Episode #576: From a $12K Monthly Bill to Zero: Owning Your AI Stack and Your Data

In this episode of the Crazy Wisdom Podcast, host Stewart Alsop sits down with Devin Elliot, founder of Monetic xyz, to explore the evolving landscape of AI development, decentralized infrastructure, and the future of software engineering. The conversation ranges from Devin's decision to move away from Anthropic's services—cutting his monthly spend from $12,000 to zero—to his early adoption of "vibe coding" in 2022 and his extensive experience building with cursor and custom AI infrastructure. They discuss the technical architecture behind privacy-preserving systems, the value of embedders and fine-tuning, the economics of frontier AI companies, and why Devin believes the internet is becoming increasingly inhospitable to humans while agents take over. Along the way, Devin shares insights from his decades in engineering, his background in crypto since 2011, and his vision for a self-custodial future where users control their own data through content-addressed systems and encrypted safe tensor files rather than centralized databases. Key Insights1. Devin Elliott transitioned from traditional engineering to vibe coding in 2022 after fifteen years of experience, recognizing it as a fundamental cycle shift rather than resisting it. He reduced his Anthropic spending from $12,000 monthly to zero, partly due to disagreement with their public communications and partly because he could migrate his entire codebase to other platforms. His approach was strategic: he deliberately chose Cursor as a bridge tool despite knowing it would cost more in tokens, prioritizing speed and continuity over constant tool-switching. By staying focused on one platform, he avoided the productivity drain of repeatedly learning new interfaces while others chased the latest options.2. Elliott's token usage was extraordinarily high, placing him in the top 0.2% of Cursor users with 25 billion tokens processed. His heavy consumption stemmed from spending tokens on priming context rather than reusing prompts or relying heavily on rules. His background as an architect rather than a pure engineer meant he focused on understanding architectural patterns and system design rather than memorizing specific function implementations. By investing heavily in context priming, he achieved near one-shot execution where the AI could implement complex features like WebSocket servers correctly and efficiently without multiple iterations.3. The lack of engineering standards in software development has always been problematic, but AI coding amplifies this issue significantly. Most software engineers cannot explain fundamental architectural concepts like model-view-viewmodel or understand why different frameworks embody different state-handling philosophies. Engineers often debate which framework to choose without understanding the underlying engineering principles that make each suitable for specific purposes. If buildings were constructed like software is written, they would be structurally unsound and dangerous. This knowledge gap becomes more critical with AI coding because the quality of output depends heavily on understanding architectural patterns to guide the AI effectively.4. The future of the internet will likely become inhospitable to human users as bots gain increasingly efficient access at lower levels than human interfaces allow. Elliott observed this in DeFi through MEV (miner extractable value) where bots could intercept and extract value from transactions before humans could complete them. He predicts the internet will evolve into a space primarily for agent-to-agent interaction, with humans interacting through local AI systems that venture into this hostile environment on their behalf. These personal agents would operate through proxy environments that can be attacked or compromised without exposing the user's actual systems or data.5. Centralized AI services like Anthropic face fundamental architectural problems that decentralized approaches can solve more effectively. The traditional Silicon Valley model of centralizing user data in company databases to deliver personalized services cannot achieve true privacy preservation or meaningful personalization. This centralization model failed during the Internet of Things wave when consumers rejected giving their data to companies like Samsung just to receive basic functionality from their own devices. Elliott built his entire system around self-custodial, privacy-preserving architecture using local GPU racks and custom models, demonstrating that individuals and businesses can achieve AI capabilities without surrendering data to centralized providers.6. Elliott developed a novel approach using embedders and content-addressed systems instead of traditional databases to handle data more efficiently and securely. Embedders are essentially LLMs with different final layers that convert text to vector space representations, and they can run locally with zero cost while generating tokens hyper-fast. He stores data as encrypted safe tensor files in blob storage rather than Postgres databases, allowing individual ownership, licensing, and sale of personal data. Users could compile their Facebook, Google, and social media data into valuable inventory files they control and potentially sell directly to buyers, bypassing traditional data brokers while maintaining privacy through encryption.7. The system Elliott built over a year represents approximately 80,000 engineering hours of work that would have been impossible before AI coding capabilities emerged. His core system contains 3.5 million lines of quality code that passed specifications, with the account system alone comprising 250,000 lines. He successfully created an AI agent that autonomously set up an LLC in Wyoming, completing all paperwork, payment, and registration using biometric signatures for authorization. The architecture is built on a "thin client, fat protocol" model with extensive infrastructure supporting agent operations, including WireGuard-based mesh networks, custom security implementations, and seed files that can instantiate entire systems when copied to new locations. Timestamps00:00 Devin discusses leaving Anthropic after spending $12,000 monthly, moving entirely to cursor for vibe coding after adopting it in 2022 with fifteen years engineering experience behind him05:00 Token management strategy explained through staying with cursor despite higher costs, choosing speed over switching tools while burning through $200 plans in fifteen minutes reaching 25 billion tokens10:00 Engineering background shapes approach to context priming over reusable prompts, focusing on architectural patterns rather than memorizing native functions to achieve one-shot implementation15:00 Compiler determinism discussed through donut-to-car mutation analogy, explaining how LLMs improved through better tool chains and loop checking rather than transformer changes20:00 Internet of Things failure traced to centralized data architecture, arguing Ring camera backlash preceded GDPR and revealed impossibility of true privacy through centralized servers25:00 Personalization requires decentralization as centralized algorithms need users staying static, conflicting with human ability to improve daily and make incremental changes30:00 Local agent architecture described using WireGuard mesh networks and proxy cloud environments, enabling bots to safely interact with hostile Internet while protecting local systems35:00 Anthropic's $42 billion losses and $8 billion monthly revenue discussed alongside server infrastructure value, questioning centralized model sustainability as decentralization pressure increases40:00 Seed file system explained where single file instantiates across devices, reaching back through mesh networks to access compute resources like rack of A100s45:00 Fine-tuning requirements range from 10,000 to 100,000 samples typically, though custom embedder training used 40 million samples to make geometry-respecting vector space50:00 Agent autonomously filed Wyoming LLC using biometric signatures and custom-built secure enclaves, demonstrating 80,000 engineering hours compressed into one year55:00 Data broker alternative proposed using safe tensor files in blob stores instead of databases, letting users own and sell their complete data inventory through encrypted vaults LinksMonetic

In this episode of the Crazy Wisdom Podcast, host Stewart Alsop sits down with UI designer Peter Petrash to explore the evolving landscape of user interface design and technology. Peter walks through his journey from discovering the early web on GeoCities to working with gaming companies in the nineties, and how his background shaped his current perspective on design. The conversation moves from the technical foundations of web design to deeper philosophical territory, examining how AI is transforming creativity, whether machines can truly understand taste and relevance, and what happens when human imagination becomes the bottleneck instead of technology. They discuss everything from self-driving cars in San Francisco and urban design principles to the future of rendering engines, the meaning crisis in an age of infinite content generation, and why formally defining goals often backfires—all while considering what it means to design interfaces when the full breadth of imagination can finally be unleashed on screen. Key Insights7 Key Insights from Peter Petrash's Episode:1. The evolution of UI design has come full circle from requiring deep technical knowledge to becoming accessible through AI coding tools. Petrash describes starting in the Geocities era when designers had to master HTML, CSS, deployment, and package management just to get anything online. Today, tools like Claude handle most of the technical overhead, lowering barriers to entry. However, this democratization creates a new challenge where design principles and aesthetic judgment become more critical differentiators than coding ability.2. Visual design operates on fundamental principles rooted in human cognition, not just subjective taste. Petrash explains gestalt principles of visual perception, such as the law of proximity where elements placed close together are perceived as related, and common fate where animated movement helps brains track relationships. These principles represent an assembly code for visual design that could theoretically be formalized and validated, similar to how code is verified, though this remains largely unexplored territory for AI systems.3. The convergence of rendering technologies HTML, SVG, 3D, shaders, and now AI video generation points toward a future where any imaginable visual can be rendered on screen. Petrash describes emerging AI video models that can generate interactive, realistic environments without traditional rendering engines or lighting calculations. This represents a fundamental shift where the technical constraints that historically limited designers are disappearing, making imagination and taste the primary bottlenecks rather than technical capability.4. Voice interfaces and multimodal AI create opportunities for capturing nuanced human communication that text misses. Petrash notes that when speaking spontaneously, humans convey uncertainty through pauses, vocal modulation, and hesitation patterns that current AI systems largely ignore through transcription to text. He envisions AI that could detect these subtle cues and adjust responses accordingly, offering educational context when uncertainty is detected or providing more confident direction when the user seems assured.5. The professionalization of UI design creates a constant critical awareness of environmental design flaws that can become psychologically taxing. Petrash describes how training in user experience design makes designers perpetually scrutinize their surroundings, noticing poor design choices like Norman Doors that have push bars but require pulling. This occupational hazard of seeing flaws everywhere extends beyond digital interfaces to urban planning, architecture, and physical products, requiring conscious effort to disengage from this analytical mode.6. The democratization of creative tools through AI is creating an attention economy where imagination itself may become currency, but also risks imaginative exhaustion. Petrash reflects on how technology has finally caught up to human imagination after decades of the reverse being true, but now finds himself coming up dry creatively as everyone's imagination is simultaneously unleashed on platforms like Twitter. This creates a paradoxical situation where infinite creative possibility collides with limited human attention and the challenge of generating meaningful novelty rather than novelty for its own sake.7. Goal setting and life planning tools built on AI fundamentally misunderstand how human motivation and meaning work, revealing limits in artificial intelligence despite mathematical prowess. Petrash describes how apps attempting to help users identify and achieve goals through explicit articulation and reminders often fail because formalizing goals in writing strips away essential implicit elements. This Jungian insight suggests that bringing things fully into intellectual spotlight loses their essence, and that true intelligence involves navigating unconscious dimensions that current AI systems cannot access despite solving complex mathematical problems. Timestamps00:00 Peter's journey into UI design begins with discovering Mad Magazine's website as a kid, building on Geocities with HTML and inline styles05:00 Gaming journalism and visiting Valve Software to see Half-Life in alpha, learning technical skills alongside artistic vision10:00 Modern vibe coding versus old technical barriers, design becoming more important as AI handles coding tasks15:00 Gestalt principles and visual perception laws like proximity explain how humans cognitively process interfaces20:00 Voice UI experiences and capturing uncertainty through speech patterns, the potential for AI to detect hesitation25:00 Norman Doors illustrate bad design everywhere, how designers constantly scrutinize their environment for flaws30:00 Urban design comparing San Francisco's organic growth to Barcelona's centralized planning and Buenos Aires architecture35:00 Building new cities takes generations, Brasilia and Chandigarh show problems with inorganic hyperfunctional design40:00 Self-driving cars create social consciousness about gig economy impacts while improving safety dramatically45:00 Argentina's robotics testing environment and Milei's transhumanist views including cloning his dogs50:00 Interactive video models eliminate traditional rendering engines, creating persistence challenges for world state memory55:00 Imagination becoming the bottleneck as technology catches up, future currency being taste and creative vision60:00 Explicit goals fail when formalized, LLMs lack true intelligence despite reasoning capabilities and test-time compute LinksMad Magazine WebsitePete's Personal Website

In this episode of the Crazy Wisdom Podcast, host Stewart Alsop sits down with Ryan Marsh of thestack.io to explore what it really takes to build production AI systems. They discuss how production AI has evolved from simple prompt-to-API demos into complex systems requiring evaluation suites, human-in-the-loop feedback mechanisms, and sophisticated approaches to handling context and data retrieval. The conversation covers the challenges of domain mapping, the fundamental difficulty of translating messy human business processes into structured systems, and the role of ontologies in AI development. Ryan and Stewart also examine the limitations of LLMs, the debate between specialization versus generalization in AI models, consciousness and cognition in system design, and the regulatory landscape facing AI companies. They touch on infrastructure constraints, the democratization of AI through open source models, and whether we'll eventually hit a ceiling where human intelligence can no longer distinguish between increasingly capable AI models. Key Insights1. Production AI systems today fundamentally differ from demos through their reliance on comprehensive evaluation suites that function like unit tests to measure and maintain quality, though they cannot be as deterministic. The key distinction is that production systems require clearly defined metrics for what good looks like, along with feedback mechanisms that allow the system to evolve over time. Without this foundational understanding of success metrics and continuous improvement processes, a system is not truly production-ready regardless of how many users it serves.2. The fundamental challenge in building production AI systems is not the technology itself but rather mapping business domains into structured formats that models can work with effectively. This problem of translating messy, subjective human processes and language into precise specifications has plagued software engineering for decades. Different people within organizations use the same words to mean completely different things, and humans naturally operate with assumed context and imprecision that must be explicitly defined for AI systems to function reliably.3. Large language models excel at generalization but struggle with specialization, which creates friction in production environments where specific outputs or styles are required. While they can code in any programming language, getting them to write code exactly the way a particular engineer wants remains extremely difficult. This explains why professional documentation and specialized coding tasks often require extensive prompting and fighting with the models, as they naturally gravitate toward their trained patterns rather than highly specific user preferences.4. Modern production AI systems primarily solve classification problems wrapped in natural language interfaces rather than requiring true open-ended cognition. The models work best when they can leverage reasoning over provided information to make verifiable decisions, but they still lack common sense despite their vast knowledge. Success comes from teaching models everything about your specific domain and what good and bad outcomes look like, rather than relying solely on their general intelligence.5. Context retrieval in production AI systems is fundamentally a data storage, search, and retrieval problem that has been solved many different ways throughout computing history. The appropriate solution depends entirely on the type of data being accessed, whether through vector databases, graph databases, relational databases, or even simple text search. The harnesses and frameworks for orchestrating AI agents have matured significantly, making the real challenge the quality and structure of the data being fed to these systems.6. Human-in-the-loop feedback mechanisms are essential for production AI because models will inevitably encounter situations they have not been trained to handle. When confidence is low or novel scenarios appear, systems should flag these for human review rather than proceeding blindly. The feedback provided during these interventions must be captured and scored so it can be incorporated into the permanent behavior of the system, creating a continuous improvement cycle similar to how model vendors perform reinforcement learning on their base models.7. The rush to regulation in the AI industry is driven primarily by the fact that these companies are currently completely exposed to existing consumer protection and liability laws with no legal precedents to protect them. If AI agents cause harm, companies could be sued into oblivion under current law. By establishing compliance frameworks through regulation, these companies can create carve-outs and exemptions that limit their liability when they follow prescribed rules, similar to how heavily regulated industries like airlines and banking have become nearly impossible to enter due to compliance requirements. Timestamps00:00 Welcome and introduction to Ryan Marsh discussing production AI systems and what companies need to understand about building them at scale05:00 The cognitive load challenge of working with invariants and probabilistic systems, discussing how LLMs function like PhD students without common sense10:00 Building eval suites to measure AI performance, handling the long tail of edge cases in complex contracts using human-in-the-loop feedback systems15:00 Context as a data retrieval problem and the fundamental challenge of mapping business domains when humans struggle with imprecision and ambiguous language20:00 How different departments use the same words with different meanings and why LLMs generalize well but don't specialize effectively for specific coding styles25:00 The ontology debate and intractable problem of mapping subjective human systems to rigid structured formats with diminishing returns on perfect mapping30:00 Why humans struggle distinguishing what is from what ought to be and the challenge of creating SOPs when companies lack updated documentation35:00 The liability exposure AI companies face and why they're begging for regulation to protect themselves from existing consumer protection laws40:00 Open source knowledge transfer between Chinese and American labs, chips and power as the real constraint not algorithms for frontier models45:00 Reaching intelligence ceiling where specialists can't distinguish state-of-art models and fundamental physical laws limiting LLM scaling through layered optimization strategies LinksWebsiteX

Stewart Alsop sits down with Antoine Marcel, CEO and cofounder of Flourish, to explore the future of home robotics and what he calls "solarpunk" automation. Antoine discusses why Flourish is building wheeled robots with arms instead of following the humanoid hype, arguing that the expensive legs and teleoperation approach isn't practical for real consumers today. The conversation ranges from the philosophy of robots that disappear when you're home (rather than becoming constant companions) to the bureaucratic nightmares of modern life that robotics might actually solve. Antoine explains how their $3,500 robots can be trained in thirty minutes using just your phone to handle personalized household tasks, and why saving people two hours a day—not just ten minutes—is the threshold for consumer adoption. They also dig into whether Paris will become a solarpunk or cyberpunk city, why Apple represents solarpunk design principles, and how the first 50 Flourish units are about to ship worldwide at flourish-robots.com. Key Insights1. The core innovation behind Flourish is building mobile-based robots with arms but no legs, moving on wheels rather than attempting humanoid locomotion. This design choice stems from practical considerations since even humanoid robots cannot reliably handle stairs, and legs represent approximately twenty thousand dollars of the cost in humanoid platforms. By eliminating legs, the company can focus on delivering functional value to consumers today rather than developing research products. The founder emphasizes that accessibility and usefulness are paramount, rejecting the demonstration-focused approach that dominates the robotics industry where companies raise billions on teleoperated demos that never ship to actual consumers.2. The founder's background in automation across logistics, software, and sales led him to recognize that the most painful tasks in life occur outside the computer. After automating digital work processes, he realized that returning home after a long workday to clean, cook, and tidy remained exhausting and unaddressed by existing technology. This insight, combined with advances in physical AI over the past three years, drove him to develop the first robot prototype that runs in his own home, constantly testing and iterating based on real-world use cases like navigating doorways and picking up scattered items from floors.3. The company is building toward a solarpunk vision rather than cyberpunk future, where robots disappear from conscious awareness and operate silently in the background. The founder envisions robots emerging from closets when people leave home, performing cleaning and tidying tasks, then retreating before residents return. This approach contrasts sharply with the prevalent humanoid robot vision featuring teleoperated machines with cameras potentially monitored by remote operators. The goal is reducing technology's presence in daily life to create more time for family, creativity, and meaningful activities rather than introducing intrusive robotic presences into living spaces.4. Consumer robotics faces fundamental challenges that explain why almost no companies successfully sell to end users despite massive investment in the sector. Industrial robotics offers clearer paths to revenue, while consumer products require extensive market education, safety certifications, and must genuinely change lives rather than provide marginal improvements. The founder argues that saving people ten or thirty minutes daily is insufficient and forgettable, but saving two hours daily creates transformative value that sustains product adoption. This high bar for meaningful impact explains why the consumer robotics market remains largely unaddressed despite technological capabilities.5. The business model centers on teaching robots specific tasks through a thirty-minute training process using a smartphone, then fine-tuning AI models for those particular workflows. Rather than pursuing general physical AI that can handle any task, Flourish enables users to create custom automations for their specific needs, like retrieving items from the refrigerator or collecting clothes for the washer. This approach acknowledges that general physical AI is not yet ready while still delivering practical value through task-specific learning. The company plans to launch fifty units at thirty-five hundred dollars, shipping worldwide, with rapid sellout expected due to existing demand.6. The robot extends vertically to reach from floor level to table height, enabling tasks like cleaning surfaces and accessing shelves while maintaining a compact mobile base that can navigate through doorways. Current limitations include lack of waterproofing, preventing dishwashing tasks, and challenges with delicate object manipulation requiring advanced tactile sensing. The founder uses first-generation prototypes in his own home, scheduling the robot to pick up scattered items like socks and trash throughout the day. This real-world testing revealed critical design issues like initial prototypes being too large to pass through standard doorways, problems only discoverable through actual home deployment rather than warehouse testing.7. The rise of AI-powered robotics fundamentally transforms hardware development by enabling software engineers without traditional robotics expertise to launch robotics companies after just weeks of learning. This democratization stems from downloadable models from well-funded research companies like Physical Intelligence, whose work the founder incorporates into Flourish robots. This represents a shift from the industrial age's capital-intensive factory model to an information age paradigm where substantial capability can be rapidly deployed through software. However, this transformation raises questions about competition intensity, market education for skeptical consumers, and whether the benefits will broadly distribute or concentrate among those with will and intelligence to build while others remain excluded and fearful. Timestamps00:00 Stewart welcomes Antoine Marcel, CEO of Flourish, who explains they're building accessible home robots with wheeled bases and arms rather than expensive humanoid forms for practical domestic use.05:00 Antoine discusses testing prototypes in his home for picking up clothes and trash, explaining how real-world testing revealed issues like doors being too narrow for early designs.10:00 The conversation shifts to how AI hype enables useful research, with Antoine noting he uses physical intelligence models in his robots that wouldn't exist without funding bubbles.15:00 Antoine emphasizes robots must save users two hours daily to matter, explaining why consumer robotics is harder than industrial applications due to certification requirements and market education needs.20:00 Discussion of solarpunk philosophy versus cyberpunk futures, with Antoine envisioning robots that hide away when not needed, emerging only to clean and organize while families enjoy life.25:00 They explore how different cities might adopt solarpunk or cyberpunk approaches, comparing bureaucracy as inherently cyberpunk while discussing Argentina's potential as a solarpunk capital.30:00 Antoine positions Flourish like early Apple, making complex robotics simple and accessible, rejecting teleoperated solutions in favor of onboard AI that learns from users.35:00 The business model discussion centers on unit economics, with Antoine arguing domestic robots should monetize time saved rather than attention captured like social media platforms.40:00 Antoine advocates against robot screens and rental models, favoring voice control and ownership so robots learn individual routines and preferences through personalization.45:00 They discuss robots disappearing from view when unneeded, contrasting home automation with dark factory models, before exploring robot aesthetics and art possibilities.50:00 Antoine announces Flourish's launch of fifty units at $3,500 that users can train in thirty minutes using phones, teaching specific tasks the AI then fine-tunes for daily execution. LinksFlourish Robots

In this episode of the Crazy Wisdom Podcast, host Stewart Alsop sits down with Neil Bortolus, founder of Esteem Biomechanics and Stewart's personal trainer for the past year and a half, to explore the intersection of functional movement, human biology, and emerging technology. Their conversation ranges from the principles of biomechanics and the evolutionary blueprint of human movement—standing, walking, running, and throwing—to the dangers of modern movement practices that prioritize aesthetics over function, the role of fascia and connective tissue in chronic pain, and why most gym exercises fail to respect our biological needs. They tackle bigger questions about where technology is taking us, from AI and LLMs replacing human cognitive work to transhumanism and whether we're headed toward a cyberpunk dystopia or a solarpunk regenerative future, all while emphasizing the importance of first principles thinking, the need for humans to remain in the loop, and creating environments that promote biological and mental regeneration rather than degeneration. Visit Neil's website atesteembiomechanics.comTimestamps00:00 Stewart introduces Neil Bortolus, founder of Esteem Biomechanics and his trainer for a year and a half, discussing their interesting conversations about movement and health05:00 Discussion shifts to negative and positive pressure in the body, how athletes naturally create proper core tension, and the differences between training high-performance athletes versus clients with cerebral palsy or stroke recovery10:00 Stewart shares his journey through yoga and discovering modern postural yoga's surprising origins in late 1800s British calisthenics mixed with Indian anticolonial movements, questioning why modern movement practices are fundamentally flawed15:00 Neil explains functional patterns' first principles approach, emphasizing standing walking running and throwing as evolutionary movement patterns that should guide all exercise design20:00 Conversation explores Brian Johnson's technology-focused longevity experiments versus optimizing natural biological function, questioning whether peak biology or technology interventions lead to better health outcomes25:00 Neil describes environmental factors like Southern California and Hawaii providing optimal conditions for human health through fresh food, sunlight, and natural grounding environments year-round30:00 Discussion of solarpunk versus cyberpunk futures, examining how technology could either support biological health or create dependency, with Stewart sharing his Invisalign experience as cautionary tale35:00 Neil and Stewart analyze education systems, influencer marketing, and Jacques Ellul's concept of technique autonomously taking over human goals and replacing both muscle and mental labor40:00 Neil questions Stewart's first principles for approaching technology, leading to discussion about using AI to accomplish tasks faster while maintaining human oversight and decision-making authority45:00 Stewart explains the black box problem with LLMs and neural networks, admitting he's built applications he can't fully understand, questioning whether understanding mechanisms matters if outputs work50:00 Debate about benevolent AI governance using El Salvador's transformation as analogy, questioning whether society should surrender control to AI systems for farming or other critical infrastructure55:00 Final discussion on determinism versus free will, alignment problems in AI development, and Neil's concept of regenerative versus degenerative environments for optimal human biological and mental healthKey Insights1. Movement training must be adapted to individual needs and capabilities rather than following a one-size-fits-all approach. Neil explains that working with clients ranging from those with cerebral palsy and stroke recovery to high-performance athletes requires fundamentally different techniques. While stroke and cerebral palsy clients experience every improvement as monumental, such as being able to walk a mile, elite athletes already possess optimal biomechanics and need only tiny refinements. The challenge lies in keeping athletes motivated through detailed adjustments while helping severely impaired clients build basic functions from the ground up.2. Modern fitness and yoga practices are built on flawed first principles that prioritize aesthetics over functional movement. The conversation reveals how modern postural yoga was actually invented in the late 1800s through a combination of British calisthenics and Indian anticolonial movements, not ancient wisdom as commonly believed. Similarly, conventional gym exercises and bodybuilding focus on how bodies look rather than how they function evolutionarily. The key insight from Functional Patterns methodology is that if a technique cannot help someone who is broken, it should not be applied to someone who is healthy. This principle exposes how much of modern fitness is based on working with people who are already genetically gifted rather than developing universally applicable methods.3. Human movement should be designed around four fundamental patterns that reflect our evolutionary blueprint: standing, walking, running, and throwing. These movements represent the biomechanical demands that shaped human evolution and remain essential for optimal health. Hunter-gatherer tribes that still exist today demonstrate this principle, as their members typically exhibit athletic builds, low body fat, minimal injury rates, and few mental health disorders despite having no access to modern gyms. The critical insight is that 80 percent of human movement occurs horizontally through space rather than vertically, yet most conventional exercises focus on vertical force vectors like squatting and deadlifting, which do not align with how humans actually move in nature.4. Technology should be used to optimize our environment for biological health rather than replace biological function entirely. The discussion contrasts two approaches to human enhancement: using technology to create better living conditions versus using it to bypass the need for healthy habits. Examples include using renewable energy to reduce electromagnetic frequency exposure, ensuring clean water access, and designing indoor lighting that mimics natural sunlight patterns. The key is maintaining environments that support regenerative rather than degenerative processes. This regenerative approach considers whether food, exercise, and environmental factors promote cellular regeneration and joint health, rather than simply preventing obvious harm.5. The rapidly changing technological environment is outpacing human biological adaptation in unprecedented ways. While human populations historically had thousands of years to develop epigenetic adaptations to their environments, such as Icelandic peoples developing larger bodies for heat retention or Sub-Saharan Africans developing efficient heat expulsion, modern society changes too quickly for such adaptation. The past two hundred years have seen dramatic shifts, and the pace is accelerating rather than slowing. Most dramatically, the media environment is changing fastest of all, particularly with AI development creating a cognitive flywheel where staying current requires constant attention to collective knowledge networks like Twitter and Reddit, leaving those outside these loops increasingly disconnected from technological reality.6. AI and large language models represent both opportunity and risk depending on whether humans maintain agency in their use. The conversation distinguishes between using AI as a tool to accomplish tasks more efficiently while keeping humans in control versus allowing AI to make decisions autonomously. The example given involves using AI to complete projects that previously required hiring people, eliminating interpersonal problems while still maintaining human oversight and learning. However, there is concern that many people are beginning to ask AI how to make every decision, effectively abdicating their agency. The critical variable in any AI interaction is actually the human prompter and their instructions, yet this is often overlooked by AI developers who believe in determinism and lack of free will.7. Modern society operates within a deterministic framework where free will is exercised primarily in choosing which constrained environment to inhabit. Using the example of an accountant earning two hundred thousand dollars annually with a four hundred thousand dollar mortgage and three children in private school, Neil illustrates how most daily decisions are predetermined by earlier choices about environment and commitments. True free will exists at major life forks, such as choosing a career path or living situation, but day to day existence operates within deterministic constraints created by those choices. This insight applies to both human development and AI alignment, suggesting that the solution is not giving AI ultimate freedom but rather constraining its environment so that available options consistently benefit humanity, similar to how Functional Patterns constrains exercise choices to only those respecting biological blueprints.

In this episode of the Crazy Wisdom Podcast, host Stewart Alsop sits down with Daniel Segundo to discuss why today's social networks have evolved into entertainment networks and how Daniel is building Bien, a social app focused on depth over breadth. Daniel shares his journey from trading commodities to creating a platform designed to keep users genuinely connected to their inner circle, inspired by insights gained while meditating in caves in India. The conversation covers everything from the problems with the "like button" and algorithmic feeds to Daniel's contrarian approach of removing public-facing engagement metrics and using voice notes as the primary interaction method. They also explore the challenges of building consumer social products, the impact of AI on development, and Daniel's philosophy drawn from Eastern mysticism and his study of Ramana Maharshi. To learn more about Bien, visit bien.social or follow Daniel on Twitter at @DanielPSegundo.Timestamps00:00 Daniel introduces Bien as a real social network, contrasting entertainment networks disguised as social platforms05:00 Building with AI tools allowed bootstrapping the app before hiring a five-person team for professional development10:00 Social networks evolved into entertainment networks optimized for engagement and advertising rather than genuine connection15:00 The advertising model incentivizes dopamine delivery and algorithmic content over posts from actual friends20:00 Removing the like button and public comments forces deeper interaction through private voice notes instead25:00 Social graphs become stale over time because unfriending feels awkward, so people curate for weakest relationships30:00 Matrix and Nostr protocols enable interoperable messaging, suggesting a Cambrian explosion of new social apps35:00 Depth matters more than metrics—success means users actually feel closer to their inner circle40:00 Voice notes combined with photo sharing mimics natural storytelling behavior when friends gather in person45:00 Meditation in Ramana Maharshi's caves in India inspired reflections on silence, self-inquiry, and building products mindfullyKey Insights1. Daniel Segundo was inspired to create Bien after recognizing that existing social networks excel at breadth but fail at depth of connections. While meditating in caves in South India, he went through his phone contacts and realized only about 5 percent of his 1,700 contacts were people he actually wanted to stay close to, with the remaining 95 percent being noise. He saw that tools like Facebook, Instagram, and WhatsApp have solved the problem of maintaining surface-level relationships across broad networks of people, but there was no good tool for maintaining deep connections with your inner circle beyond basic messaging apps like iMessage or WhatsApp, which still suffer from having too many contacts with access.2. The fundamental problem with current social networks is that they have become entertainment networks rather than true social networks, driven by the advertising business model. Instagram only shows content from your actual friends about 7 percent of the time, with the rest being algorithmically suggested content designed to maximize engagement and ad revenue. The like button, while useful for maintaining broad social graphs and understanding your status in larger groups, reduces complex human relationships to superficial interactions. This creates what Segundo calls a circus-like experience where you are constantly bombarded with entertaining content rather than meaningful updates from people you care about, and the advertising model incentivizes this transformation because the product is ultimately about delivering dopamine to keep people scrolling.3. Bien removes traditional social media features like public likes and comments, replacing them with private voice note responses capped at 60 seconds. The theory is that hearing three voice notes from close friends provides more genuine connection than receiving 300 likes on a post. This design choice stems from recognizing that while the like button serves a useful function for breadth-first social networks where people want quick validation from large peer groups, it is completely inadequate for depth-first relationships. By forcing more intimate and effortful interaction through voice, Bien attempts to upgrade the quality of connection rather than the quantity, building off existing habits like sending voice notes to close friends rather than trying to create entirely new behaviors.4. The traditional follower-following model creates stale social graphs because people are uncomfortable unfriending or unfollowing others as relationships naturally evolve over time. Instead of this model, Bien uses a system where each user maintains a private list of people they want to stay close to, and if two people have each other on their lists, they see each other's content. This eliminates the social awkwardness of unfriending someone while allowing social graphs to naturally evolve. The current model forces people to curate their sharing based on the weakest relationship in their network because they do not want to offend distant connections or go through the work of managing close friends lists, which results in less authentic sharing overall.5. Segundo is building Bien on a paid subscription model rather than an advertising model to align incentives directly with users. He believes no one can build a compelling social network on the advertising model anymore because it creates conflicting incentives where the company must serve both advertisers and users, ultimately degrading the product into what he calls slop. The straightforward deal with customers is that if the product cannot provide value equivalent to the price of a cup of coffee per month through premium features, then there is no justification for the business to exist. This allows Bien to focus solely on whether users feel closer to their inner circle after using the product rather than optimizing for maximum scrolling time.6. Segundo bootstrapped the initial development of Bien using AI coding tools like Replit before hiring a team of five people to develop it professionally. He notes that with current AI tooling, if you can think clearly and understand the problems you are trying to solve, you can build solutions much more cheaply than even five or ten years ago. He eventually hired a designer and iOS and web developers to do things properly with a do it right or do not do it at all mindset. The app is currently iOS-only, built in Swift for a premium experience, with plans to potentially build a React Native version for Android if there are stronger signs of product market fit.7. Segundo's background in trading commodities and futures, along with his deep interest in Eastern philosophy and self-inquiry practices, significantly influences his approach to building Bien. He spent time studying Ramana Maharshi and practicing meditation in caves at Tiruvannamalai in South India, which he says has a profound impact not just on spiritual practice but on thinking about product development and participating in markets. His experience with a major trading drawdown in 2025 after running up a small account 25 times led him to this period of reflection that ultimately resulted in the conception of Bien, demonstrating how his various interests in trading, philosophy, and technology intersect in his approach to building a depth-first social network.

In this episode of the Crazy Wisdom Podcast, host Stewart Alsop sits down with TJ Marbois, founder of Tobiko, for a wide-ranging conversation that spans LLMs, data sovereignty, knowledge management tools like Obsidian, and Terence McKenna's ideas about an increasingly weird future. They explore how AI is simultaneously centralizing power through data control while decentralizing software development capabilities, allowing more people to build their own tools and escape big tech ecosystems. Drawing on his experience at Apple's special projects group (the team that built the iPod and later iPhones), TJ discusses his vision for personal AI assistants—what he calls the "R2-D2 belongs to you" principle—where advanced technology serves individuals rather than corporations. The conversation touches on everything from quantum encryption and local manufacturing with ESP32 microcontrollers to the economics of future society, biometric data unions, and why distributed trust matters more than ever as we approach what both describe as an increasingly strange technological inflection point. Visit Tobiko's site at tobiko-pbc.ghost.ioTimestamps00:00 Stewart introduces TJ Marbois from Tobiko, discussing LLMs, data sovereignty, knowledge management, and the tension between centralization and decentralization in AI05:00 TJ explains the R2-D2 belongs to you concept and why personal AI assistants need maternal alignment, caring about humans like mothers care for children10:05 Discussion of how LLMs will shrink and improve while emphasizing the sentient loop concept for building loyal personal AI agents rather than corporate controlled systems15:00 Exploring digital nervous systems for humanity, social networks as infrastructure, and humans as cavemen with iPhones navigating unprecedented technological complexity20:00 TJ discusses data unions for sovereign data ownership, inverting insurance models where AI helps extend healthspan, and maximizing truth seeking through collective data25:30 Money as social construct, crypto enabling value system reengineering, and working toward Star Trek's post scarcity replicator economy from the ground up30:40 Capital formation challenges, corporatism versus true capitalism, and pessimism about current systems reaching limits while seeking new models35:00 Solar power democratization, ESP32 microcontrollers enabling local manufacturing, and the replicator future through distributed maker communities and open source robotics40:00 Science fiction as roadmap, Isaac Asimov and Arthur C Clarke warnings, and building collaborative futures rather than centrally controlled dystopias with useless eaters45:00 Encyclopedia to LLM transition, information quality concerns, local training importance, and NVIDIA's incentive to put GPUs everywhere for personal AI agents50:00 Corrupted financial incentives, protecting vulnerable people from technological exploitation, and benevolent technologists building systems that honor humanity and children55:00 Hardware validation for owned robots, Starlink dependencies, assembly language abstraction toward natural language programming, and preventing AI escape scenarios58:00 Tobiko as AI toy company building sentient loop interactions similar to Xerox PARC GUI moment, emphasizing maternal AI alignment and cross cultural human collaborationKey Insights1. The concept of R2D2 belonging to you represents a critical vision for the future of artificial intelligence and personal technology. When thinking about robots and AI assistants that follow us around and know everything about us, the question of ownership becomes paramount. These systems will know incredibly intimate details about our lives, from our health data to our daily habits, and if they are controlled by centralized corporations or governments rather than individuals, we lose fundamental sovereignty over our own information. The science fiction of Star Wars and Star Trek provides roadmaps for how we should think about these technologies, showing us both the positive possibilities and the warnings we need to heed about centralized control.2. Local AI models and distributed computing represent a pathway to technological sovereignty that is becoming increasingly viable. While large language models currently run primarily in centralized data centers, the technology is rapidly advancing toward a future where powerful models can run locally on personal computers and devices. This shift is crucial because it means individuals can have full control over their AI assistants without relying on API calls to external servers. Combined with open source infrastructure and open weight models, this creates the foundation for truly personal AI that cannot be controlled or monitored by external parties, whether corporations or governments.3. Data unions and collective data ownership offer an alternative model to current centralized data collection practices. Rather than having individual data harvested by large tech companies who profit from it, the concept of data unions suggests people could collectively pool their data for specific beneficial purposes while maintaining ownership and control. For example, in healthcare, millions of people could share anonymized biometric data to train medical AI systems that are incentivized to keep people healthy rather than treat them when sick, inverting the current insurance model to align incentives with actual health outcomes rather than profit from illness.4. The democratization of manufacturing and robotics through accessible technologies like ESP32 microcontrollers and local fabrication tools is creating new possibilities for distributed production. Just as desktop printers seemed impossible to early printers who controlled book production, we are approaching an era where individuals and small communities can manufacture sophisticated electronic devices and robots locally. This includes the ability to use language models to generate code for microcontrollers, order custom PCBs, and use desktop machines for component placement. While high quality manufacturing will still require larger operations, this gradiation of capability allows for much more local innovation and reduces dependence on centralized manufacturing.5. The concentration of power in technology, finance, and industry has reached levels that are unhealthy for both society and even for those who hold the power. When profit and control become too concentrated in the hands of a few entities, it creates a cancerous dynamic that threatens the stability of the entire system. History shows us warnings about the military industrial complex and other concentrated power structures, and now we are seeing similar patterns emerge in the tech industry. The solution requires building technology from the ground up that empowers individuals and communities, focusing on basics like food production, energy generation, and local manufacturing rather than increasing dependence on centralized systems.6. The provenance and quality of information is becoming a critical challenge as AI systems become more sophisticated and reality itself becomes harder to verify. We are rapidly approaching a point where video calls and digital interactions will be indistinguishable from AI generated fakes, which is why figures like Sam Altman have invested in systems like Worldcoin to verify human identity. However, this verification capability should not be centralized in the hands of single companies. End to end encryption and quantum encryption technologies need to be preserved and expanded to allow humans to communicate and verify information peer to peer without centralized intermediaries who could manipulate or control the flow of information.7. The future of human computer interaction is evolving toward sentient loop systems where machines have sensory input, real time learning, context understanding, and continuous operation in service of human needs. Self driving cars represent the first widespread consumer facing example of this architecture, with onboard computers that must function independently while occasionally connecting to networks. The critical question is whether these systems will be aligned to benefit their human users like a caring mother as AI pioneer Geoffrey Hinton suggests, or whether they will be controlled by centralized powers. The technologists building these systems have a responsibility to be benevolent and build structures that serve humanity rather than concentrate power, helping to create a future more like Star Trek than Terminator.

In this episode of Crazy Wisdom, Stewart Alsop sits down with Charlie D. Becker, a second-generation bookseller whose family runs Houston's largest used and rare bookstore, to unpack the viral tweet that had people convinced AI companies were secretly buying up used books to train their models. Charlie walks through what he actually found in his own warehouse orders, why the more likely explanation is old-fashioned reseller arbitrage and FBA "bookjacking" rather than AI training data, and how that story connects to the real, well-documented case of Anthropic scanning and destroying physical books for legal reasons. From there the conversation moves into the difference between rare and valuable books, the discoverability problem in the used book market, historical parallels like palimpsests and lost texts, his own AI tool for used bookstores, and broader questions about wealthy patronage funding independent research and passion projects. For more, check out Charlie's personal site at charliedbecker.com and his Substack at charliebecker.substack.com.Timestamps00:00 Stewart introduces Charlie D. Becker, a second generation bookseller building an AI tool for used bookstores and discusses Anthropic's controversial book acquisition practices05:00 Charlie explains how Anthropic legally destroyed physical books by slicing spines to scan them, avoiding copyright violations while building training datasets for AI models10:00 Charlie describes receiving bizarre bulk book orders through non-Amazon platforms, initially suspecting AI companies but discovering evidence of sophisticated book arbitrage operations instead15:00 Discussion of the banal explanation for mysterious orders: algorithmic resellers buying cheap books from obscure platforms to flip on Amazon through FBA warehouses20:00 Stewart and Charlie explore historical parallels between the printing press era and today's digital transition, discussing the loss of archival records and palimpsests25:00 Charlie emphasizes the discoverability problem for rare obscure books and how profit-driven algorithms prevent people from finding books unless they know exactly what to search for30:00 Detailed explanation of Charlie's AI cataloging tool that creates bibliographic profiles from photos of pre-1970 books lacking ISBNs, addressing the hard problem of metadata creation35:00 Discussion of the technical challenges solved: archival-safe removable stickers, RFID systems, and creating canonical records that become definitive sources for rare books40:00 Charlie describes building copy-level databases beyond edition-level records, creating VIN numbers for books, and designing knowledge graphs linking works to editions and translations45:00 Vision for navigable work-edition hierarchies allowing researchers to explore translation genealogies and linguistic families, solving problems Amazon has no incentive to address50:00 Stewart raises the possibility of returning to gentleman's science and aristocratic private libraries in an age of AI abundance and accessible three-d printing technology55:00 Charlie reflects on supporting idiosyncratic passion projects regardless of profit, his fellowship from Jim O'Shaughnessy, and navigating economic inequality while promoting eccentric research pursuitsKey Insights1. Charlie D. Becker is a second generation bookseller whose family runs Houston's largest used and rare bookstore, and he is currently building an AI tool specifically designed for used bookstores. He became widely known after a tweet he wrote about AI companies potentially purchasing books went viral with approximately one and a half million impressions, though he emphasizes the importance of being careful about distinguishing between what he has directly observed, what is on public record, and what is his intuition or speculation about these events.2. The controversy around Anthropic and book destruction centers on how AI companies acquire training data from physical books. Court documents revealed that Anthropic acquired physical books in mass quantities to scan them, and they were industrially slicing the spines off to make scanning faster and easier. The legal justification for this practice was that because they destroyed the original physical book after scanning it, they were not violating copyright law since no duplicate copy existed alongside the original. A judge ruled this was technically legal, even though it appeared problematic to many observers, because the destruction of the original meant they were not running afoul of copyright provisions about making copies for distribution.3. Becker received unusual bulk orders for obscure books through a non-Amazon platform in late April, which led him to investigate whether AI companies were responsible for these purchases. However, after analyzing the pattern of purchases and where the books were being shipped, he concluded that a more mundane explanation was likely at work: sophisticated book arbitrage operations. These operations identify books selling cheaply on one platform that could be listed for higher prices on Amazon through Fulfillment by Amazon warehouses, and books that do not sell eventually get recycled or liquidated anyway, meaning rare books are being destroyed through normal commercial operations regardless of whether AI companies are involved.4. The main technical challenge Becker is solving with his AI tool relates to books printed before 1970, which lack ISBNs or International Standard Book Numbers. Modern book cataloging systems are built around ISBNs, which makes it extremely difficult and time-consuming to catalog older books for online sale since there is no automated way to populate bibliographic data for pre-1970 books. His tool uses computer vision and AI to analyze photographs of book covers, title pages, and copyright pages to automatically generate rich bibliographic metadata, and he has partnered with a PhD AI computer vision specialist to develop this technology over the past year.5. A surprising discovery during the development of this cataloging tool was that the existing data for many older books is extremely poor, inconsistent, or completely absent from major databases. For approximately a quarter of the books they process, the only existing records might be an incomplete eBay listing from years ago or a sparse entry in WorldCat, the interlibrary database. This means that rather than simply aggregating existing data, they are actually creating canonical records for many books that will become the authoritative source that others reference, essentially building new infrastructure for book metadata rather than just accessing what already exists.6. Becker advocates strongly for the preservation of obscure and seemingly unimportant books because while they may not have obvious value today, future researchers, tinkerers, or engineers might need them to solve problems we have not yet encountered. He compares this to historical palimpsests where important ancient texts were accidentally preserved when medieval scribes wrote over them, noting that the internet functions more like a palimpsest than an archive since we constantly overwrite and lose information rather than truly preserving it. The current system for deciding which books get preserved or destroyed is essentially random and driven purely by short-term profit motives rather than any thoughtful consideration of potential future value or historical significance.7. The long-term vision for the project extends beyond simple cataloging to creating a comprehensive knowledge graph that distinguishes between works, editions, and individual copies of books in ways that current commercial platforms do not adequately address. Unlike Goodreads which treats all editions of a book as a single work, or platforms like eBay that only show individual edition listings, Becker envisions a system where users can navigate between different organizational levels and explore the genealogy of works across translations, editions, and languages. The project also aims to create copy-level records similar to what libraries maintain, which would track provenance and availability of specific individual copies rather than just edition-level information, something no commercial platform currently does at scale.

Stewart Alsop sits down with Juan Verhook, founder of Tender Market, for a second conversation that ranges from the mechanics of European public tenders to the future of how we organize digital information. They cover how Tender Market helps smaller companies work around barriers like SOC 2 and ISO certification requirements, the surprising scale of public procurement (roughly 20% of GDP), and how AI and machine learning are reshaping the bidding process. From there the conversation opens up into bigger territory: the changing tolerance for being wrong in an AI-saturated information landscape, how language and culture shape perception, the reverse Turing test and the challenge of verifying human versus AI identity online, and Juan's daily workflow running eight or nine MCP servers through Claude Code. They close out talking about whether the folder and file system will survive the shift to AI-native interfaces, tying back to Stewart's own Stewart Squared episodes on the history of the PC. You can visit Tender Market at tendermarket.eu.Timestamps05:00 — Tender Market's origin story and how they help smaller companies work around SOC 2 and ISO certificate barriers.10:00 — Public procurement and its scale, roughly 20% of GDP, plus a look at public-private partnerships.15:00 — Local LLMs on a plane with no Wi-Fi, and comparing local model performance to frontier models.20:00 — Supply versus demand in AI infrastructure and whether hyperscaler token efficiency is quietly improving.25:00 — Whether AI will replace knowledge work tasks, and the shifting reality of what lawyers and other professionals actually do.30:00 — Reverse Turing test, digital identity verification, and the idea of a "pre-AI internet."35:00 — Model poisoning, RLHF, and the difference between pretraining and post-training.40:00 — Interleaved tool calling and how Tender Market ties pricing to task deliverables instead of billable hours.45:00 — RAG versus fine-tuning, prompt engineering, and when context windows actually matter.50:00 — Deterministic programming versus probabilistic agents, and when to build custom tools versus buy existing ones.55:00 — Juan's daily MCP stack (Supabase, GitHub, Calendly, CRM), and whether the folder-and-file system will survive the shift to AI-native interfaces.Key InsightsCertification requirements aren't dead ends—they're routing problems. When smaller companies got rejected from tenders for lacking SOC 2 or ISO certificates, Juan didn't turn them away. He found that EU procurement rules allow bidding as a consortium or subcontracting to a certified partner, turning a disqualifier into a workaround that builds trust with clients.Public procurement is a massive, underexamined market. Roughly 20% of GDP flows through public purchasing of private-sector goods and services, yet most people have no visibility into how tenders work or how governments post and award these contracts.Being wrong has become more socially acceptable. Juan traced this shift to the falling cost of information: in the Stack Overflow era, giving a wrong answer was costly, but now that answers are instant and abundant, both mistakes and corrections happen faster, changing how people learn and communicate.Task-based pricing beats hourly billing for AI-era services. Rather than charging per hour, Tender Market prices around the deliverable, winning a tender, which avoids the perverse incentive of hourly billing to be inefficient and instead rewards actually solving the client's problem.RAG and fine-tuning solve different problems. RAG helps a model reference large documents without hitting context limits, while fine-tuning changes a model's internal weights so it learns new behavior or style. Juan noted that true RAG use cases needing thousands of pages of context are rarer than the hype suggests.Deterministic code should replace repeated LLM calls once a pattern is found. Stewart described his own workflow: solve a task with an LLM a handful of times, then convert the repeated pattern into deterministic software so tokens are no longer spent on it, freeing the model for genuinely new problems.AI agents are never truly autonomous. Both hosts agreed that no matter how many steps an agent chains together, a human operator always initiates the first prompt, meaning accountability and intent trace back to a person even in multi-agent systems.

In this episode, Stewart Alsop sits down with Mark Quadros, founder of GrowthOG and former link-building agency owner, for a wide-ranging conversation covering the state of Facebook ads and paid marketing, the decline of traditional SEO and backlink building in the age of AI, how Google and Cloudflare are jockeying for position as AI search reshapes the internet, and the messy economics of AI-generated video versus real footage (including Mark's work with Google Omni and Epidemic Sounds). They also get into monetization shifts across YouTube, Substack, and TikTok, the "reality disturbance" thesis behind Stewart's show with his father, and where video content and AI video ads are headed next. Find Mark Quadros on X @dherealmark and at his website, markxquadros.com.Timestamps00:00 Stewart welcomes Mark Quadros, discussing his shift from link building to GrowthOG and diving into Facebook ads and paid marketing.05:00 They explore AI psychosis and how AI-driven discovery is replacing traditional Google search behavior.10:00 Talk turns to bloated b2b ad budgets and why large companies struggle to adapt efficiently to AI.15:00 Mark shares his Bitcoin background, the "every company becomes a bank" theory, and his agency's slow decline.20:00 Deep dive into AEO, llms.txt, Cloudflare, and concerns over AI companies scraping the open internet.25:00 Conversation shifts to video, FFmpeg, open-source tools, and Mark's years living in Thailand.30:00 Mark recounts YouTube demonetization of AI content and pivoting back toward b2b clients.35:00 They unpack ghostwriting, ethical writing tensions, and how backlink value was really priced.40:00 Discussion of Google and YouTube killing old SEO playbooks, tied to shifting interest rates.45:00 Mark details pricing AI video ads, working with Google Omni and Epidemic Sounds, blending real footage with AI.50:00 Closing thoughts on the "reality disturbance" thesis and the value of human connection over AI.Key InsightsLink building is dying but not dead. Mark's SEO agency business peaked around 2022 working with brands like monday.com and HubSpot, but AI has steadily eroded the value of backlinks. There's still budget for links, just far less than during the gold rush years of 2018-2022.Big companies waste money because they can't track it. B2B companies spending $2-500M+ a year in revenue often can't account for where ad and vendor spend actually goes. Once a budget exists, it gets allocated regardless of efficiency, especially in large organizations too bureaucratic to figure out AI.AI is changing product discovery itself. Instead of searching Google and choosing from options, people now get AI recommendations directly, shifting the entire model of how consumers find products, which threatens the search-and-link economy that powered SEO for two decades.The open internet may be closing. Concerns about Anthropic, OpenAI, and China scraping content freely, including old books, have pushed creators like Stewart to restrict access to older podcast episodes. This marks a cultural shift away from the openness that defined the early internet era.AEO and llms.txt are the new frontier, and nobody fully understands them yet. Much like early SEO in 2001, there's a new discipline forming around how AI agents discover and read content, but no established playbook exists, leaving even experienced marketers uncertain.AI-generated video is a high-effort, low-margin trap. Mark found that fully AI-generated ads were nearly unprofitable given the time needed to get results right, while blending real human footage with AI-altered environments (using tools like Google Omni) produced better, faster, more believable results.YouTube's AI demonetization wave hit creators hard. As AI-generated content flooded YouTube, the platform cracked down broadly, devastating even legitimate animators and renderers. This pushed Mark to pivot away from consumer video tools back toward serving his existing B2B client base.