khanal, co-founders of Grow and Convert, to separate what's real from what's noise in AI search optimization. Drawing on their work with 100+ brands and their own AI visibility tracking data, they challenge popular narratives around listicle penalties, Reddit citations, and one-size-fits-all GEO tactics. They discuss how personalization and context-rich prompts make AI search fundamentally different from Google, why long-form content is losing its role in the buyer's journey, and the rise of cold inbound leads who arrive with no prior connection to the brand. The conversation also covers why positioning, reputation, and on-site source material matter more than mass third-party mentions, why attribution is getting harder as marketing becomes multi-channel, and how brands need to rethink where they earn attention.
Key Takeaways
Self-promotional listicles are not penalized by default; the widely cited examples involved thousands of AI-generated pages with misleading claims, while human-written, category-relevant comparison content has stayed stable through Google updates.
Aggregate citation studies overweight Reddit and Wikipedia because their datasets skew toward high-volume consumer queries, whereas Grow and Convert found 86% of citations for B2B buying prompts came from industry-specific sites.
YouTube shows up in citations across B2B clients far more often than the industry discusses, making video a real opportunity for reaching buyers.
Roughly 80% of sources cited by LLMs in Grow and Convert's research ranked in Google for ordinary keywords with search volume, which suggests SEO fundamentals still drive much of AI visibility.
AI summaries strip out the differentiated arguments that used to win readers over, producing cold inbound leads who compare several vendors on deliverables and need to be educated on your point of view during the sales process.
Personalization means the same prompt can return entirely different recommendations for different users, so prompt-tracking tools cannot capture many of the moments where a brand actually wins a lead.
Positioning and voice-of-customer research are now root-level requirements, because content on topics outside your core category can weaken the brand associations LLMs form about you.
Hyper-specific, pain-point content lets smaller brands beat incumbents in AI search, and there is no 301 redirect for LLMs, which makes rebrands, pivots, and reputation fixes much harder than in traditional SEO.
Holding a defined experimentation budget with expected-value thinking keeps low-cost tests like FAQs separate from expensive bets like newsletter sponsorships that rarely justify their cost.
Third-party mention strategies work best when they back into the specific topics and sites that influence your category, while strong on-site source material is both more persuasive and more defensible against inaccurate claims.
In this Kitchen Side episode, Alex Birkett and David Khim unpack the volatility of AI search, from citation drift and the sudden Reddit citation drop to why chasing every new AEO tactic is both exhausting and, often, a losing game.
They explore how to build durable brand visibility upstream of any single citation source, the difference between expertise and tools in the age of AI, and a decision philosophy rooted in capping downside risk, preserving optionality, and making intelligent bets with asymmetric upside.
Key Takeaways
Creating a perception of ubiquity through repeated exposure is the same underlying play across SEO, AEO, and PR, essentially the mere exposure effect applied to search surfaces.
Citation sources turn over fast, roughly 50 percent month over month in their own analysis, so optimizing only for currently cited URLs means chasing a moving target.
Rather than targeting individual citation sources, brands should optimize for the probability of adding more of their own marbles to the urn, a path-dependent advantage that compounds over time.
For brands with near-zero AI visibility, almost any mention on an influential surface compounds, so early-stage effort should focus on tactics and outreach.
As brands mature, the leverage shifts upstream to things that make citation sources irrelevant, like great customer experience, original research, and shipping products worth talking about.
Original research does not need to be a 30-page report; quick surveys and single-chart studies can spark conversation and earn citations with far less effort.
The Reddit citation drop matters less than whether a brand's ICP is genuinely active there, since Reddit stays valuable as training data and a decision-influencing surface regardless of citation metrics.
Chasing whatever is trending on LinkedIn is a paper-hands strategy; durable growth requires a long-term hypothesis and a genuine point of view that daily data should not override.
The real value of an experienced partner over AI plus a content team is judgment, knowing what to ignore, spotting risk, and debating a client out of detrimental moves.
People systematically overweight improbable, low-impact downside and miss asymmetric upside; capping the downside through experiments and expected-value thinking lets you take high-upside bets without risking everything.
Kitchen Side: The Internet Has a Memory
Episode 245
Wednesday, August 12, 2026 • Duration 56:43
In this Kitchen Side episode, Alex Birkett and Allie Decker unpack what actually makes content valuable in the age of AI, moving past the tired debate over human versus AI-produced content to the deeper question of how companies should measure quality at all. Drawing on decision theory and the idea that almost anything can be measured, they argue that production method matters far less than whether a piece teaches something new and lands with the people it is meant for.
They explore AI visibility as a kind of digital brand recall, why sentiment and context matter more than raw share of voice, and how incumbents and startups face very different battles in shaping their narrative across the web. The conversation also digs into why brands no longer own their own story, the compounding risk of building exposure in the wrong category, and why a human QA layer grounded in real customer understanding is the one part of the content system that should never be automated.
Key Takeaways
The debate over AI-produced versus human-produced content misses the deeper question of how to define and measure content quality in the first place.
Almost anything can be measured if a decision carries uncertainty and risk, including fuzzy concepts like brand affinity or customer experience, by breaking them into concrete, quantifiable components.
Measurement is only worth doing when the cost of collecting it is lower than the value of reducing uncertainty, and when you will actually act on the result.
AI visibility works like a digital brand recall survey, but raw visibility is meaningless without understanding the context and sentiment in which a brand is mentioned.
Segmenting prompts by ICP and buying stage reveals visibility gaps that aggregate scores hide, such as a brand appearing at 40 percent overall but only 4 percent for enterprise queries.
Brands no longer own their own narrative, because if the rest of the web contradicts a company's website, LLMs treat the external consensus as the truth.
Incumbents with high domain authority have an SEO advantage but face a harder AEO battle, since years of press, mentions, and third-party content are slow and expensive to re-steer.
Startups can win by getting specific for narrow prompts and buying-journey stages, but building heavy exposure around one positioning creates risk if they later pivot.
Proof Over Promises, Case Studies, and Marketing When Trust Is Low with Sofia Sulikowski
Episode 244
Wednesday, July 15, 2026 • Duration 01:01:06
In this episode of The Long Game Podcast, Alex Birkett sits down with Sofia Sulikowski, founder of Solera Strategies and former product marketer at dbt Labs, Tableau, and Atlan, to explore why standing out in a saturated content landscape has become a core product marketing problem. Sofia breaks down what "basic marketing" really requires, why internal narratives so often drift from how customers actually describe a product, and how pulling voice of customer language directly from calls and interviews can anchor messaging that actually resonates.
They discuss the erosion of trust in marketing, from products being marketed ahead of their real capabilities to the flattening effect of AI generated content, and why human authored, technical, practitioner led voices are becoming a premium differentiator. The conversation also digs into how case studies are evolving from static logo and metrics templates into technical, narrative driven assets, and why the real measure of a successful customer story is whether the featured customer feels proud enough to share it themselves.
Key Takeaways
Product marketing is fundamentally "basic marketing": identifying what value a product provides, for whom, and how, then aligning every channel and message to that core.
Internal assumptions about a product's value often diverge from real customer usage, which is why product marketing has to synthesize input from product, sales, and customer success teams.
Pulling the exact language customers use, by sitting in on beta calls or QBRs, is an underused but effective way to anchor messaging customers already recognize.
Not every customer quote deserves equal weight; separating signal from noise means understanding who gave a quote, how invested they are, and what their actual outcomes look like.
Companies are increasingly marketing products ahead of their real capabilities, widening the gap between what's promised and what a customer experiences on first use.
AI generated content compresses everything toward the same "average" output, flooding channels with low value content and burying pieces that offer something distinct.
Technical, practitioner authored content, written by the people who did the work and lightly polished by marketing, is outperforming generic thought leadership because it carries real expertise.
Kitchen Side: The Hidden Drivers of AI Search
Episode 243
Wednesday, July 8, 2026 • Duration 51:16
In this Kitchen Side episode, Alex Birkett, Allie Decker, and David Ly Khim unpack how brands should actually measure success in AI search, and why traditional attribution models are breaking down as buyer behavior shifts from search links to AI assistants and workflows.
They discuss why self-reported attribution is becoming the most reliable signal available, the different tiers of AI visibility from simple citations to category-level consensus, and why AI visibility functions more like a brand recall survey than a channel you can optimize in isolation. The conversation also covers the idea of a “post-channel marketer” who coordinates across product, customer education, and PR, and why chasing visibility tactics with no underlying business purpose rarely pays off.
Key Takeaways
Self-reported attribution is more reliable than clickstream data for AI search because most AI-driven visits never result in a tracked click.
AI visibility behaves more like a brand recall survey than a channel, reflecting how a brand compares to competitors rather than something to optimize in isolation.
Picking up a citation on a narrow, low-competition prompt is easy, but shifting broader category-level consensus, like being named among the best CRMs, is far harder and requires changing an entire conversation.
Correcting outdated third-party pricing information moved AI search outputs within about a month in one client engagement, though this was a sentiment fix rather than a visibility gain.
Some AI answers now include negative or qualifying recommendations, meaning a mention doesn't always translate into a positive outcome for a brand.
Different AI tools serve different roles, with ChatGPT and AI Overviews functioning as quick answer engines while agentic tools like Claude Code perform deeper multi-step research, and visibility strategies should account for that difference.
Whether a company should publish a markdown version of its site for easier AI retrieval depends on its audience, mattering more for technical, developer-facing products than for typical B2B SaaS buyers.
Sustainable AI search performance comes from investing in product experience, customer education, and reviews, since genuine third-party validation is difficult to fake once a brand has earned it.
AI as the New Front Door to Brands, Solution Marketing, and Why Your Promise Beats Your Product with Johann Wrede (UserTesting)
Episode 242
Thursday, May 21, 2026 • Duration 59:36
In this episode of The Long Game Podcast, David Khim sits down with Johann Wrede, Global CMO at UserTesting, to explore how AI is reshaping brand perception, the role of the modern CMO, and why truly customer-centric marketing still comes down to diet and exercise.
They discuss why AI has become the new front door to brands — compressing and abstracting how companies are perceived before a human ever visits their site — and how marketers can influence (but never fully control) that narrative. Johann also shares his philosophy on solution marketing over product marketing, the big bets he's making on in-person events, and how he's building agentic marketing workflows to give his team better first drafts without replacing their judgment.
Key Takeaways:
AI has become the new front door to brands, compressing and abstracting brand identity before a prospect ever reaches your website — and marketers can influence this but not control it.
Semantic pre-compression — stripping fluff and using single, precise descriptors — is the most practical way to influence how LLMs represent your brand.
Brand consistency across every customer touchpoint (marketing, sales, support, product) is the only durable lever marketers have in an AI-driven world.
The CMO's role is not just pipeline — it's stewarding how the market understands the company across the entire customer journey, including post-sale.
Solution marketing outperforms product marketing because people spend money to solve problems, not to add tools to their stack.
Listening to sales calls is still the most underutilized source of messaging, positioning, and prompt-tracking insight available to marketing teams.
Agentic marketing workflows — chaining copywriter, persona, humanizer, and CRO agents — can dramatically improve first-draft quality before a human ever reviews the output.
The workplace is shifting from knowledge work to thought work: the value is no longer what you know but how creatively and critically you can think through problems.
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Kitchen Side: The GEO Gold Rush Problem
Episode 241
Wednesday, April 8, 2026 • Duration 48:29
In this Kitchen Side episode, Alex Birkett, Allie Decker, and David Khim unpack the increasingly noisy world of AI search (GEO/AEO), including spam tactics, flawed attribution models, and widespread confusion around what actually drives results.
They explore why visibility without revenue is a trap, how brand sentiment and off-page signals shape AI outputs, and why trust, positioning, and human validation are becoming more important than ever in B2B buying decisions.
Key Takeaways
The SEO vs AEO debate is largely unproductive and distracts from creating real business value.
Spammy, short-term tactics are resurging in AI search due to a “gold rush” mindset.
Many teams optimize for visibility and citations without tying efforts back to revenue or pipeline.
Attribution in AI search is messy, and many current tracking methods are fundamentally flawed.
Large datasets in AEO research can be used to justify almost any strategy or narrative.
Talking directly to customers is still more valuable than inferred data or prompt tracking.
There is a growing tension between experience-based judgment and rapid experimentation with AI.
AI search compresses information, making brand narrative and sentiment more influential than ever.
Visibility is only the first step; positioning and how a brand is described matter more.
AI search is increasingly overlapping with online reputation management (ORM).
Larger brands face greater risk from sentiment manipulation and lack of narrative control.
Off-page signals like reviews, PR, and community discussions heavily influence AI outputs.
Review sites and categorization accuracy can significantly impact visibility and positioning.
Reddit is becoming influential but requires authentic engagement rather than manipulation.
AI-driven discovery is often validated through peer recommendations before purchase decisions.
In this episode of The Long Game Podcast, Alex Birkett sits down with Josh Spilker, Head of Search Marketing at AirOps, to explore how content teams are evolving in response to AI, automation, and changing search behavior. Josh draws on his background in SEO, writing, and systems thinking to outline why traditional content marketing models are breaking down and what’s replacing them.
They discuss the concept of content engineering, including how workflows, brand context, and AI-assisted processes change the way teams create, refresh, and scale content. The conversation also covers identity shifts for marketers, the growing complexity of search surfaces, and where real differentiation and business value are created as content production becomes easier.
Key Takeaways
Content engineering represents a shift from one-off content creation to building systems that manage, update, and scale content across channels.
AI lowers the marginal cost of content, but differentiation still comes from strategy, brand context, and human editorial judgment.
Modern content teams increasingly separate roles between content strategy and content engineering, even if one person covers both in smaller orgs.
The expansion of search surfaces and longer, more contextual queries increases demand for more specific and tailored content.
As traffic becomes less reliable as a KPI, teams need to focus more on conversion quality, brand presence, and downstream business impact.
Kitchen Side: AI Search Tactics, Telemetry and Team Structure
Episode 239
Wednesday, February 4, 2026 • Duration 01:13:46
In this Kitchen Side episode of The Long Game Podcast, Alex and David are joined by Nick Lafferty from Profound to unpack how teams are navigating AI search visibility amid shifting metrics, attribution challenges, and unclear best practices.
They discuss how companies choose which prompts to track, why case studies in AI search are hard to define and share, where brand and citations fit into AI-generated answers, and what organizational bottlenecks are preventing teams from acting on AI search insights.
Key Takeaways
Prompt selection matters, but most teams underestimate how much customer language and internal feedback should shape what they track in AI search.
AI search case studies are difficult to standardize because visibility depends heavily on prompt framing, attribution models, and competitive sensitivity.
Revenue and self-reported attribution remain the most reliable signals as clicks, impressions, and rankings become less dependable.
Problem-based prompts frequently surface brand recommendations, even when users don’t explicitly ask for tools or products.
Citation share acts as an influence layer, shaping future AI responses even when a brand isn’t directly recommended in the output.
Brand-building activities upstream of content can meaningfully impact AI visibility by associating a company with specific problem spaces.
AI search ownership is increasingly cross-functional, spanning growth, SEO, PR, comms, and product marketing rather than a single team.
Internal resourcing and approval processes are major bottlenecks, especially for off-site efforts like Reddit and YouTube.
In this Kitchen Side episode of The Long Game Podcast, Alex Birkett and the team unpack a question that’s coming up more and more: who actually “owns” being found in AI search—and what AI visibility means for modern marketing teams. They explore why the “AI is killing SEO” debate misses the point, and how AI search is collapsing traditional channel boundaries while changing how buyers discover brands.
They also dig into what’s actually being cannibalized (undifferentiated, consensus content), how teams should rethink success metrics as clicks get harder to track, and what the velocity vs. quality debate looks like now—especially as some teams bet on subject-matter depth while others bet on scaled output with AI-assisted production.
Key Takeaways
AI isn’t “killing SEO” so much as reducing the value of undifferentiated, consensus content that used to earn easy traffic.
Losing traffic doesn’t automatically mean losing business value—teams should validate impact through conversions, leads, and pipeline, not sessions alone.
AI visibility is increasingly a composite outcome of everything a company publishes and does (content, comms, brand, product, reviews, community, and customer experience).
Measurement is getting harder as discovery shifts to “dark” channels (e.g., AI tools) and attribution breaks—teams may need new proxies and self-reported attribution.
“Listicles dominate AI citations” may be partly a prompt and sampling bias problem—inputs strongly shape outputs and visibility reporting can be manipulated.
The hardest visibility problem is higher up the funnel: influencing problem-aware searches before buyers even know what category or solution to ask for.
Content teams are splitting into different bets: deep, SME-led quality (often from people who’ve done the job) vs. high-velocity production supported by AI.
A modern in-house writer role trends toward “jack of all trades” output (research, PR-like writing, CEO comms, etc.), using AI to lower marginal cost without collapsing quality control.
One big benefit of running an agency or working at one is you get to see the "kitchen side" of many different businesses; their revenue, their operations, their automations, and their culture.
You understand how things look from the inside and how that differs from the outside.
You understand how the sausage is made.
As an agency ourselves, we're working both on growing our clients' businesses as well as our own. This podcast is one project, but we also blog, make videos, do sales, and have quite a robust portfolio of automations and hacks to run our business.
We want to take you behind the curtain, to the kitchen side of our business, to witness our brainstorms, discussions, and internal dialogues behind the public works that we ship.
AI content programs fail because of weak systems, not the AI itself, so the durable model is human strategy up front, AI in the middle, and a human QA layer at the end.
Customer understanding is the last defensible advantage, and the one part of the content system that should stay human is the empathy to judge whether a piece will actually land.
One big benefit of running an agency or working at one is you get to see the "kitchen side" of many different businesses; their revenue, their operations, their automations, and their culture.
You understand how things look from the inside and how that differs from the outside.
You understand how the sausage is made.
As an agency ourselves, we're working both on growing our clients' businesses as well as our own. This podcast is one project, but we also blog, make videos, do sales, and have quite a robust portfolio of automations and hacks to run our business.
We want to take you behind the curtain, to the kitchen side of our business, to witness our brainstorms, discussions, and internal dialogues behind the public works that we ship.
Case studies are shifting from a template of company background, pain points, and results tables toward a hybrid with technical blog posts that shows the actual "how," including architecture and implementation decisions.
A mature case study program should be organized by goal: heavy hitter logos for credibility, gap fillers for common but underrepresented use cases, and hero stories that make the customer look good.
The clearest signal that a case study succeeded isn't traffic or downloads, it's whether the featured customer is proud enough to organically share their own story with their network.
Marketing teams need a “post-channel” role that coordinates across product, customer success, and PR to influence sentiment and visibility, rather than treating this as an SEO-only or PR-only problem.
Tactics pursued only to move an AI visibility score, with no underlying business purpose, are usually not worth the effort unless they're addressing existing negative sentiment.
One big benefit of running an agency or working at one is you get to see the "kitchen side" of many different businesses; their revenue, their operations, their automations, and their culture.
You understand how things look from the inside and how that differs from the outside.
You understand how the sausage is made.
As an agency ourselves, we're working both on growing our clients' businesses as well as our own. This podcast is one project, but we also blog, make videos, do sales, and have quite a robust portfolio of automations and hacks to run our business.
We want to take you behind the curtain, to the kitchen side of our business, to witness our brainstorms, discussions, and internal dialogues behind the public works that we ship.
One big benefit of running an agency or working at one is you get to see the “kitchen side” of many different businesses; their revenue, their operations, their automations, and their culture.
You understand how things look from the inside and how that differs from the outside.
You understand how the sausage is made.
As an agency ourselves, we’re working both on growing our clients’ businesses as well as our own. This podcast is one project, but we also blog, make videos, do sales, and have quite a robust portfolio of automations and hacks to run our business.
We want to take you behind the curtain, to the kitchen side of our business, to witness our brainstorms, discussions, and internal dialogues behind the public works that we ship.
One big benefit of running an agency or working at one is you get to see the “kitchen side” of many different businesses; their revenue, their operations, their automations, and their culture.
You understand how things look from the inside and how that differs from the outside.
You understand how the sausage is made.
As an agency ourselves, we’re working both on growing our clients’ businesses as well as our own. This podcast is one project, but we also blog, make videos, do sales, and have quite a robust portfolio of automations and hacks to run our business.
We want to take you behind the curtain, to the kitchen side of our business, to witness our brainstorms, discussions, and internal dialogues behind the public works that we ship.
One big benefit of running an agency or working at one is you get to see the “kitchen side” of many different businesses; their revenue, their operations, their automations, and their culture.
You understand how things look from the inside and how that differs from the outside.
You understand how the sausage is made.
As an agency ourselves, we’re working both on growing our clients’ businesses as well as our own. This podcast is one project, but we also blog, make videos, do sales, and have quite a robust portfolio of automations and hacks to run our business.
We want to take you behind the curtain, to the kitchen side of our business, to witness our brainstorms, discussions, and internal dialogues behind the public works that we ship.
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