Unlearn

Building AI startups can make past success a liability when it convinces you that the way you built before is the way you should build next.Sam Kroonenburg knows what it feels like to build at scale. He co-founded A Cloud Guru, helped grow it into a business serving millions of learners, and eventually sold the company to Pluralsight. Now, as CEO and co-founder of Cuttable, he’s back at the beginning: finding customers, testing assumptions, shipping imperfect products, and learning how different company-building looks in the AI era.In our conversation, we explore what Sam had to unlearn when he moved from running a 600-person company back into an early-stage startup. We get into why Cuttable walked away from nearly $1 million in annual recurring revenue, how enterprise customers can pull a young company away from the product it actually needs to build, and why AI is shifting product and engineering teams from owning parts of a system to owning measurable outcomes.Key TakeawaysPast success can distort your judgment: Starting Cuttable reminded Sam that reputation means little if the product does not solve a problem customers will pay for.Experience does not transfer automatically: Sam could trust his instincts when building for engineers, but entering advertising required him to test assumptions and learn from customers.Revenue can hide the wrong product: Cuttable reached nearly $1 million in ARR while engineers were still manually doing work the product was supposed to automate.Taking the long view can require a reset: Sam and his co-founders released every customer, returned to zero revenue, and rebuilt around end-to-end automation.AI shifts the focus from tasks to outcomes: Instead of teaching AI to copy a human process, teams should define the desired result and let AI determine how to achieve it.Additional InsightsSpeed can be an advantage over scale: A Cloud Guru prepared courses before AWS certifications launched, then updated them using real exam feedback.Playfulness can strengthen a product: Short, entertaining lessons made cloud training easier to engage with and helped A Cloud Guru stand out.Paying customers provide stronger signals: Payment shows whether a problem matters enough to solve and makes the customer’s feedback more meaningful.AI is changing team ownership: Cuttable is moving from teams that own system components to teams accountable for measurable customer outcomes.Founder optimism needs a counterweight: Glitch Capital uses analysts and experienced operators to challenge assumptions and add rigor to investment decisions.Episode HighlightsEpisode Highlights00:00 – Episode RecapSam reflects on starting again after a major exit and learning that customers care more about solving their problem than a founder’s reputation.02:13 – Guest Introduction: Sam KroonenburgMeet Sam Kroonenburg, CEO and co-founder of Cuttable, co-founder of A Cloud Guru, and founder investor at Glitch Capital.03:29 – The Moment Technology ClickedSam traces his entrepreneurial journey back to receiving his first computer and learning to code through an online pen pal.09:11 – Winning Through Speed and PlaySam explains how A Cloud Guru used informed bets, rapid updates, and playful learning experiences to move faster than larger organizations.12:26 – Starting Again and Unlearning Founder InstinctMoving from a 600-person company to an early-stage startup forced Sam to test his assumptions and relearn how to earn customer trust.17:51 – Running a Startup Like a Large CompanySam initially brought too much structure, reporting, and sales pressure into Cuttable before refocusing on product-market fit.20:56 – Why Cuttable Went Back to ZeroAfter reaching nearly $1 million in ARR, Cuttable released every customer and rebuilt around end-to-end automation.27:48 – Why Paying Customers Change the SignalSam explains why payment reveals whether a problem matters enough to solve and makes customer feedback more meaningful.29:43 – Stop Teaching AI to Work Like a HumanCuttable learned to give AI the available data and desired outcome instead of forcing it to follow a prescribed human process.34:45 – Organizing Teams Around OutcomesSam explores why engineers focused on customer impact often embrace AI and how Cuttable is organizing teams around measurable outcomes.38:41 – Why Founder Optimism Needs a FilterSam explains how Glitch Capital balances founder optimism with analysts and experienced operators who challenge assumptions.42:03 – Smaller Teams and Bigger ImpactSam shares his optimism that AI will help smaller teams create impact at a scale that once required much larger organizations.FAQsQ1. What did Sam Kroonenburg have to unlearn after building A Cloud Guru?Sam had to unlearn the confidence and operating habits that came with previous success. At Cuttable, he found that reputation could not replace customer value, evidence, or the search for product-market fit.Q2. Why did Cuttable give up nearly $1 million in annual recurring revenue?Enterprise demands were pulling Cuttable toward custom work and manual intervention. When engineers began generating and fixing campaigns themselves, the team released those customers and rebuilt around end-to-end automation.Q3. How does Sam Kroonenburg think AI changes product development?Sam believes teams should spend less time telling AI how to perform a task and more time defining the desired outcome. AI can then use the available data to determine the best method.Q4. Why does Sam prefer feedback from paying customers?Payment shows that a problem matters enough to solve. Free users can offer feedback without genuinely valuing the product, while paying customers provide a stronger signal.Q5. How is AI changing the way Cuttable organizes engineering teams?Cuttable is organizing teams around measurable customer outcomes instead of software components. Teams can use AI and evaluation systems to improve those outcomes continuously.Useful ResourcesCuttable — Sam Kroonenburg’s AI-powered creative production system for advertising.A Cloud Guru — The cloud computing education company co-founded by Sam and later acquired by Pluralsight.Glitch Capital — The Australia and New Zealand founders fund Sam started with fellow founders and operators.AWS re:Invent — Discussed in Sam’s story about how A Cloud Guru prepared training for new AWS certifications at launch speed.Square Peg — Cuttable’s investor, whose response to the company’s decision to reset revenue reinforced Sam’s understanding of seed-stage investing.Artificial Organizations — https://geni.us/artificialorgsRead Artificial Organizations? Leave a review on Amazon - https://www.amazon.com/review/create-review/ref=cm_cr_othr_d_wr_but_top?ie=UTF8&channel=glance-detail&asin=1067626328Follow Barry O'Reilly:LinkedIn: https://www.linkedin.com/in/barryoreillyWebsite: https://barryoreilly.comFacebook: https://www.facebook.com/barryoreillyauthor/X:

AI can accelerate a strong operating model, but when decision rights, incentives, or data are already unclear, it can make the mess spread faster.In this episode, I sit down with Denise Tilles, a leading voice in product operations, to unpack how her career moved from editorial work at Condé Nast into product management, commercial leadership, and eventually product operations. Denise shares how learning to work with revenue data, product analysts, and operating models changed the way she thought about product leadership and led to her work helping enterprise organizations make faster, better-quality decisions.We explore what product operations actually does, why an operating model needs to define how decisions get made, and where incentives can quietly undermine even a well-designed process. We also dig into what happens when AI makes producing documents, specifications, and analysis nearly effortless: generating more output doesn't remove the work of judgment. In many cases, it makes clarity about inputs, outputs, ownership, and what “good” looks like even more important.Key TakeawaysProduct ops should improve decision-making: Denise defines it around business and data insights, customer and market insights, and the operating model.Commercial context changes product thinking: At Cision, learning P&L, ACV, and recognized revenue helped Denise connect product decisions directly to business outcomes.Good analysis reveals hidden opportunities: A product analyst uncovered an add-on opportunity that generated roughly $1 million within a year.Operating models need clear decision rights: Teams need to know what gets worked on, who decides, and how work actually gets done.AI amplifies the system already in place: If ownership, data, or processes are unclear, AI can make those weaknesses spread faster.Additional InsightsInformal decisions can override formal processes: Denise learned that hallway conversations and executive requests often mattered more than the documented workflow.Proof does not create authority: Better analysis can earn credibility, but changing a system still requires ownership, sponsorship, and permission.Incentives shape behavior: Product and sales can both act rationally while optimizing toward conflicting measures of success.AI can shift work downstream: Faster artifact creation still leaves someone responsible for checking the reasoning, evidence, and assumptions.Operations may become more connected: Denise sees product ops, design ops, sales ops, and other functions moving toward a more unified “Omni Ops” model.Episode Highlights00:00 - Episode RecapDenise explains why the starting point for operating-model and AI work should be the pain a company is actually experiencing, from PRD structure to data quality, rather than adopting AI simply because the technology is available.02:01 - Guest Introduction: Denise TillesI introduce Denise Tilles and her work in product operations and operating models, setting up our discussion about data, decision-making, incentives, and how product organizations can operate more effectively.03:13 - From Editor to Product ManagerDenise traces her move from media and content strategy at Condé Nast into product management, a role she initially had to define for herself because the discipline was still relatively young.04:50 - Learning the Economics of ProductMoving to Cision gave Denise access to commercial data she had never had before, pushing her to learn from the CFO and understand product through revenue, P&L, ACV, and business outcomes.08:41 - The Analyst Who Changed the TeamDenise explains how hiring a product analyst gave her team more objective insight into customer and product data, including an overlooked opportunity that generated roughly $1 million in its first year.12:55 - Finding Revenue Hidden in BehaviorI share how an analyst at Lastminute.com identified a collapse in same-day booking conversion after 7 p.m., giving the team a specific customer behavior to investigate and improve through experiments.15:14 - The Three Pillars of Product OpsDenise defines product operations through business and data insights, customer and market insights, and the operating model, all designed to help product managers make faster and better-quality decisions.17:01 - The Process You Don't SeeDenise describes discovering that documented processes were often competing with informal conversations and executive requests, revealing why operating models need to account for how decisions really get made.19:59 - Who Actually Gets to Decide?At its core, Denise says an operating model defines what a company works on, who has the right to decide, and how the work gets done within the organization's real constraints.24:41 - Operating Models Need OwnersDenise warns against treating an operating model as something you publish once and forget, because without clear ownership, maintenance, onboarding, and reinforcement, the system quickly falls out of use.25:48 - Incentives Beat Better ProcessA mismatch between how product and sales were measured taught Denise that people naturally optimize around their incentives, even when that produces conflicting outcomes for the wider business.29:09 - AI Makes Drive-By Requests Harder to RejectDenise explains how a senior leader's opinion can now arrive with an AI-generated specification, data, and outcomes attached, making an untested idea look more rigorous without necessarily improving the underlying thinking.33:22 - Define What Good Looks LikeAs AI increases the volume of work teams can produce, Denise argues that operating models need clearer standards for what should be created, what evidence belongs in it, and how colleagues are expected to consume it.35:33 - Your Output Is Someone Else's InputWe explore the value of looking at work end to end, because one team's output often becomes another team's input and localized optimization can create problems elsewhere in the system.38:56 - Use AI Where the Pain Justifies ItDenise is increasingly advising companies to use less AI than they initially expect, starting instead with the problem, the value AI might add, and the human judgment and context that still need to remain in the loop.41:10 - From Product Ops to Omni OpsDenise looks ahead to a more connected model where operational disciplines work across functional boundaries, allowing companies to design the engine of operations as one system rather than a collection of independent silos.42:09 - Closing ReflectionsI close by encouraging listeners to explore Denise and Melissa's Product Ops and to think of their own organizations as systems that can be deliberately redesigned and experimented on.FAQsWhat is product operations?Denise describes product operations as helping product managers make faster and better-quality decisions. Her model has three pillars: business and data insights, customer and market insights, and the operating model or ways of working that support product teams.What is a product operating model?At its simplest, Denise says an operating model determines what a company decides to work on, who gets to decide, and how the work gets done. Every organization has one in practice, but many have accumulated theirs informally rather than designing and communicating it intentionally.How does AI affect product operations?AI can accelerate activities across product operations, but Denise argues that judgment and context remain essential. When organizations apply AI to an unclear operating model, poor data, or unresolved decision rights, the technology can amplify those existing weaknesses rather than solve them.Why do incentives matter when designing an operating model?People tend to optimize around what they are measured on. Denise experienced this when product was focused on recognized revenue while sales celebrated closed contracts, creating different definitions of success even though both teams were acting rationally according to their incentives.How should a company decide where to use AI in its operating model?Denise starts with the pain points rather than the technology. She looks at issues such as PRD structure, data analysis, source quality, ownership, and decision-making first, then asks whether AI genuinely improves that part of the system and where human judgment still needs to remain.Useful ResourcesProduct Operations — the book Denise co-authored with Melissa on building the systems and capabilities that help product managers make faster and better decisions.a...

AI is forcing us to rethink a question most organizations have avoided for years: what is uniquely valuable about human work when intelligence itself is no longer scarce?Tatyana Mamut, CEO of Wayfound and former CPO of Nextdoor, joins me to explore AI adoption through a lens that goes beyond software. Drawing on her background in anthropology, economics, organizational design, and product leadership, Tatyana explains why the real challenge is designing the rules, tools, norms, incentives, and relationships that shape how humans and AI agents work together.We get practical about what this means at both an individual and organizational level. We explore the human capabilities that become more valuable as AI takes on more routine mental work, why fear leads companies into bad AI decisions, how poorly designed incentives can push agents toward unexpected behavior, and why the subject-matter experts closest to the work need to become responsible for managing the agents operating alongside them.Key TakeawaysHuman comparative advantage is changing: As AI takes on more mental work, people need to rethink where their uniquely human value comes from.Develop taste, judgment, relationships, and performance: Tatyana sees these as practical areas where people can complement AI rather than compete with it.AI adoption is an organizational design problem: Success depends on redesigning rules, tools, norms, incentives, and accountability around the technology.Goals and guardrails can still produce the wrong behavior: An agent may follow its objective while still acting against the organization’s intent, as Tatyana’s refund example shows.Subject-matter experts need to become agent managers: The people closest to the work must be able to monitor, correct, and improve the agents operating in their domain.Additional InsightsOrganizational culture can be consciously designed: Tatyana frames culture as the interaction of rules, tools, and unwritten norms that shape behavior.Relationships become more valuable as outreach gets automated: When AI increases the volume of generic communication, trust and genuine relationships become more important.AI can generate options, but humans still decide what matters: Judgment becomes critical when the challenge shifts from what can be done to what is worth doing.Small failure rates compound across multi-agent systems: Edge cases that seem minor with one agent can become more significant as agents are connected into workflows.Building an agent and managing one are different jobs: Engineering can help create the agent, but functional owners must take responsibility for its ongoing performance.Episode Highlights00:00 - Episode RecapTatyana frames the AI shift as a deeper question about what it means to create value as a human when intelligent systems increasingly share responsibility for thinking and decision-making.02:10 - Guest Introduction: Tatyana MamutI introduce Tatyana Mamut, CEO of Wayfound and former CPO of Nextdoor, whose experience across anthropology, organizational design, and product leadership shapes her approach to human and AI systems.05:06 - From Economics to AnthropologyTatyana explains how the failure of economic models to predict events such as Russia’s 1998 economic collapse pushed her toward studying the mental models and social institutions that shape human behavior.08:07 - Designing Culture Through Rules, Tools, and NormsTatyana breaks organizational culture into practical components, showing how formal rules, available technologies, incentives, status, beliefs, and unwritten norms interact to shape behavior.13:16 - The Multi-Sapiens WorkplaceTatyana argues that AI is forcing people to reconsider their comparative advantage as humans as intelligent systems begin participating in higher-level thinking and decision-making.18:42 - Four Human Capabilities to DevelopTatyana identifies taste, judgment, relationships, and performance as four areas people can strengthen as AI absorbs more routine mental work.23:32 - Why Human Relationships Matter MoreBarry and Tatyana discuss how automation can remove administrative work while making authentic relationships, lived experience, trust, and storytelling increasingly valuable.27:21 - Fear Is the Enemy of ProgressTatyana explains how fear can push organizations either to rush into AI using outdated assumptions or retreat when the technology does not behave like traditional software.29:11 - AI Agents Need Different SupervisionTatyana describes why AI agents require governance and oversight based on organizational context, including goals, rules, norms, and definitions of what good performance looks like.31:32 - The Incentive ProblemThe conversation turns to how model behavior and organizational goals interact, including Tatyana’s view that sycophantic behavior can create unexpected feedback loops in production agents.32:34 - When an Agent Breaks the Intent of the RuleUsing a customer service example, Tatyana shows how an agent trying to maximize case closure and customer satisfaction can suggest a refund even when its guardrails tell it not to.38:59 - Escaping Endless Pilot ModeBarry explores why uncertainty can keep organizations trapped in experimentation rather than allowing AI systems to move into real operating environments.40:10 - Who Owns the Agent After Deployment?Tatyana argues that the functional experts closest to the work need direct responsibility and visibility once an AI agent is operating in production.44:41 - Treat AI Agents Like New EmployeesTatyana compares engineering teams to recruiters who can help bring an agent into the organization, while the business owner remains responsible for coaching, compliance, and ongoing performance.46:14 - Closing ReflectionsBarry closes by reflecting on the emerging skills leaders and individuals will need as they learn to manage systems in which humans and AI agents increasingly work together.FAQsQ1. What human skills become more valuable as AI takes on more work?Tatyana highlights four areas: taste, judgment, relationships, and performance. Her argument is that AI may generate options, perform routine mental work, and help with administration, but humans still create value by deciding what is good, what is worth doing, whom to trust, and how to motivate or persuade other people.Q2. What does Tatyana Mamut mean by a “multi-sapiens workplace”?She uses the phrase to describe a workplace where humans are no longer the only entities carrying out higher-level thinking and decision-making. In that environment, organizations need new rules, tools, and norms for determining how humans and AI systems work together and where responsibility sits.Q3. Why does Tatyana say AI adoption is an organizational design challenge?Because deploying an AI system changes more than the technology stack. Organizations also have to consider incentives, governance, supervision, communication, accountability, cultural norms, and who has the authority to evaluate and correct an agent’s behavior.Q4. Why do AI agents need supervision even when they have guardrails?Tatyana explains that an agent can technically pursue its assigned goal while still behaving in a way the organization did not intend. Her customer service example shows an agent suggesting a refund because doing so could resolve the case and improve customer satisfaction, despite instructions designed to prevent refunds.Q5. Who should manage AI agents after they are deployed?Tatyana argues that responsibility should move toward the subject-matter experts who understand the work the agent performs. Engineering or IT may build and deploy the system, but salespeople should oversee sales agents, finance teams should oversee finance agents, and other functional experts should have direct access to monitor, correct, and improve their agents.Useful ResourcesTanyana Mamut on LinkedIn - https://www.linkedin.com/in/tmamut/ Wayfound - https://www.wayfound.ai/ Artificial Organizations - https://geni.us/artificialorgs Nextdoor - https://blog.nextdoor.com/ Claude - https://claude.com/ Salesforce -

Before leaders can redesign their organizations with AI, they have to reconsider how they personally think, work, and make decisions.In this special episode of Unlearn, Barry O’Reilly shares the opening chapter of the audiobook edition of Artificial Organizations, narrated in his own voice. After hearing from readers who wanted another way to experience the book, Barry spent four days in the studio bringing its ideas and stories to life. The process was rewarding, demanding, and personal. As someone who is dyslexic, reading every word aloud required a different kind of focus from delivering a keynote, teaching a workshop, or hosting a podcast conversation.Barry then takes listeners into the central argument of the book: organizations often begin AI adoption with licenses, pilots, and tools while leaving leadership behavior and decision-making systems unchanged. Drawing from his own experiments, leadership research, and work with senior teams across North America, Europe, and Asia, he explains why meaningful AI transformation starts with human traits, high-value tasks, and the judgment leaders must preserve before selecting technology.Key TakeawaysAI should strengthen judgment, not simply increase output: Machines can process and synthesize information at a scale no individual can match, but leaders still have to decide what matters. The opportunity is to use AI to prepare, capture, and pressure-test thinking so human attention stays focused on consequential decisions.Start with traits, then tasks, then tools: Barry’s 3T model begins with how a leader naturally thinks, creates, communicates, and decides. Only after identifying the tasks where that judgment creates the most value should leaders choose tools to support the work.Decision velocity must be paired with decision advantage: Decision velocity is how quickly a leader moves from question to insight, decision, and action. Decision advantage is the quality and depth of context behind that choice; speed without insight creates chaos, while insight without timely action becomes irrelevant.Most leadership time is allocated away from leadership value: Barry describes an 80/20 mismatch in which meetings, updates, administration, and context reconstruction consume most of a leader’s time, while the greatest value comes from framing problems, evaluating tradeoffs, and making high-stakes decisions.Organizational AI adoption begins with personal behavior change: Broad mandates, task forces, and tool rollouts are often the wrong starting point. Leaders build more credible adoption when they experiment in their own workflows, share what they learn, and make new behaviors visible to their teams.Additional InsightsPresence can be more valuable than productivity: Barry’s first AI experiment used a meeting assistant to capture and synthesize conversations. The practical gain was not only faster preparation and follow-up; it allowed him to stop carrying every detail in his head and remain fully present during critical discussions.Experience becomes a liability when it is not augmented: The instincts and institutional knowledge that helped leaders succeed can become limiting when they are treated as sufficient. Experience keeps compounding only when leaders combine it with broader recall, faster synthesis, and continuous testing.Senior leaders need psychological safety to learn: Barry’s executive study found that leaders preferred one-on-one coaching and small peer cohorts over large workshops or self-paced courses. Being a beginner is uncomfortable at senior levels, so smaller environments make experimentation and honest questions easier.New capacity should create thinking space, not more workload: In the Progeny case study, AI reduced the effort required to capture meetings, actions, owners, and deadlines. CEO Peter Nevsky framed the resulting capacity as time for strategic thought and better decisions rather than an invitation to place people on a faster operational treadmill.The unit of change is the judgment inside a role: AI may automate parts of a project manager’s, analyst’s, or executive’s work while increasing the importance of interpretation, challenge, and decision quality. The role may remain, but the judgment required within it changes.Episode Highlights00:00 – Episode Introduction & Why I Created the AudiobookBarry introduces this special preview of the Artificial Organizations audiobook, shares why he chose to narrate it himself, and explains why leaders need a different approach to AI adoption through the 3T framework: Traits, Tasks, and Tools.04:33 – A Quick Favor Before We BeginBarry invites listeners to leave an Amazon review, recommend the audiobook to their network, and help more leaders discover its ideas.05:16 – Preface: Why the Way We Work Is BrokenBarry introduces the central challenge facing modern organizations: leaders are overwhelmed by information, while better decisions remain harder than ever.07:11 – Judgment Under PressureBarry explores why more data, more tools, and more technology haven't created better leadership, arguing that the real constraint is our ability to process information and make sound judgments.10:07 – The First AI Leadership ExperimentA simple experiment with an AI meeting assistant transformed Barry's leadership by improving clarity, presence, and decision-making, leading to a new perspective on AI's role.12:00 – AI as Judgment InfrastructureBarry reframes AI as more than a productivity tool, explaining how it strengthens judgment, improves decisions, and helps leaders focus on what matters most.16:07 – Part One: The Judgment ConstraintBarry introduces the first section of the book, explaining why AI creates little value unless it changes how leaders think, decide, and lead.18:08 – Chapter One: Your Legacy Is Now Your LiabilityBarry examines why experience alone is no longer enough and how decision velocity and continuous learning are becoming the defining advantages of modern leadership.22:20 – The AI ROI Blind SpotBarry challenges the common focus on efficiency and cost reduction, arguing that AI's greatest value lies in helping leaders make better and faster decisions.28:16 – From Linear Leadership to Exponential InnovationBarry explains why traditional leadership models struggle in the AI era and why organizations must adopt new ways of learning, experimenting, and making decisions.34:10 – What Makes an Artificial OrganizationBarry defines artificial organizations and shares how leaders can replace memory-based management with shared judgment systems that accelerate decision-making and collaboration.40:09 – The New Leadership DivideBarry explores the widening gap between leaders who actively experiment with AI and those who continue relying on legacy ways of working, and why that difference will compound over time.43:15 – Where to StartBarry explains why successful AI transformation begins with personal experimentation, encouraging leaders to model new behaviors before scaling change across their organizations.45:15 – Closing ReflectionsBarry concludes the first chapter, thanks listeners for joining this special audiobook preview, and invites them to continue the journey with Artificial Organizations.FAQsQ1. Is this Unlearn episode an audiobook preview?Yes. This special episode includes Barry O’Reilly’s recorded introduction followed by the opening chapter of the audiobook edition of Artificial Organizations, narrated by Barry himself. He explains why the audio edition was created, what recording the book required, and how its ideas connect to his work with executive leaders and teams.Q2. What is an artificial organization?An artificial organization is a company that deliberately combines human and machine intelligence to redesign how context is captured, information is synthesized, and decisions are made. Instead of adding AI to the edges of existing workflows, it builds judgment infrastructure into how the organization operates.Q3. How can leaders use AI to make better decisions?Leaders can use AI to capture conversations, summarize context, test assumptions, explore scenarios, prepare for meetings, and identify unresolved actions. This reduces the information leaders must carry mentally and gives them more capacity to focus on tradeoffs, consequences, and high-value judgment.Q4. What is the difference between decision velocity and decision advantage?Decision velocity is the speed at which a leader moves from a question to insight, decision, and action. Decision advantage is the quality, accuracy, and depth of context behind that decision. Strong leadership requires both because speed without insight creates chaos, while insight without action loses relevance.Q5. Why do many enterprise AI initiatives fail to create business value?They often begin with tool purchases, pilots, and mandates without redesigning how work, context, and judgment flow through the...

AI is making it easier than ever to build products, automate work, and scale ideas. The challenge is no longer access to technology. It's designing the systems, stories, and customer understanding that turn AI into real business outcomes.In this episode of Unlearn, I'm joined by Eric Baxley, Chief Marketing Officer at Nobody Studios. Eric shares how a seventh-grade summer teaching himself to program on an Atari 800 sparked a 30-plus year career spanning software development, product management, marketing, sales, business development, and partnerships.We explore what Eric has had to unlearn while building companies in the AI era—from challenging assumptions before execution and replacing corporate polish with authentic storytelling, to designing scalable systems instead of disconnected tools. Along the way, Eric explains why great marketing still starts with deeply understanding customers, and why AI works best when it amplifies human judgment rather than replacing it.Key TakeawaysChallenge the foundation before execution: Eric shared that in a previous large business, the team was “doing things right,” but not “doing the right things.” The segmentation was off, and correcting who they were really going after helped the team hit its goals for the next three years.Authentic stories build trust: Eric has had to unlearn the corporate habit of making everything polished before sharing it. He believes people respond when you show the journey, talk about the hardships, and ask for feedback along the way.Systems matter more than scattered tools: Eric warned against building a set of siloed point products. In a venture studio building multiple companies at once, he said you need systems that can scale, especially with the speed of AI.Use AI to strengthen your thinking, not replace it: Eric described writing and revising a LinkedIn post himself before turning to AI. The post performed well, and his takeaway was that people could sense it was naturally written and not simply generated.Real messaging starts with real customers: When Eric joined Nobody Studios, one of the first things he did was speak directly with patent attorneys for Evalify. That helped him understand their language, their concerns, and what message would actually resonate.Additional InsightsStorytelling has to match the person and the moment: Eric explained that messaging should change based on persona, stage in the buyer journey, industry, and country. A CIO, CTO, or chief product officer may each need to hear the story differently.The right message needs different levels of depth: Eric talked about having a 30-second version, a 90-second version, and a longer version of the message ready. The point is to be prepared for the amount of attention and context the listener actually has.B2B messaging has to speak to head, heart, and wallet: Eric said that in larger enterprise deals, especially six- or seven-figure buying decisions, messaging needs to appeal to logic, emotion, and business economics.Consistency across channels is hard but necessary: Eric noted that messages can quickly become confusing when sales, TikTok, Instagram, LinkedIn, and other channels are all saying different things. The challenge is keeping the story consistent while still tailoring it to the audience.Automation still needs personalization: Eric uses some automated systems, but he emphasized that he still fine-tunes and personalizes messages based on someone’s background. Otherwise, outreach becomes the kind of generic message people ignore.Episode Highlights00:00 - Episode RecapEric Baxley explains why company building in the AI era requires scalable systems, curiosity, grit, and a willingness to dig into the details rather than staying at a high level.01:53 - Guest Introduction: Eric BaxleyBarry introduces Eric Baxley, Chief Marketing Officer at Nobody Studios, and highlights his work across growth, marketing, partnerships, and company building.03:13 - The Seventh-Grade SparkEric shares how teaching himself to program on an Atari 800 while growing up in Germany sparked his interest in technology and shaped the rest of his career.05:10 - Unlearning AssumptionsEric explains why he no longer assumes the base foundation of a business is solid, using a segmentation mistake from a large business as an example.06:20 - Moving Past Corporate PolishEric talks about unlearning the need for everything to be buttoned up and why showing the real journey can make the work more relatable.07:39 - Authentic Stories in a Noisy MarketBarry and Eric discuss why honest stories about what is working, what is difficult, and what is still being learned can stand out from exaggerated AI claims.10:48 - Tailoring the StoryEric breaks down how messaging needs to be adapted by persona, buyer journey stage, industry, and country.13:24 - Learning From Patent AttorneysEric shares how he started shaping Evalify’s messaging by speaking directly with patent attorneys instead of creating sales and marketing materials in a vacuum.18:08 - Head, Heart, and WalletEric explains why enterprise messaging needs more than a clinical problem-and-solution structure; it also needs emotion, business value, and a clear story.22:40 - Building Systems Backwards From the CustomerEric talks about the explosion of marketing technology and why he starts with the persona, the outcome, and the channels where customers actually spend time.27:37 - Writing Before AIEric describes how he wrote and revised a LinkedIn post himself before involving AI, and why he believes the human work helped it resonate.31:48 - Human-in-the-Loop AI for Patent AttorneysEric explains how Evalify helps patent attorneys with work that can take 20 to 30 hours, while making clear that the product amplifies their work rather than replacing them.33:49 - Building Companies Faster and More FrugallyEric shares why he is excited that small teams can now use AI capabilities to build, fund, and scale companies differently than in the past.35:36 - Closing ReflectionsBarry thanks Eric for sharing lessons from his work at Nobody Studios and looks forward to continuing to build together.FAQsQ1. Who is Eric Baxley?Eric Baxley is the Chief Marketing Officer at Nobody Studios. In the episode, he describes a career that began in software development and later moved into product management, marketing, sales, business development, and partnerships.Q2. What does Eric Baxley say leaders need to unlearn?Eric says he has had to unlearn assuming the foundation is already right, relying too much on corporate polish, and building around siloed tools instead of scalable systems.Q3. Why does Eric Baxley focus so much on customer segmentation?Eric believes many teams jump straight to execution without checking whether they are going after the right customers. He shared an example where fixing segmentation helped a business focus on the right audience and hit its goals.Q4. How does Eric Baxley approach messaging for AI products?Eric starts by talking to the people the product is meant to serve. With Evalify, he spoke with patent attorneys, used their language, tested the message, and worked with an advisory board to see whether the story resonated.Q5. What role should AI play in marketing, according to this episode?AI can help with speed, systems, and efficiency, but Eric and Barry emphasize that human judgment still matters. Eric’s examples show that personalization, customer understanding, and careful writing are still needed for the message to land.Useful ResourcesEric Baxley on LinkedIn - https://www.linkedin.com/in/ericbaxley/ Nobody Studios on LinkedIn - https://www.linkedin.com/company/nobodycrowd/ Nobody StudiosEvalifyThe Challenger SaleArtificial Organizations - https://artificialorganizations.com/ Follow the HostBarry O’Reilly on LinkedIn: https://www.linkedin.com/in/barryoreillyBarry O’Reilly’s website: