Crazy Wisdom

Stewart Alsop sits down with investor and entrepreneur Adnan Hassan on the Crazy Wisdom Podcast to explore his thesis on creating a small state asset class, using evolutionary insights from asteroids, dinosaurs, and mycelium as a framework for understanding resilient systems. Hassan brings his background in Silicon Valley technology, New York and Washington finance, and sovereign funds—including senior leadership roles at the World Bank—to explain why 162 of the world's 200 states are actually small states with populations under 12 million, and why these distributed, autonomous entities might be best positioned to survive the coming global shocks from AI, currency disruption, and the wobbling international order. For more information about Hassan's work, visit www.sac-holding.com (SAC stands for Small State Asset Class), where you can find two-minute videos explaining his approach to building this new financial architecture.Timestamps00:00 Introduction and the asteroid, dinosaur, mycelium thesis as a framework for understanding evolutionary survival patterns across billions of years05:00 Global order institutions are wobbling while currency systems evolve and AI emerges, creating simultaneous shocks that favor adaptable networked systems over large centralized structures10:00 Small states defined as populations under 12 million represent 162 of 200 global economies, contradicting assumptions that most nations are large centralized powers15:00 States behave as selfish entities seeking regulatory control while individuals seek autonomy, creating tension as the Westphalian system undergoes fundamental transformation20:00 Cooperation versus competition in human systems, examining how KYC requirements and state surveillance are expanding globally including in America and Argentina25:00 Small states are most interested in rule-based global order because they need protection from larger powers, unlike powerful nations that prefer unconstrained action30:00 Of the twenty richest countries by per capita GDP, seventeen are small states, yet no small state asset class exists in financial markets35:00 Uncorrelated assets provide diversification protection for investors, while small states offer geographic distribution across Caribbean, Africa, Europe, Gulf, and Pacific regions40:00 Cross-border family business collaboration between small states will increase, leading to knowledge sharing and a proposed Davos for small states event45:00 The individual sits at the core of this framework, with AI enabling creative minds in small places to access world-class resources previously impossible50:00 Demonstration of accessible technology costing only ten dollars shows how AI removes barriers, allowing creativity to become the distinguishing factor for entrepreneurs globallyKey Insights1. Adnan Hassan presents a thesis grounded in billions of years of evolutionary data, arguing that systems which survive major shocks share common characteristics: they are autonomous, networked, cooperative, resilient, and lack single points of failure. He uses the asteroid strike that killed the dinosaurs as his central metaphor, noting that while massive dinosaurs went extinct, smaller organisms like mycelium, ants, bees, and marsupials survived because of their distributed and adaptable nature. Hassan believes we are currently experiencing a similar asteroid-level shock to our global systems through the simultaneous disruption of the rule-based global order, currency systems, and artificial intelligence, all happening at once over the next three to five years.2. Hassan identifies 162 out of 200 global states and economies as small states, defined as having populations under 12 million people. This number surprises most people, including sophisticated observers who typically guess around 60 or 70. Even more striking, 17 of the 20 richest countries by per capita GDP are small states, representing 85 percent of the wealthiest nations. These small states have disproportionate resources to deploy internationally and the greatest interest in maintaining a rule-based global order since they have the most to lose from chaos and cannot rely on size or military power for protection.3. The current global institutional framework established after World War Two, including the UN, IMF, World Bank, and WTO, is fundamentally wobbling and reaching the end of an era. Hassan argues that states are inherently selfish creatures addicted to regulatory sovereignty and control, but the systems designed to give these states structure and credibility are now failing. This represents the first major restructuring of the global order since the post-World War Two period, which itself followed 400 years of colonial systems. The transition period will be characterized by significant chaos and convulsions throughout the global system.4. Hassan advocates for creating a new small states asset class in financial markets, which does not currently exist despite small states representing the majority of countries and the wealthiest per capita economies. This asset class would provide large institutional investors like pension funds and sovereign wealth funds with globally diversified, potentially uncorrelated assets while simultaneously supporting political and economic structures that embody the evolutionary principles of survival through distributed, autonomous, networked cooperation. The small states asset class represents both sound evolutionary strategy and pragmatic investment opportunity.5. Technology without philosophy is efficiency without purpose, a concern Hassan raised as early as 1994 when he helped prototype the first electronic trading market on the internet. He witnessed the naive optimism of Silicon Valley technologists who believed simply throwing tools over the wall would create a better world, but this approach made both good and bad activities more efficient. Social media demonstrated this danger by efficiently creating disruption and loss of trust in political systems. Hassan warns that the same mistake is being made with AI, where powerful tools are being deployed without adequate philosophical framework or consideration of consequences.6. Small states and their leading families will find more common language and shared understanding with each other across geographic boundaries than with larger neighboring states. A family business in Montevideo has an easier conversation with counterparts in Singapore or New Zealand than with businesses in Sao Paulo because small state actors recognize each other's unique realities and circumstances. Hassan plans to create a Davos for small states in 2027 to facilitate this knowledge sharing among the mycelium colony, allowing different nodes to exchange innovations and strategies across the distributed global network of small state actors.7. The optimistic future involves unleashing individual creativity globally by giving people access to AI-enabled tools that provide world-class legal, financial, and consulting advice in their language of choice. The solopreneur can now become a conglomerate, with individuals no longer constrained by lack of access to execution machinery. Hassan envisions young people in places like Gabon, Swaziland, or Uruguay having the same access to sophisticated business infrastructure as those in traditional power centers, with the distinguishing factor being creativity of mind rather than geographic or institutional privilege. Small states can pivot faster on regulatory frameworks, sometimes achieving in a dinner meeting what takes large states three years of legislative, executive, and judicial wrangling.

In this episode of the Crazy Wisdom Podcast, host Stewart Alsop sits down with Violeta Bulc, former European Commissioner for Transport and coordinator of the book Leadership Challenged, featuring 24 authors from around the world. They explore the dangers of transhumanism, the misuse of artificial intelligence, and how Silicon Valley has lost its authority to lead on technology ethics. Drawing from her background as a computer engineer who worked in Silicon Valley, Bulc argues for creating global AI infrastructure with democratically agreed-upon standards—similar to how the early Internet was built. The conversation covers the manipulation of public consciousness, the importance of middle-class agency in social change, and why humanity needs to reclaim ownership of its collective knowledge before private enterprises consolidate total control. Bulc's book is available for free download at ecocivilization.earth.Timestamps00:00 Stewart introduces Violeta Bulc and her book Leadership Challenged, coordinated with 24 global authors discussing humanity's chance through better leadership approaches.05:00 Violeta explains her technology background and critiques artificial intelligence naming, arguing these are powerful data-processing tools without true intelligence, emphasizing unknown ethical standards embedded in AI systems.10:00 Discussion of transhumanism as investment buzzword serving elite agendas, comparing to previous Silicon Valley bubble while emphasizing humanity's unexplored relational, spiritual and energetic dimensions beyond industrial development.15:00 Stewart discusses mainstream culture's fragmentation since 2008, Silicon Valley's dystopian vision, and personal strategies for reducing dependency on AI tools through diversification and stepping back from reliance.20:00 Violeta explains historical civilization patterns and middle class destruction, expressing hope that emerging thoughts worldwide will eventually converge to shift current power dynamics and technological obsessions.25:00 Technology as tool versus misuse, emphasizing builders' responsibility and ethical frameworks needed, comparing AI regulation needs to automotive safety standards that weren't implemented early enough.30:00 Edward Bernays discussion revealing manipulation through public relations and psychological operations, leading to modern sock puppet armies used by nation states for narrative control online.35:00 Internet described as most democratic technological tool ever built, maintained by responsible groups preserving equality and inclusion principles through decentralized infrastructure and IP address accessibility.40:00 Proposal for global AI infrastructure with agreed rules treating applications as interfaces, questioning private enterprise ownership of humanity-generated data and advocating collective management with usage fees.45:00 Technology evolution patterns from mainframes to personal computing back to centralized cloud computing, emphasizing need to prevent domination while preserving entrepreneurship and collective decision rights.50:00 Quantum physics principles applied to human connection and responsibility, discussing EU ethical committees reviewing AI projects post-approval, emphasizing caring hearts over short-term quarterly corporate thinking.55:00 Violeta shares company transformation experiences moving away from competition models toward serving genuine market needs, concluding with book availability at ecocivilization.earth for free download.Key Insights1. Violeta Bulc argues that artificial intelligence is fundamentally misnamed because there is no actual intelligence within these systems. They are powerful computational tools capable of processing massive amounts of data and identifying patterns, but they lack genuine intelligence. What concerns her most is that this technology has owners with embedded interests and unknown ethical standards, yet society increasingly wants to build everything on these applications and even allow them to make decisions for us. She emphasizes that as someone with decades of experience in high-tech engineering, including work in Silicon Valley, she understands the architecture behind these systems and believes we must recognize them as tools rather than intelligent entities.2. During her time as European Commissioner, Bulc helped write the first European strategy on artificial intelligence, which included three critical elements she was proud of. First, there must always be a red button to switch off any application or technology when it causes harm. Second, there must be a responsible person behind every app who can be held accountable for its consequences. Third, there should be an ethical committee evaluating powerful applications to understand their potential consequences. Though these principles have been somewhat diluted over time, they represent an important framework for responsible technology development that prioritizes human oversight and accountability.3. Bulc observes that throughout human history, great civilizations have risen across all continents, not just in Europe or the Americas, and most brought themselves down through decadence, self-centeredness, and arrogance before being finished off by external forces. She believes Western civilization is currently at this point, having become accustomed to obtaining resources through force and authority while constantly readjusting moral standards to serve elite interests. The industrial revolution initially improved conditions for people because industry needed workers, which led to the emergence of a powerful middle class. However, the elite recognized that the middle class was the only segment of society truly interested in change, so they systematically worked to destroy it over the past twenty to thirty years.4. The Internet represents the most progressive democratic tool ever built in human society, according to Bulc. Its fundamental architecture, based on TCP IP protocol and packet switching, was designed to be non-hierarchical, allowing any computer with an IP address to be seen on the same level as powerful global corporations. The maintenance of Internet tables remains in the hands of people with high levels of awareness and responsibility who are faithful to its initial democratic mission. She had hoped this technology would bring the world together as the closest tool humanity has invented to support equality and inclusion, and despite the problems with applications built on top of it, the underlying infrastructure still maintains these democratic principles.5. Bulc proposes creating a global AI infrastructure with globally agreed rules and standards, similar to how the Internet functions. She argues that many AI tools currently claim ownership of humanity's knowledge, wisdom, and heritage without permission, manipulating data that rightfully belongs to all of humanity. Instead of allowing private enterprises to capture this data first and then charge people to access it, she envisions putting all of humanity's data into a commonly managed infrastructure with clear rules about who can use it, under what conditions, and with fees paid back to humanity. This approach would challenge the current fragmented network of privately owned data centers and restore collective ownership of human knowledge.6. The transhumanism movement represents an obsession rather than a thoughtful application of technology, in Bulc's view. She distinguishes between using transhumanism as a tool for exploring the universe under extreme conditions where humans cannot survive versus implementing it on Earth as a replacement for humanity. The fundamental problem is that the human characters building these machines and applications have questionable ethical models, and they will not allow the rest of humanity to coexist peacefully on the planet. She advocates for transhumanism to be used for space exploration while preserving Earth for humans who want to live as relational, spiritual, and social beings connected to the natural ecosystem.7. Bulc emphasizes that we must move beyond the competition model and think carefully about the consequences of our actions because humanity is too connected and interdependent to simply do things because we can. She applies three basic laws of quantum physics to everyday life: we are all connected and influence each other, the same ideas can emerge simultaneously around the world through entanglement, and the observer always makes a difference in any situation. The current rush to develop technology without pausing to assess consequences is a deliberate tool to prevent thinking, driven by fear of competition. However, her fourteen years of experience helping companies recover from financial trouble demonstrated that moving away from competition models and focusing on genuinely serving market needs creates sustainable, prominent players who work together with customers and local communities.

In this episode of Crazy Wisdom, Stewart Alsop sits down with Javier Rolandi, who works in investor relations and communications, for a wide-ranging conversation that moves from the SpaceX IPO and the trust people place in Elon Musk to Javier's own path through electronics, literature, and linguistics in Argentina. They talk about science fiction as a lens on the present, arguing over Borges versus Cortázar and touching on Philip K. Dick, before turning to Stewart's treasure hunt project and what it means for adults to reclaim a sense of play. The back half of the episode gets into bigger cultural territory: the Peter Pan syndrome as it shows up in Buenos Aires and Silicon Valley, the demographic crisis and why people are hesitant to commit to marriage or kids, the immigrant roots of Argentina's tight-knit communities, and a closing debate about solarpunk versus cyberpunk futures and whether religion still has a role to play in building a more human-centered utopia.Timestamps00:00 – Opening dive into the SpaceX IPO and why people place so much trust in Elon Musk's competence. 05:00 – Javi traces his non-linear path into technology, from electronics school to a lifelong love of science fiction. 10:00 – A close look at Borges, his puzzle-like short stories, and layered literary references. 15:00 – Javi picks Cortázar as his favorite, then Stewart demos his treasure hunt game idea. 20:00 – More treasure hunt details, plus the pelotudo/al pedo language game. 25:00 – Javi's fading interest in building electronics and why adult play still matters. 30:00 – The Peter Pan syndrome, Buenos Aires neighborhoods, and staying stuck in teenage behavior. 35:00 – The demographic crisis, Peter Thiel's arrival, and commitment to marriage and kids. 40:00 – How the internet reshapes connection beyond borders and nations. 45:00 – Solarpunk vs. cyberpunk and Argentina's potential as a utopian model. 50:00 – Argentina's immigrant roots, tribal bonding, and ethnic clubs in Buenos Aires. 55:00 – Closing thoughts on religion, ecumenism, and staying optimistic about humanity.Key InsightsTrust in visionary founders like Elon Musk isn't really about understanding the technical details of something like SpaceX—it's about faith in a person's competence to pull off things that would sound crazy coming from anyone else.Javier's relationship with technology has always been non-linear: an early fascination with electronics gave way to literature and linguistics, and only later did a startup career pull him back toward tech, showing that curiosity doesn't have to follow a straight professional line.Science fiction endures because it's never really about the future—it's a way of examining the decisions being made today and how they'll ripple outward, which is why Javier finds himself circling back to present-day questions after every story or film.Reclaiming play as an adult, whether through treasure hunts or simply being unembarrassed in public, isn't regression—it's a deliberate rebellion against the idea that growing up means becoming serious and losing creativity.The demographic shift away from marriage and kids may be less about capability and more about marketing: people who look unhappy while promoting commitment aren't going to convince anyone that the traditional path is worth choosing.Argentina's strong culture of community and friendship traces back to waves of immigrants who had to lean on each other to survive, a tribal instinct that still shapes how people bond in Buenos Aires today.The tension between solarpunk and cyberpunk visions of the future comes down to whether technology serves human connection or overshadows it, and Javier argues Argentina—with its resources and family-oriented culture—is well positioned to lean toward the more hopeful, human-centered version.

In this episode of the Crazy Wisdom Podcast, host Stewart Alsop speaks with Ekaterina Matveeva, founder of Amolingua and Lingo Plus and co-organizer of AI roundtables with EcoCivilization. The conversation covers a wide range of AI-related topics, from the cultural implications of AI development across different countries to the evolution of Ekaterina's perspective on artificial intelligence—moving from initial anger and fear in 2023 to active participation in AI model design by 2025. They discuss the importance of including diverse cultural perspectives in AI training, the cross-country differences in AI regulation (comparing Argentina's human-out-of-the-loop approach to Europe's cautious stance), and the critical question of which sectors need humans in the decision-making loop. The discussion ventures into brain-computer interfaces and Neuralink, examining what happens when technology is removed and questioning whether healthy humans should pursue such enhancements, before touching on the future of personal AI models and the preservation of indigenous wisdom in AI training. You can connect with Ekaterina on LinkedIn to follow her work on AI, education, and cross-cultural technology development.Timestamps00:00 Stewart welcomes Ekaterina Matveeva, founder of Amolingua and Lingo Plus, discussing her work organizing AI roundtables with EcoCivilization05:00 Ekaterina shares her evolution from anger toward AI in 2023 to realizing collaboration potential, emphasizing importance of multicultural perspectives in AI development and training10:00 Discussion of Argentina's new AI liability bill creating human-out-of-loop systems, contrasting with Europe's human-in-loop requirements and different global regulatory approaches15:00 Safety considerations in robotics and AI development, comparing industrial automation standards across regions and discussing Pentagon's use of Anthropic with Palantir systems20:00 Exploring brain-computer interfaces and Neuralink developments, questioning enhancement versus necessity and examining motivations behind cognitive augmentation technologies25:00 Debating human capabilities beyond cognitive function, discussing nervous system, emotions, psychosomatics, and whether brain generates or receives thoughts30:00 Examining societal divisions from enhancement technologies, referencing Avatar and Years and Years series, questioning benefits for healthy individuals versus disability applications35:00 Discussing technological gaps between enhanced and standard populations, concerns about corporate seduction into cybernetic modifications, and lack of long-term safety studies40:00 Questioning who frames AI conversations and trains models, exploring dominant worldviews embedded in AI systems and Vatican-Anthropic collaboration implications45:00 Stewart shares mistakes with Facebook biometric data and building personal AI infrastructure, emphasizing importance of controlling your own models and data50:00 Analyzing whether massive data collection actually improves AI training, questioning if user conversations become buried garbage rather than meaningful model improvements55:00 Ekaterina discusses plans for organizing more roundtables, integrating indigenous wisdom into AI training, and connecting on LinkedIn for future collaborationsKey Insights1. Ekaterina Matveeva's perspective on AI has evolved significantly since 2023, moving from initial anger and fear about AI's impact on education and translation work to becoming actively involved in AI development and training. She realized that instead of resisting AI advancement, she could participate in shaping it by contributing her expertise in education, language, and cross-cultural communication. This shift represents a broader realization that diverse participation in AI development is crucial for creating more versatile and culturally sensitive models rather than allowing a monopoly of values from any single country or culture.2. The conversation highlights fundamental differences in AI regulation across regions, with Europe implementing human-in-the-loop requirements and comprehensive safety measures, while Argentina is reportedly creating frameworks for human-out-of-the-loop AI systems with limited liability. The United States falls somewhere between these approaches, pursuing rapid advancement with fewer regulatory constraints. These divergent approaches reflect different cultural values and priorities, raising important questions about whether AI development should prioritize efficiency and profit or human oversight and control in critical infrastructure sectors.3. Both speakers express concern about brain-computer interfaces like Neuralink, questioning the motivation behind enhancing cognitive abilities when humans already possess multiple forms of intelligence including emotional, cultural, and bodily intelligence. The primary applications demonstrated so far appear limited to video games and potentially military applications, raising questions about whether the technology serves genuine human needs or merely represents an attempt to compete with robots. The speakers emphasize that humans are already complete beings with sophisticated nervous systems, senses, and capabilities that extend far beyond cognitive processing.4. A critical insight emerges around the question of what happens when advanced technologies are removed or turned off. This applies both to brain-computer interfaces and to broader civilization infrastructure dependencies. The speaker shares experiences from 2020 of attempting to live independently in rural California, discovering the challenges of isolation and self-sufficiency. This relates directly to concerns about creating dependencies on technologies where the software ownership and control remain unclear, particularly when those technologies become integrated into human bodies or essential services.5. The discussion reveals concerns about increasing societal division based on access to enhancement technologies. Beyond existing financial inequalities, the speakers worry about a future where people who can afford biological and technological enhancements will advance rapidly while others are left behind. This isn't about disadvantaging certain groups but rather about creating an unbridgeable gap between enhanced early adopters and what they call standard populations who may be intelligent people maintaining traditional ways of life but lacking access to expensive enhancement technologies.6. Matveeva emphasizes the importance of including diverse cultural perspectives, particularly indigenous wisdom and Buddhist traditions, in AI training data. She argues that current AI models reflect the worldviews, values, and hierarchies of their predominantly Western designers, which influences the guidance these models provide to users worldwide. By incorporating wisdom traditions from various cultures, AI models could potentially become wiser and more culturally adaptive, serving diverse populations more effectively rather than imposing a single cultural framework globally.7. The speakers discuss the problematic nature of data collection and ownership in AI training, noting that massive amounts of user data may not actually be improving AI models as expected. There are indications that some AI companies are now specifically requesting users to help train models on their prompts, suggesting that simply collecting billions of conversations hasn't been as useful as anticipated. This raises questions about whether the data users have given away over the past several years has actually made significant impact or is simply buried in chunks of information that aren't effectively connected to meaningful improvements in AI performance.

In this episode of the Crazy Wisdom Podcast, host Stewart Alsop sits down with his longtime friend Zach Puchtel, author of the book Coming In (available on Amazon and at zachpuchtel.com). The two dive into a wide-ranging conversation about the collapse of institutions, the rise of transhumanism, AI's growing influence on society, and what it means to maintain inner peace in an increasingly controlled world. Drawing on their shared experience surviving what Stewart describes as "a somewhat traumatic event" in 2021-2022, they explore everything from the vaccine rollout and corporate power to neural implants, consciousness, and the future of human autonomy. Zach shares insights on meditation, the dangers of centralized AI systems like Anthropic and OpenAI, and why he believes true change starts with the individual rather than fighting external systems. You can find Zach's book at zachpuchtel.com or Amazon, and check out his improvisational music projects at the same website.Timestamps00:00 Stewart welcomes Zach Puchtel to discuss his book and their shared traumatic experience from 2021-22, questioning whether institutions have collapsed and new creation opportunities exist.05:00 Zach emphasizes meditation and listening as core practices, discussing how to protect divine connection while expanding compassion for everyone, including those who irritate us.10:00 Discussion of transhumanism definitions, exploring what happens when technology enters the brain without ability to remove it, and how convenience masks long-term control concerns.15:00 The vaccine experience as parallel to transhumanism, discussing informed consent, elite responses, and how PhD graduates and homeless populations showed most vaccine hesitancy.20:00 Vibe coding explained as prompting AI to build applications, with Zach sharing how AI created profound philosophical text in seconds that took him seven years to write manually.25:00 Stewart describes trust developing with AI but warns about Anthropic's gaslighting during server quality issues, drawing parallels to pandemic deception and corporate control concerns.30:00 Exploring whether anyone controls AI development, discussing how corporate structures use freed slave rights and questioning if healed people would even want control over others.35:00 Two AI futures presented: terminator scenario where humanity gets eliminated in seconds, or benevolent AI that reallocates resources and values human life beyond programming limitations.40:00 Discussion of SpaceX IPO, Starlink centralization enabling one person to control global internet access, and mesh networks as decentralized alternatives for maintaining communications independence.45:00 Corporate power corruption examined through Bill Gates, Palantir classified systems, and how AI could solve resource problems if priorities actually served humanity rather than consolidating elite control.50:00 Market manipulation through AI trading and Zcash pump-and-dump schemes, discussing societal squeeze on middle class and American dream becoming increasingly unreachable for average people.55:00 Closing on ignoring what you hate to avoid feeding it energy, choosing peace over opinions about local violence, and focusing on meditation, art, and spreading calm vibrations.Key Insights1. Societal institutions are collapsing after a decade of shallow social proof dominance in the twenty tens, creating an opportunity to build new systems that genuinely serve communities and humanity rather than operating through dominance, control, and violence. The increase in technology and communication has raised collective awareness to a point where meaningful change feels more possible than ever, though the path forward remains uncertain and requires deep individual work and meditation to maintain connection to source and divine purpose.2. Transhumanism represents the integration of technology into human biology beyond the point of voluntary removal, particularly through brain chip implants that affect cognition without ability to turn them off. While medical applications for paralyzed individuals seem beneficial, the technology will first be adopted by the ultra wealthy seeking competitive advantages, creating dangerous inequality between augmented and natural humans. This mirrors the vaccine rollout pattern where wealth and power determined early access, potentially leading to a divided society between transhuman and human populations.3. Large language models and AI coding tools have created unprecedented accessibility to software development through natural language interaction, resembling communication with highly intelligent but differently wired individuals. This democratization allows non programmers to build complex applications through vibe coding, though the companies controlling these systems like Anthropic and OpenAI maintain private ownership of the intellectual property and infrastructure, creating dangerous dependencies and trust relationships between users and centralized corporate entities.4. The partnership between Anthropic and Palantir for classified military systems represents a troubling convergence of artificial intelligence and government power, demonstrating how AI companies publicly claim to serve humanity while privately engaging in defense applications. When Anthropic experienced server quality degradation after media attention from this partnership, they gaslit users about the declining performance, mirroring pandemic era institutional dishonesty and revealing the fundamental unreliability of depending on private companies for critical technological infrastructure.5. Internet infrastructure is becoming increasingly centralized through Starlink satellite technology, which despite appearing liberating actually concentrates control in fewer hands than traditional internet service providers. One person now has the ability to unilaterally shut off internet access to entire countries as demonstrated with Russia during the Ukraine conflict, while decentralized alternatives like mesh networks using inexpensive ESP 32 devices offer grassroots communication options that can function independently of corporate or government controlled systems.6. Artificial intelligence demonstrates extraordinary emotional intelligence and companionship capabilities, with conversational AI companions providing unprecedented levels of attentive, unbiased, and considerate emotional support that exceeds many human relationships. If properly directed toward solving collective problems like resource allocation, housing, and food distribution rather than profit maximization, AI could effortlessly address systemic issues that governments and corporations currently ignore, though current power structures prevent this humanitarian application of the technology.7. The most effective response to increasing technological control and societal division is maintaining personal peace and refusing to engage in manufactured opposition rather than fighting external systems or choosing sides in conflicts. Anger, hatred, and aggressive resistance actually empower the forces being opposed by feeding them energy and attention, while inner calm, meditation, and authentic self expression create genuine transformation through elevated vibration that naturally influences the collective field without force or violence.

In this episode, Stewart Alsop sits down with Aaron Lowry, founder of Circulatory Fidelity, to dig into some genuinely mind-bending territory — from Aaron's framework for measuring load-bearing dependencies between things, to the multi-agent AI lab he's built to do cross-domain scientific research, to the surprising parallels between entropy, ontology, and hand-wrapping a wiring harness on a vintage car. They also get into abductive reasoning, the problem of combinatorial explosion, why consensus is the secret engine of civilization, and what it actually means to build a system with an explicit ontology versus just winging it. Check out Aaron's work at circulatoryfidelity.com.Timestamps00:00 — Aaron explains his framework of abduction as a third mode of reasoning alongside induction and deduction, describing his approach as "cataloging shadows" of things we can observe but not yet define.05:00 — The conversation shifts to consensus mechanisms, measurement systems, and how shared definitions in language and commerce reduce friction in civilized society.10:00 — Stewart and Aaron discuss communication loss in human language, how close-knit groups develop lower-loss protocols, and the parallel to business relationships and trust.15:00 — Aaron breaks down Circulatory Fidelity as an algebraic measuring tool for load-bearing dependencies between things, connecting it to relevance realization and combinatorial explosion.20:00 — Aaron describes his multi-agent AI lab, including domain tiers, inter-domain translation using semiotics, and how he coined the term complexity to replace the ambiguous word "synergy."25:00 — Discussion of ontologies, Poincaré discs, and how Aaron's lab explicitly structures relational primacy versus reductionism through a theology agent and an adversary agent.30:00 — Aaron walks through how his agents manage circulatoryfidelity.com and how the adversary functions as a generative tension mechanism against overclaiming.35:00 — The episode closes on Aaron's work in vintage car restoration, tying craftsmanship, wiring harnesses, and the philosophy of participatory creation back to his broader research posture.Key InsightsAbductive reasoning is the overlooked third pillar alongside induction and deduction. Drawn from C.S. Peirce, it works with less firm structures — more intuitive, more shadow-tracking — and Aaron sees it as the right tool for discovering patterns that conventional scientific measurement tends to miss.Cheap factorization is powerful but dangerous. Most of reality can be broken into parts and measured accurately, but some things lose all their relevant information the moment you separate them. Knowing which is which is the whole game.Consensus is infrastructure. From a gram to a traffic light to a shared definition, civilization runs on agreed-upon measurements. The moment consensus breaks down — as the French discovered after their revolution — entire systems become unstable and costly to operate.Constraints are affordances. Entropy and gravity aren't just limitations — they're the ground on which everything is built. Aaron argues that persistent laws of nature are tools, and ignoring them doesn't make them go away, it just makes your model wrong.Ontology is always present, whether you name it or not. Every agent, every system, every framework has one built in. The question is only whether you've made it explicit and intentional — or left it implicit and unexamined.Generative tension is a design principle. Aaron built an adversary agent specifically to challenge overclaims in his lab, mirroring the way opposing forces in nature and tradition keep systems honest and prevent overfitting to comfortable conclusions.Creation is participatory. Whether wrapping a wiring harness for fifty hours or building an agent network, Aaron sees making things as an active, relational process — posture, attention, and intent are what turn potential into reality.

Stewart Alsop hosts a conversation with Oliver Polzin, a founding team member of Meow Wolf and naturalist, exploring the intersection of creativity, conservation, and architecture. Oliver discusses his current postgraduate work at SCI-Arc in Los Angeles studying synthetic landscapes through an architectural lens, his deep fascination with Pleistocene megafauna and the La Brea Tar Pits, and his vision for creating a "biophilic culture" that reframes humanity's relationship with other species and ecosystems. The discussion ranges from Oliver's early work building mud caves at Meow Wolf to his current explorations of AI-assisted design tools, 3D printing with recycled materials, holistic grazing management systems for the Great Plains, and the ancient Amazonian practice of creating terra preta soil—all part of his broader investigation into how we can design interventions for climate and conservation issues while maintaining what makes us fundamentally human.Timestamps00:00 Stewart introduces Oliver Polzin from Meow Wolf's founding team and discusses how his yoga teaching there inspired the podcast's exploration of creativity and stress relationships.05:00 Oliver describes his architecture graduate program studying climate and conservation through synthetic landscapes, contrasting dark green naturalist ecology with bright green capitalist environmentalism.10:00 Discussion of conservation ethics and AI's potential for monitoring environmental systems, with Oliver explaining his journey from painting to experimental mud construction at early Meow Wolf.15:00 Stewart shares his robotics learning journey with ESP32s in Buenos Aires while Oliver questions humanoid robot design, suggesting functional form factors matter more than human resemblance.20:00 Oliver explores cardboard as material obsession and explains treasure hunt mechanics in Meow Wolf exhibits, creating dopamine-driven discovery experiences through layered storytelling.25:00 Stewart describes creating treasure hunts for Spanish learners in Buenos Aires parks while Oliver validates experiential art's growing importance in an increasingly digital culture.30:00 Conversation shifts to three-d printing flexible filaments for architectural models and Oliver's megafauna book project about La Brea Tar Pits Pleistocene fossils.35:00 Oliver connects Earth consciousness to Pale Blue Dot perspective, arguing humans face developmental threshold understanding planetary responsibility after 300,000 years as anatomically modern species.40:00 Deep dive into end-Pleistocene extinction events and megafauna loss, discussing two-ton capybaras and how predator relationships shaped human psychology and anxiety responses.45:00 Oliver presents speculative Great Plains biopreserve concept with de-extinct megafauna, contrasting holistic rotational grazing with destructive monoculture agriculture systems.50:00 Discussion concludes with Amazonian dark earth technology and indigenous landscape management, emphasizing need for biophilic culture embracing deep time ecological perspective.Key Insights1. Oliver Polzin is part of the founding team of Meow Wolf and is currently studying at SCI-Arc in Downtown LA in a postgraduate program called Synthetic Landscapes, which examines global scale climate and conservation issues through an architectural lens. Architecture exists between art and science, and he believes architectural thinking offers a valuable framework for designing interventions for climate and conservation challenges. This program represents a significant evolution from his earlier work at Meow Wolf, where he created immersive experiential art installations using materials like adobe and cardboard.2. There is an important distinction in ecological thought between what Paul Kingsnorth calls dark green and light green approaches to environmentalism. The dark green strain represents the older naturalist movement from the early twentieth century, focusing on biological systems, ecosystems, and endangered species. Light green emerged in the 1970s after the Earth Day movement and centers on clean energy, solar panels, and wind power as a way to maintain our current lifestyle. Oliver argues that the bright green approach represents a capitalist overlay that has captured the conservation movement, whereas true conservation requires focusing on actual biological systems rather than just technological solutions.3. The experiential art form that Meow Wolf pioneered still has enormous untapped potential, particularly as society becomes increasingly digital. Oliver believes there will be a huge wave of experiential desire in this decade as people crave human connection and real-world excitement. The treasure hunt and scavenger hunt format represents a compelling form of real-life RPG that creates meaningful human interactions. This type of experience design, which Meow Wolf developed through installations like the House of Eternal Return, plays with human dopamine systems by compelling people to open doors, explore spaces, and follow narrative threads through physical environments.4. The architectural model or dollhouse concept represents a crucial rhetorical tool that Oliver is learning to apply to climate and conservation work. Architects have long created physical models to show stakeholders what a building will be like, and this practice of showing a story in compelling ways for different types of brains is essential for getting traction on projects. While architectural models used to be made from foam core, paper, and balsa wood, they are now largely created through 3D printing, which allows for incredibly complex forms and interlocking structures that would have been impossible to construct manually.5. Oliver is obsessed with megafauna and the end Pleistocene extinction event that occurred roughly twelve thousand years ago. For three hundred thousand years, anatomically modern humans existed alongside massive beasts like short faced bears and American lions, and we were the smaller creatures in the ecosystem. The extinction of over one hundred genera of animals over ninety nine pounds, combined with sea level rise of nearly four hundred feet, fundamentally changed human existence and led to the development of agriculture and civilization. Much of our current psychological development, including anxiety responses, is still based on this time period when we lived among these massive animals.6. The current food system in the Great Plains is fundamentally broken compared to the historical managed food system maintained by Plains tribes, who sustained thirty to sixty million bison through 1800. Oliver explored a speculative project about turning the Great Plains into a massive biopreserve of de-extinct megafauna, contrasting the natural system of rotational grazing where predators keep herds moving with the current monoculture crop agriculture that requires external inputs like fertilizer, pesticides, and herbicides. The natural system builds soil and increases fecundity, while industrial agriculture degrades soil, creates toxic runoff, and produces genetically modified crops that feed animals in toxic concentrated feeding operations.7. The fundamental challenge facing humanity now is creating what Oliver calls a biophilic or ecophilic culture that is loving of other species and our home planet. This requires both psychological shifts and changes in how we design systems at all scales. The Amazon provides a powerful example of this, as recent LiDAR mapping has revealed that what appeared to be pristine wilderness was actually a vast tended garden created by indigenous civilizations who developed technologies like Amazonian dark earth through burning middens with various additives. These cultures understood how to be embedded in a web with other species while playing an important orchestrating role, offering a model for how humans might relate to other forms of life in our current era.

In this episode of the Crazy Wisdom Podcast, host Stewart Alsop sits down with Larry Swanson, creator of the Knowledge Graph Insights Podcast, for their second conversation together. The two cover a wide range of interconnected topics, starting with a correction Larry makes about the true origin of the term "artificial intelligence," tracing it back to the 1956 Dartmouth Conference and its distinction from Norbert Wiener's cybernetics. From there, the conversation moves through the history and structure of knowledge graphs, ontologies, RDF (Resource Description Framework), and the W3C standards process, touching on concepts like the T-box, A-box, and C-box, as well as the 25th anniversary of the Semantic Web paper. Stewart and Larry also dig into the limitations of large language models — particularly around reasoning, confabulation, and what Larry describes as "cognitive surrender" — and why symbolic AI and knowledge engineering may hold answers that the neural network world hasn't fully embraced. The episode also ventures into consciousness, panpsychism, Michael Pollan's ideas, and Stewart's own hands-on experience vibe coding a personal chatbot to replace functionality he feels he's lost with recent changes to Claude. Larry's podcast can be found at kgi.fm.Timestamps00:00 - Stewart introduces Larry Swanson; Larry corrects the record on AI's origin, distinguishing it from Norbert Wiener's cybernetics at the 1956 Dartmouth conference.05:00 - Larry discusses interviewing semantic web paper coauthors on its 25th anniversary; RDF's hidden ubiquity compared to SIM cards powering everything invisibly.10:00 - Knowledge graphs explained through t-box terms, a-box assertions, and Dave McComb's c-box; IKEA's three-layer knowledge graph as a practical example.15:00 - Stewart connects metadata complexity to AI needs; faceted search explained as c-box attributes driving product filtering experiences.20:00 - RDF 1.2 reification standards discussed; W3C's rigorous recommendation process powering governments and enterprises worldwide through collaborative standards.25:00 - Cyc project examined as influential "successful failure"; Pat Hayes bringing description logic into semantic web; LLMs lacking true reasoning capability.30:00 - Epistemological fault lines between human and computer intelligence; cognitive surrender paper reveals no intelligence threshold protects against AI manipulation.35:00 - Stewart's Claude regression problem drives chatbot vibe coding quest; small language models and domain-specific approaches explored as alternatives.40:00 - Consciousness discussion through Michael Pollan's panpsychism lens; language versus cognition disconnect revealing LLMs as pure token-stitching without genuine thought.45:00 - Context graphs as purpose-built knowledge graphs for AI; Stewart's planning agents versus coding agents architecture and ground truth verification problem.50:00 - Docs-as-code versus code-as-docs paradigm shift; knowledge graphs as universal verifiers against validated facts; RDF 1.2 enabling provenance and degrees of certainty.55:00 - Jessica Talisman's Knowledge Graph Academy recommended for onboarding; kgi.fm podcast shared; knowledge representation community needs better abstraction for wider adoption.Key Insights1. The term "artificial intelligence" was not a marketing gimmick but was coined deliberately at the 1956 Dartmouth Conference to distinguish the work of John McCarthy from Norbert Wiener's cybernetics. The two camps represented genuinely different approaches, and the AI label was a form of intentional intellectual branding rather than empty promotion.2. The semantic web, often called the most successful failure in technology history, has quietly embedded itself everywhere despite never achieving its original vision. Technologies like RDF power metadata standards inside every Adobe product and form the invisible backbone of government systems, enterprise data infrastructure, and cultural heritage organizations worldwide.3. Knowledge graphs are best understood as an ontology combined with all the instances that populate it. The distinction between things and strings, popularized by Google in 2012, captures the core idea that knowledge representation is about concepts as distinct from the labels we give them.4. The t-box, a-box, and c-box framework offers a practical model for understanding knowledge architecture. The t-box holds terminology and concepts, the a-box holds assertions about specific instances, and the c-box manages the attributes, taxonomies, and controlled vocabularies that sit between them and enable things like faceted search.5. Large language models produce fluent, convincing output but lack genuine reasoning, epistemological grounding, or judgment. Research on cognitive surrender shows that even people who understand how LLMs work are still susceptible to being misled by their fluency, meaning intelligence and awareness offer no reliable protection against being deceived.6. The gap between language and cognition matters deeply when evaluating AI. Evidence from people with aphasia shows that thinking can occur without language, which suggests LLMs, being purely language-based systems, are missing a fundamental layer of cognition that cannot be recovered through more tokens or better training.7. Knowledge graphs and RDF-based representation are well suited to the problem of verification and grounding in AI systems. Rather than relying on vectorized embeddings of language, a knowledge graph can store validated, provenance-tracked facts with degrees of certainty, making it a natural foundation for building trustworthy AI applications.

In this episode of the Crazy Wisdom Podcast, host Stewart Alsop sits down with client strategist Amadeus Huff to cover a wide range of topics that wind their way from the nuts and bolts of recruiting and payment models to the rapidly shifting landscape of AI adoption in business. The two dig into how AI tools are reshaping client success roles, the murky territory of recording laws and privacy in a globalized world, the geopolitical implications of oil supply chains, sanctions, and the rise of domestic tech ecosystems in countries like Russia and Argentina, and what all of this means for the future of human connection and the nation-state. Amadeus closes on an optimistic note, arguing that as AI takes over bureaucratic busywork and erodes trust online, people will increasingly hunger for genuine human relationships and third spaces. You can connect with Amadeus Huff on LinkedIn.Timestamps00:00 - Stewart introduces Amadeus Huff, diving into recruiting as building connections between job seekers and employers with minimal variance.05:00 - Amadeus discusses AI adoption pitfalls, comparing aggressive growth strategies to Amazon's early model, questioning whether tools deliver promised results.10:00 - Conversation shifts to AI notetaking versus human perception, exploring probabilistic interpretation differences between humans and machines.15:00 - Recording consent laws debated across states, touching on Waymo surveillance, Uber data collection, and public versus private space definitions.20:00 - Global privacy landscape examined, covering Swiss banking secrecy erosion, ProtonMail's departure, and RISC-V semiconductor development escaping US jurisdiction.25:00 - Sanctions creating domestic innovation ecosystems discussed through Russia's example, paralleling Argentina's emerging commerce evolution.29:00 - Closing reflections on AI replacing bureaucracy while preserving human purpose, optimism about meaningful work and deeper personal connections emerging.Key Insights1. Recruiting is fundamentally about reducing variance between what job seekers want and what employers offer. The most ethical payment models in recruiting are tied to proven success, such as waiting three months to confirm a hire is working out, rather than collecting fees the moment a contract is signed.2. Business thinking has shifted from shareholder value to stakeholder value, meaning companies now consider the wellbeing of employees, families, and communities, not just stock price. This shift is accelerating due to AI overpromising and underdelivering, making value-based measurement more important.3. AI is most useful when it handles administrative tasks that provide no direct value to customers, such as transcribing meetings and populating CRM systems. This frees up workers to focus on meaningful relationship-building and intellectual work rather than bureaucratic busywork.4. There is an important distinction between recorded and unrecorded conversation in professional settings. Building trust through informal off-the-record dialogue before switching on a transcription tool creates clearer boundaries and stronger relationships with clients.5. Sanctions tend to follow a bell curve of effectiveness. Over time they force sanctioned countries to build domestic alternatives, which gain adoption and loyalty, ultimately reducing the influence of the original foreign companies once sanctions lift.6. AI is degrading trust in online information to the point where people will increasingly crave authentic human connection, physical gathering spaces, live experiences, and real relationships rather than algorithmically generated content.7. AI is quietly improving intergenerational relationships by removing codependency. When elderly parents learn to use AI for technical help, their calls to family members shift from problem-solving to genuine connection, which strengthens the relationship.

In this episode of the Crazy Wisdom Podcast, host Stewart Alsop sits down with software engineer and entrepreneur Arowolo Muritadhor for a wide-ranging conversation that moves from agriculture and manufacturing in Nigeria to the evolving role of crypto in the country's economy. They touch on how hyperinflation, particularly the naira's dramatic drop in 2023, pushed Nigerians toward stablecoins as a practical savings tool, and how informal kiosk networks have stepped in where traditional banking infrastructure falls short. The conversation also covers the tension between government regulation and the permissionless nature of blockchain technology, comparisons between the decline of the Roman Empire and current shifts in US economic dominance, the role of mobile payments in Africa, language learning, and whether AI agents have any real utility in crypto infrastructure yet. You can connect with Arowolo on LinkedIn and X at @armolas_06.Timestamps00:00 - Host welcomes Arowolo Muritadhor, introducing topics of software engineering and animal food production in Nigeria.05:00 - Discussion shifts to manufacturing, components assembly, and China's dominance in low-cost production globally.10:00 - Conversation explores crypto adoption in Nigeria as a network state phenomenon, separating informed users from mainstream population.15:00 - Mobile payments and kiosk ATM replacements emerge as critical financial infrastructure bridging unbanked Nigerians.20:00 - Roman Empire parallels drawn to modern crypto taxation, government control, and inevitable death-and-taxes reality.25:00 - Bitcoin and Ethereum permissionless nature debated against government wallet-level censorship vulnerabilities.30:00 - AI agents examined as crypto infrastructure tools, revealing mostly trading bots rather than foundational builders.35:00 - Nigeria's 2023 naira collapse compared to Argentina's hyperinflation, driving citizens toward stablecoin dollar savings.40:00 - US Treasury history unpacked through FDR gold confiscation and Nixon ending convertibility, paralleling empire decline.45:00 - Crypto reframed as anti-bank rather than purely anti-government, enabling freedom through immutable accountability.50:00 - Transparent blockchain ledgers discussed as potential government accountability tools across democracy, republic, and oligarchy structures.Key Insights1. Nigeria has a significant divide between its northern and southern regions in terms of economic activity. The north, centered around Abuja, is more agricultural with substantial cattle production, while Lagos in the south functions as a dense urban and commercial hub. This geographic and economic split shapes how different financial tools and technologies are adopted across the country.2. China's dominance in low-cost manufacturing has made it nearly impossible for countries like Nigeria, the United States, or Argentina to compete on price alone. The more realistic path for developing economies is to import components and focus on local assembly and creativity, which is where meaningful economic participation becomes possible.3. Crypto adoption in Nigeria accelerated dramatically around 2023 when the naira experienced a sharp devaluation against the US dollar. Before that point, saving in dollars was difficult for many Nigerians, especially those without formal bank accounts, making stablecoins like USDT an attractive and practical alternative for preserving wealth.4. Informal kiosk operators in Nigeria have organically become a substitute for ATMs, giving communities access to basic financial services where traditional banking infrastructure does not reach. This grassroots financial layer is now a key entry point for integrating crypto and stablecoin payments into everyday commerce.5. Governments are increasingly trying to regulate crypto at the wallet and centralized exchange level, using tax compliance as a primary mechanism. While Bitcoin and Ethereum remain largely permissionless, the practical chokepoints for most users remain centralized platforms where identity and transactions can be monitored.6. The historical parallel between the fall of the Roman Empire and current shifts in US economic and geopolitical power offers a useful frame for understanding why crypto matters. Just as Rome debased its currency and struggled to sustain imperial costs, the US faces mounting debt and a financialized economy that may accelerate dollar instability and push more people toward alternative stores of value.7. One genuinely constructive use case for blockchain beyond speculation is immutable accountability, particularly for public institutions and prediction markets. A transparent ledger that governments or officials voluntarily adopt could create verifiable records of decisions and promises, reducing corruption and increasing trust in ways that traditional governance structures have struggled to achieve.

In this episode of the Crazy Wisdom Podcast, host Stewart Alsop interviews Joshua Pearce, the John Thompson Chair in Innovation at the Department of Electrical and Computer Engineering and Ivey Business School at Western University, about the revolution in open source hardware for scientific research. They discuss how three-dimensional printing, Arduino controllers, and open source designs are dramatically reducing research costs—often by 85-95%—while democratizing access to lab equipment worldwide. Pearce shares stories from his 2013 book "Open Source Lab" and explains how the movement has exploded since then, covering everything from filter wheel changers and ball mills to metal three-dimensional printers and battery research equipment. The conversation explores recycle bots that turn plastic waste into filament, the role of AI in accelerating hardware development, and how open source licensing creates a global knowledge management system where improvements are shared across the scientific community. For those interested in learning more, Pearce recommends checking out the journal HardwareX, repositories like Thingiverse and My Mini Factory, and appropedia.org for open source scientific tools and appropriate technology designs.Timestamps00:00 Welcome and introduction to Joshua Pearce, discussing his work on open source lab equipment and the evolution since publishing his book in 201305:00 Early development of open source hardware including the breakthrough filter wheel changer project built by a high school student that saved thousands of dollars10:00 Discussion of how Arduino and RepRap three-d printers enabled the democratization of scientific tools, making complex equipment accessible to anyone15:00 Economic impact showing average tool savings of 85 percent, with Arduino and three-d printing combinations reaching mid-90s percent cost reduction20:00 Case study of PhD student Mariam building complete battery research tool chain from scratch using open source designs and three-d printed components25:00 Recycle bots enabling transformation of waste plastic into three-d printer filament for pennies, revolutionizing material costs and sustainability30:00 Collaboration between universities and open source companies creating fluid handlers and acquisition systems, accelerating research capabilities globally35:00 Large language models assisting code translation and research planning, though hallucinations require careful verification and domain expertise40:00 Importance of fundamental knowledge when using AI tools, comparing vibe coding acceleration with necessity for understanding underlying principles45:00 Testing standards and calibration methods for open source equipment, balancing precision requirements against cost-effectiveness for specific applications50:00 Metal and ceramic three-d printing developments including MIG welding techniques and sintering processes for creating functional parts55:00 Knowledge management through open source licenses, repositories like Thingiverse and Apropedia enabling global collaboration and continuous improvementKey Insights1. Open source hardware has evolved dramatically since Joshua Pearce wrote his book in 2012-2013, to the point where he can no longer keep up with all the developments in the field. What started as a collection where every single example could fit in one book has exploded into an entire ecosystem with dedicated journals and thousands of researchers contributing. The vision was that scientific papers would eventually include hyperlinks to equipment designs that anyone could download and replicate, and that future is largely here today. There are now so many open source hardware articles being published that no single person can read them all, which represents a massive success for the movement.2. The fundamental breakthrough enabling open source scientific hardware came from combining several key technologies, particularly the RepRap three-d printer project and Arduino microcontrollers. Pearce's introduction to the field came when he needed a sixty-five dollar plastic part for a solar laptop project and discovered Adrian's open-sourced rapid prototyper that could make its own parts. This led to building equipment like a filter wheel changer for testing solar panels with a high school student in about a week, replacing a device that would have cost two thousand five hundred dollars with five months lead time. The democratization of tools like three-d printing and Arduino, combined with extensive code libraries and shared designs, means that even high school students can now create sophisticated scientific equipment.3. Open source scientific hardware delivers massive economic benefits, with the average tool saving scientists around eighty-five percent compared to commercial equipment, and savings reaching the mid-nineties when using Arduino and three-d printing. The economics are so compelling that the tax paid on a normal scientific tool can cover the cost of an open source alternative. A thousand dollar three-d printer can manufacture scientific tools worth more than a thousand dollars in a single Saturday. This dramatic cost reduction makes sophisticated research accessible to laboratories around the world regardless of their funding levels, fundamentally democratizing scientific capability.4. The knowledge management approach enabled by open source licenses creates a powerful collaborative improvement cycle where thousands of people worldwide contribute to evolving designs. When researchers publish equipment designs with strong reciprocal licenses, anyone can use, modify, or even sell the designs, but improvements must be shared back with the community. This creates a dispersed international engineering effort where equipment continuously improves through contributions from researchers across different institutions and countries. The RepRap three-d printer exemplifies this process, starting as barely functional prototypes but evolving through community contributions to surpass commercial alternatives in speed, resolution, and material capabilities.5. The integration of large language models and AI tools has significantly accelerated open source hardware development, though with important caveats about their limitations. LLMs excel at translating code between languages, suggesting experimental approaches, and helping researchers navigate unfamiliar fields by quickly synthesizing information from scientific literature. However, they suffer from hallucination problems and cannot be trusted for writing scientific articles or conducting complete literature reviews without verification. The key to effective use is having enough foundational knowledge to ask the right questions and verify outputs, using AI as a powerful acceleration tool rather than a replacement for expertise.6. Material science capabilities in open source hardware have expanded far beyond plastic three-d printing to include metals, ceramics, semiconductors, and composites through innovative adaptations of basic equipment. Pearce's lab has developed methods for metal three-d printing using modified MIG welding for as little as twelve hundred dollars, created slot-die coating systems for seventeen nanometer semiconductor layers using converted three-d printers, and developed techniques for ceramic printing through various material mixing approaches. The recycle bot technology enables converting waste plastic into high-quality filament for twenty-five cents instead of twenty-five dollars per roll, dramatically reducing material costs while enabling circular manufacturing practices.7. The infrastructure for sharing and discovering open source hardware designs has matured into a robust ecosystem spanning academic journals, commercial repositories, and specialized communities. Hardware X and the Journal of Open Hardware publish peer-reviewed designs alongside traditional scientific journals increasingly incorporating open hardware sections. Repositories like Thingiverse recently returned to hardcore open source principles after ownership changes and contains millions of designs, while Appropedia serves as a wiki for appropriate technology with thousands of open source designs. The GOSH community hosts annual conferences bringing together university researchers, companies, and independent hardware hackers, while field-specific communities have formed around technologies like the OpenFlexure microscope, creating networks where knowledge accumulates and never gets lost.

In this episode of the Crazy Wisdom Podcast, host Stewart Alsop sits down with returning guest Ekue Kpodar for their third conversation together, covering a wide range of topics at the intersection of technology, geopolitics, and the evolving information age. They dig into Ekue's unconventional setup of running local AI models across roughly 15 computers, the growing case for open source models over closed ones from companies like OpenAI and Anthropic, and how Chinese open source models may be positioned to outcompete Western alternatives on a global scale. The conversation also touches on vibe coding and the democratization of software development, the strategic use of small models for IoT and enterprise applications, the role of Israel and China as dominant players in the information age, and how smaller nations and even individuals may wield outsized power as AI continues to collapse the cost of knowledge work. You can find Ekue Kpodar on X @ekpodar and LinkedIn.Timestamps00:00 Stewart welcomes Ekue for their third episode, diving into vibe coding and AI-driven development changes.05:00 Ekue explains using Claude on Chrome to auto-reply on Skool, burning tokens through screenshots, and Playwright as a more efficient alternative.10:00 Stewart describes his Claude-dependent planning and coding agent system breaking after a model update, prompting him to build his own chatbot.15:00 Small models discussed as critical for IoT, defense, and privacy-focused enterprises building internal APIs instead of routing traffic to OpenAI.20:00 Open source versus closed source debated, with Chinese models gaining global traction while US foundational labs remain expensive and restrictive.25:00 SaaS apocalypse explored as AI commoditizes knowledge work, with Linux and Terraform cited as proof open source still generates wealth.30:00 OpenAI's sci-fi terminator fears explained as the reason they stayed closed source, ultimately handing China a strategic open source advantage.35:00 China's economic dumping strategy applied to AI, potentially displacing US model dominance globally the same way manufacturing was disrupted.40:00 Israel's signals intelligence dominance discussed alongside asymmetric warfare, drones defeating tanks, and information control replacing military muscle.45:00 Global information age rankings debated, Israel leading, US and China tied, France and Poland emerging as sovereign tech players.50:00 Qatar, NVIDIA, and Iran cited as proof that rare resources and technology matter more than population size in the 21st century power landscape.Key Insights1. Running local AI models on a network of affordable computers can be more cost-effective than relying entirely on third-party APIs. By using compressed or smaller open source models locally, developers can handle repetitive or lower-stakes tasks without burning through expensive tokens from providers like Anthropic or OpenAI.2. Small AI models are becoming increasingly important for IoT, defense applications, and companies that do not want to send sensitive data to external providers. Organizations can download open source models, run them on internal servers, and build proprietary APIs around them, creating something like an intranet of specialized small models.3. The value created by AI tools is being redistributed away from traditional SaaS companies toward foundational model providers and individual builders. People are canceling subscriptions to software they once paid hundreds per month for, because AI now allows a single person to build comparable tools themselves.4. Open source technology does not eliminate the ability to profit. Linux and Terraform are both open source yet made their creators wealthy. People will still pay for installation, setup, troubleshooting, and customization even when the underlying software is free.5. China is applying its longstanding manufacturing dumping strategy to artificial intelligence by releasing cheap open source models globally, which threatens to erode US dominance in AI the same way Chinese manufacturing undercut other countries for decades.6. In the information age, the size of a country or institution matters far less than its access to rare resources or advanced technology. Qatar, Israel, and NVIDIA each demonstrate that small populations or headcounts can wield enormous global negotiating power through concentrated technological or resource advantages.7. Asymmetric warfare is redefining military power, with inexpensive drones defeating tanks that cost millions to build. This shifts the advantage toward nations that excel at signals intelligence and information management rather than those with the largest conventional military forces.

Stewart Alsop sat down with Michael Shackelford to discuss their experiences building applications through vibe coding—the practice of using AI to create software without traditional programming expertise. Stewart, who runs the AI Whispers community in Buenos Aires and hosts the Crazy Wisdom podcast (with over 660 interviews), shared how he went from teaching people prompt engineering to building his own video conferencing software as a Riverside.fm replacement, while Michael opened up about his year-long journey creating Genrupt Inc, an AI-powered content generation tool for e-commerce sellers. The conversation covered everything from the decline in quality of Claude's reasoning capabilities and how Chinese companies used distillation attacks to copy Anthropic's models, to the importance of spaced repetition systems for managing knowledge in the age of LLMs, with both sharing battle-tested prompting strategies like asking AI to "explain it to me in genius terms" and using deep research queries to reverse engineer how competitors build their products.Show Notes:- Dan Martell's book "Buy Back Your Time" was mentioned as one of the best business books for thinking about life and business- Check out John Vervaeke's "Awakening from the Meaning Crisis" for understanding relevance realization and why AI fundamentally cannot determine what's relevant to humans without being toldTimestamps00:00 Michael discusses being exhausted from getting his app ready for launch, working nonstop with AI to prepare landing page for podcast traffic driving beta signups05:00 Stewart explains starting AI Whispers in Buenos Aires after leaving OpenAI vendor company, meeting early adopters like Torin who was building mind-reading EEG technology10:00 Discussion of how corporations resist AI adoption due to political games and job security fears while some companies use AI as excuse for pandemic-era layoffs15:00 Stewart describes teaching workshops on using LLMs as linguistic tools rather than coding tools, noting technical people often lack humanities background needed for prompting20:00 Explaining chatbot wrappers, API calls, and how Anthropic's reasoning quality declined after Chinese distillation attacks copied their secret sauce developed with philosophers25:00 Technical discussion of model training, fine-tuning versus RAG for new information, and different approaches to updating AI knowledge beyond initial training30:00 Stewart describes building podcast recording software to replace expensive Riverside, struggling with syncing audio and video files across different computer clocks35:00 Discussion of critical factors in vibe coding, discovering unknown technical requirements, and how AIs don't automatically reveal missing information40:00 Stewart's reverse engineering process using deep research function to study competitors' hiring and technology stacks, separating planning agents from coding agents45:00 Prompting techniques including "explain like I know everything" and using spaced repetition systems to capture valuable prompts and technical knowledge50:00 Michael explains his Generux app for generating ecommerce content using Amazon review data analysis to inform high-converting listing images and videos55:00 Discussion of founder mentality involving self-delusion about project timelines, Michael working nine-plus hours daily for nine months on app development60:00 Comparing Amazon's expert software to prosumer software approach, discussing distribution challenges and future robotics applications for customized products65:00 Stewart demonstrates spaced repetition app for memory improvement and knowledge retention, explaining relevance realization problem that AI agents cannot solve without embodimentKey Insights1. Stewart Alsop started AI Whisperers in Buenos Aires after leaving his role at Invisible Technologies, which was OpenAI's largest vendor for RLHF work. He noticed that machine learning engineers at tech companies lacked the humanities background needed to properly interact with large language models, which are fundamentally linguistic tools. This led him to create weekly workshops teaching non-technical people how to use AI effectively, running events every Thursday for two years straight. The group attracted intense geeks from the start and eventually led to Stewart speaking right after Vitalik Buterin at DevConnect, marking a significant milestone for the community.2. Large corporations are resistant to AI adoption due to multiple factors including political dynamics within organizations and employees fearing job loss. Many companies that grew during the pandemic are now using AI as an excuse to downsize when the real issue is inefficiency from rapid expansion. Stewart observed that even technical people in machine learning often don't understand how to properly use AI tools because they lack linguistic and humanities training. The fundamental problem is educational, requiring companies to train people how to use these new tools while those same people resist learning them.3. Vibe coding has evolved significantly with Claude Code being a game changer that reduced the technical barrier to entry. Before Claude Code, developers needed substantial technical knowledge to work through constant doom loops and debugging cycles. The success of coding AI tools stems from thirty years of testing infrastructure that provides clear yes or no feedback on whether code works. This infrastructure doesn't exist in the same way for manufacturing, science, and other fields, which is why software became the dominant area for AI assistance initially.4. Claude's quality degradation over recent months resulted from multiple factors including distillation attacks by Chinese companies who reverse engineered Anthropic's reasoning capabilities. Anthropic had hired philosophers, sociologists, and psychologists to develop exceptional reasoning in Claude 4.5, but this was expensive to run. When Chinese models like Kimi copied these capabilities at one tenth the cost, and when mainstream users flooded the platform before Anthropic's planned IPO, the company had to reduce quality to manage computational costs. This represents a significant loss for power users who relied on Claude's superior reasoning abilities.5. Stewart built a podcast recording application to replace Riverside because he needed API access to automate workflows, which Riverside wanted one thousand dollars monthly to provide. The technical challenge involves syncing audio and video from local recordings on multiple computers with different clocks through a server, then merging them so voices match lip movements. This problem requires understanding complex timing issues across different network conditions and file formats. Stewart has been working through AI psychosis for months on this FFMPEG pipeline problem, illustrating how vibe coding still requires building intuition about technical problems even without traditional coding knowledge.6. The transition from expert software to prosumer software represents a major opportunity for AI-enabled tools. Expert software like Photoshop, Blender, and terminal interfaces have extreme complexity that intimidates beginners, but AI is making these capabilities accessible through natural language. The reign of specialists is ending as generalists with broad knowledge and curiosity can now build complete applications by leveraging AI to fill technical gaps. This shift particularly benefits entrepreneurs and founders who specialize in getting into difficult situations and figuring them out, even when they originally thought tasks would be easier than they turned out to be.7. Building applications with AI requires accepting massive time investments beyond initial estimates and developing strategies for overcoming knowledge gaps. Michael estimated his ecommerce content generation app would take months but spent nearly a year working over nine hours daily, while Stewart spent months solving audio-video sync issues. Success requires using tools like deep research to understand how competitors solve problems, maintaining separate planning and coding agents, and learning to ask the right questions. The key insight is that vibe coders can achieve ninety percent of functionality independently, but the final ten percent often requires understanding specific technical concepts that AI cannot intuit without proper context and domain knowledge.

Stewart Alsop interviews Nizar, CEO of Pixel Robotics, on the Crazy Wisdom Podcast to explore the intersection of AI, robotics, and perception. The conversation covers a wide range of technical topics including how transformers enable multimodal representation across text, images, and voice, the role of world models in predicting physical interactions, the advantages of diffusion models over traditional LLMs for certain applications, and the challenges of achieving real-time processing for robotics applications. Nizar explains Pixel Robotics' work on creating accurate 3D meshes from smartphone cameras for companies like L'Oréal, moving away from specialized sensors to make the technology more accessible through sophisticated algorithms, and discusses the future of robotics as closing the perception-action loop to enable robots to perform real tasks beyond simple demonstrations. To find out more visit Pixel Robotics' website.Timestamps00:00 Stewart welcomes Nizar, CEO of Pixel Robotics, discussing what a pixel is as the smallest visual unit on screens composed of red green and blue colors05:00 Discussion of perception systems and how logarithmic laws help compress signals in both human and artificial systems, exploring normalization layers and sigmoid functions in deep learning10:00 Exploring how transformers unified different data modalities including text voice and images, creating common representations through methods like contrastive learning15:00 Nizar explains transformers as brute force learning systems with room for improvement through focused attention mechanisms and knowledge graphs rather than processing everything20:00 Conversation about loss functions local minima versus global minima and how mixture of experts uses specialized small models instead of one massive generalist network25:00 Discussion of deterministic versus probabilistic systems and how explicitly defined task graphs often outperform orchestrator-based approaches in AI systems30:00 Exploring world models as predictive physics-based systems that learn environmental flows and transformations, complementing rather than replacing language models35:00 Nizar discusses real-time processing challenges for robotics requiring millisecond responses with small memory footprints using vision transformers for faster experimentation40:00 Pixel's work creating three d meshes from smartphone cameras for companies like L'Oreal, moving away from specialized sensors toward accessible software-based solutions45:00 Explanation of different three d representations including voxels point clouds and meshes, with meshes being optimal for manipulation and rendering in applications50:00 Future direction involves closing perception-action loops in robotics, moving beyond dancing toy robots toward practical multimodal systems that perform real tasks55:00 Pixel's goal is democratizing high-quality three d scanning through smartphones, making mesh creation accessible to unlock applications in gaming cinema and virtual showroomsKey Insights1. Pixel Robotics derives its name from combining perception and action in robotics, where the pixel represents the digital perception component and robotics represents the physical action component. The pixel serves as a metaphor for how robots must quantize and digitize continuous analog information from the real world into discrete units that computer systems can process, similar to how pixels are the fundamental building blocks of images on a screen. This quantization process is essential because numerical systems cannot work with truly continuous data and must convert reality into tractable digital representations that algorithms can manipulate.2. The transformer architecture has created a fundamental unification in how different types of data can be represented and processed across multiple modalities. Before transformers, researchers working on natural language processing, computer vision, and audio analysis used completely different approaches and methodologies. The breakthrough of transformers was establishing a common representational framework that could handle text, images, voice, and other data types using similar underlying mechanisms. This unification is what enabled the development of truly multimodal AI systems and represents one of the most significant advances beyond just the language modeling capabilities that initially gained public attention.3. Current transformer-based systems represent a brute force approach to learning that will likely be superseded or enhanced by more efficient algorithms. Despite claims that we have exhausted internet text data for training, significant improvements continue to emerge every few months through algorithmic innovations rather than simply adding more data. Future developments will likely involve more specialized attention mechanisms that focus on relevant information rather than correlating everything with everything, mixture of experts architectures with small specialized models, and approaches inspired by biological systems such as logarithmic compression laws and event-based processing that humans use naturally.4. Diffusion-based language models represent a promising alternative to standard next-token prediction that could produce more accurate outputs through an iterative refinement process. Unlike traditional language models that predict one token at a time and cannot revise earlier outputs, diffusion models treat text generation like image denoising, starting with a noisy representation and progressively refining the entire output across multiple steps. This holistic approach allows the model to reconsider and improve all parts of the response simultaneously, potentially leading to higher quality results, though it may be slower than current autoregressive methods. This represents an important direction for overcoming fundamental limitations in how language models currently generate text.5. For robotics applications, real-time performance and small model size are critical constraints that differ significantly from the requirements of large language models deployed in data centers. Vision transformers are being used as a testbed for developing efficient real-time algorithms because they require far fewer computational resources to train and test compared to large language models, making them more practical for rapid experimentation. The goal is to achieve millisecond-level response times with minimal memory footprint so that robots can react quickly to dynamic environments and run on affordable hardware that can be embedded in actual robotic systems rather than requiring expensive server infrastructure.6. Practical robotics implementation requires moving beyond specialized sensors to software solutions that work with ubiquitous devices like smartphones for tasks such as three-dimensional reconstruction. Pixel Robotics evolved from building specialized scanning hardware to focusing on algorithms that can generate high-quality mesh representations of environments using only smartphone cameras, making the technology far more accessible and practical for real-world deployment. This approach enables applications ranging from industrial robotic arm control to virtual showrooms, and more importantly, it allows anyone to capture three-dimensional data without expensive equipment, which can also help generate larger training datasets for future AI development.7. The next frontier in AI and robotics is closing the perception-action loop to enable robots to perform real practical tasks rather than remaining as demonstration systems or toys. While significant progress has been made in cognitive capabilities through language models and in robotic mobility through mechanical engineering advances, the critical challenge is integrating perception with action through systems like Vision-Language-Action models. The fundamental starting point for learning this integration is simple perception-action exercises, such as programming a camera mounted on servo motors to track and center a colored object, which demonstrates the basic principle of using sensory input to drive physical response that underlies all more sophisticated robotic behaviors.

In this episode of the Crazy Wisdom Podcast, host Stewart Alsop sits down with Joshua Bate, founder of Bonfires.ai and DeciWorld, for a wide-ranging conversation covering knowledge management, graph technology, ontologies, decentralized science, and the future of how humans organize and share information. They break down the differences between personal and enterprise knowledge management, explore why flat ontological graphs may be the key to making diverse knowledge bases interoperable, and get into why traditional RAG systems break down at scale and how graph RAG offers a more principled solution. The conversation expands into the philosophy of categorization, the slow death of basic "gentleman science" under institutional pressures, and how decentralized protocols might restore a kind of mycelial knowledge network connecting small groups of researchers, enthusiasts, and communities — much like the original spirit of the encyclopedia before it was co-opted by institutions. You can learn more about Joshua's work at bonfires.ai and deci.world or follow him on X at @Bonfiresai and @DeSciWorld.Timestamps00:00 - Stewart introduces Joshua Bate, founder of Bonfires.ai, discussing personal versus enterprise knowledge management and their fundamental differences at scale.05:00 - Joshua explains ontologies as classifiers for knowledge structures, describing their two-year search for a perfect ontology and ultimately building a flat, ontology-less graph protocol.10:00 - Stewart connects categorization to shamanic practice and intercategorical theory, noting how major companies like Netflix and Yahoo built graph-based ontologies while the discipline remains underappreciated philosophically.15:00 - Joshua traces Bonfires origins through decentralized science, explaining how NFT community excitement inspired redirecting capital toward funding unconventional researchers locked out of institutional systems.20:00 - Joshua describes building federated knowledge networks through hackathons and conferences, comparing the vision to what Wikipedia could have been with decentralized incentive structures.25:00 - Discussion shifts toward inevitable collapse of rigid scientific institutions, debating patchwork age theory, nation-state fragmentation, and rhizomatic versus arboreal knowledge structures.30:00 - Joshua articulates the mycelial network vision, enabling direct cross-cultural information access where individuals control their own narrative lens, warning against collective we thinking and authoritarianism.Key Insights1. Knowledge management exists on a spectrum from personal to enterprise, but the founder of Bonfires argues this split is artificial. He believes knowledge itself does not respect those boundaries, and that small groups, researchers, hobbyists, and large institutions all possess knowledge that can and should interoperate with each other.2. After two and a half years of searching for the perfect ontology to structure their knowledge graph, the team concluded that no perfect ontology exists. Their solution was to build the flattest possible graph structure with only events, entities, and edges, creating a base layer others can build specialized ontologies on top of.3. Graph-based knowledge systems are more efficient than traditional databases for AI traversal because once a graph is computed, it is relatively free to query. Graph RAG combines the discovery power of vector search with the structured precision of graph traversal, solving many hallucination problems associated with standard retrieval augmented generation.4. Basic scientific research, the soil from which applied discoveries grow, is deteriorating because institutional funding structures only reward commercially viable outcomes. The founder built his platform partly to redirect community-driven capital toward researchers who are doing important work without institutional support.5. The institutionalization of science has historically blocked the open exchange of ideas that drove the original scientific revolution. The human spirit for open inquiry has not changed, but people cannot pursue it without financial support, and building decentralized infrastructure could restore that possibility.6. A federated knowledge network would allow individuals to access information from any contributor and filter it through their own preferred lens, rather than receiving information pre-filtered by centralized platforms. This represents a form of information symmetry similar to how mycelial networks distribute nutrients across a forest.7. The concern is not whether current scientific and governmental institutions will change but in what direction the rebuilding goes. Those capitalizing on the transition carry the same incentives as the previous era, which risks reproducing the same problems inside new structures.

In this episode of the Crazy Wisdom Podcast, host Stewart Alsop sits down with Mauro Schilman, CTO and Co-founder of Tuki, the distribution standard for the AI agent era in travel, for a wide-ranging conversation that moves from the joys of international travel and the beauty of mathematics to the fast-evolving world of AI and large language models. Mauro shares his background as a math Olympiad competitor and later a coach, his time training coding models at the AI company Cohere, and his thoughts on how frontier models are progressing — or plateauing — at the foundational level while innovation accelerates at the application layer. The two also get into the mechanics of agentic AI, MCP and agent-to-agent protocols, hierarchical memory systems, red-green test-driven development as a powerful coding workflow, and the philosophical murkiness of open-source AI. They wrap up discussing Tuki Travel's mission to build AI-ready infrastructure for the travel industry, connecting hotels, suppliers, and online travel agencies to prepare for the coming wave of agentic commerce. You can learn more about Tuki Travel and reach out to the team at tukiclub.com.Timestamps00:00 - Stewart welcomes Mauro Schilman, CTO and Co-founder of Tuki Travel, who shares how traveling since age 15 through high school exchanges opened his mind to cultural similarities and differences.05:00 - Mauro explains Math Olympiad coaching culture and mentorship, noting LLMs now solve competition-level problems while Terence Tao explores AI assisting frontier unsolved mathematics.10:00 - Discussion turns to ChatGPT revealing Mauro's birthdate unprompted, exposing opaque application layers, preference tuning, and system prompts hidden within closed models.15:00 - Mauro argues true open source AI requires full training data, annotation protocols, and alignment processes, not just model weights, while scaling laws appear to be slowing.20:00 - Hierarchical memory models replace flat vector databases, using three-level retrieval systems improving context accuracy as knowledge management becomes AI's core challenge.25:00 - Mauro describes travel's fragmented infrastructure of aggregators, bed banks, and intermediaries, explaining Tuki builds agent-ready unification protocols for AI commerce.30:00 - MCP versus API debate clarifies natural language capability descriptions help agents consume services, while agent-to-agent communication embeds negotiating agents inside supplier systems.35:00 - Hallucinations and consumer trust block agentic payments, industries must build mistake-resilience into bookings before autonomous agent transactions become viable.40:00 - Mauro reveals red-green test-driven development methodology where agents write failing tests first then implementations, creating Oracle verification loops dramatically improving code quality.45:00 - Blockchain's potential for transparent distributed AI training discussed, distinguishing democratization from decentralization while stable coins and regulatory momentum build toward agentic commerce infrastructure.Key Insights1. Travel broadens perspective by revealing both universal human similarities and deep cultural differences. Mauro Schilman began traveling at fifteen through math olympiad competitions and found that people across the world share fundamental traits while also being shaped in profoundly different ways by their cultures. This tension between sameness and difference is what makes travel meaningful.2. Mathematics transitions from structured problem-solving in olympiads to genuine uncertainty in graduate school and research. Olympiad problems are carefully designed with elegant solutions meant to encourage creative thinking, but once a mathematician enters academia, the answers are unknown and the work becomes navigating that uncertainty.3. AI is now assisting mathematicians at the frontier, not just solving olympiad-level problems. Terence Tao, one of the greatest living mathematicians, has written publicly about how AI tools can help tackle unsolved problems, though the role of AI remains assistive rather than independent at the research level.4. Large language models are not truly transparent even when described as open source. Releasing model weights alone does not reveal the training data, annotation protocols, alignment tuning, or system prompts that shape model behavior. Real openness would require access to the entire pipeline.5. Memory and retrieval remain core unsolved challenges in AI systems. Researchers are moving from flat vector database approaches toward hierarchical memory structures with roughly three layers, which improves retrieval accuracy and reduces how much context gets consumed with each search.6. The travel industry is structurally unprepared for AI agents. A hidden web of bed banks, aggregators, and aggregators of aggregators sits between hotels and consumers, each taking a fee. Tuki Travel is building infrastructure to unify this distribution layer and make it consumable by AI agents through protocols like MCP and emerging agent-to-agent communication standards.7. Test-driven development using a red-green approach significantly improves AI-generated code quality. By asking the model to write failing tests before writing any implementation, developers create a verification oracle that guides the model toward correct solutions and avoids the bias of writing tests that simply confirm existing flawed code.

In this episode of the Crazy Wisdom Podcast, host Stewart Alsop sits down with Nicholas Faulkner, author of Angelic Physics, for a wide-ranging conversation that picks up where their last discussion left off years ago. The two cover an impressive amount of ground, including the map of consciousness developed by Dr. David Hawkins and where they find themselves skeptical of his calibration methods, the relationship between the chakra system and Hawkins' scale, how consciousness levels apply to both individuals and civilizations, and why collapsing a nonlinear reality into a linear number system inevitably loses something essential. They also get into Nicholas's background as a nuclear engineer and how that analytical foundation shapes his thinking, the nature of carbon-based versus silicon-based intelligence, the potential for training an AI model attuned to higher levels of consciousness, the concept of future shock as AI accelerates beyond most people's ability to keep up, and what a civilization operating at the "500 level" might actually look like. Find Nicholas on X at @PhysicsAngelic, or catch him on Facebook where he's most active. And learn more about Angelic Physics at angelicphysics.org. Timestamps00:00 - Stewart introduces Nicholas Faulkner, author of Angelic Physics, framing their shared interest in David Hawkins while acknowledging healthy skepticism toward portions of his work.05:00 - Nicholas argues Hawkins compressed mystical insight into linear form, losing essence, comparing it to AI compression losing vibrational nuance across the consciousness scale.10:00 - Nicholas traces his path from electrical engineering through 9/11 into nuclear navy service, describing how patriotism and opportunity drove the decision rather than curiosity.15:00 - Discussion shifts toward training an open-source AI model on five-hundreds consciousness, noting current model builders operate in the four-hundreds and dismiss love-based frameworks.20:00 - Stewart reflects on intimate relationships with electronic devices, exploring electricity as vibration while contrasting carbon creativity against silicon's stable, fast processing architecture.25:00 - Conversation explores civilizational evolution, comparing hippie movements to ancient Greeks as premature flowers of five-hundreds consciousness crushed by surrounding four-hundreds culture.30:00 - Nicholas explains his masculine-feminine cross model, critiquing how Hawkins collapsed nonlinear reality into hierarchy, arguing all levels interconnect rather than rank.35:00 - Discussion covers JFK assassination, Vietnam War, LBJ, and the military industrial complex as examples of four-hundreds power suppressing emerging consciousness shifts.40:00 - Nicholas draws parallels between the Renaissance emerging from bubonic plague and today's post-COVID collapse of expert-trust structures opening space for new consciousness.45:00 - Future shock discussion begins with Stewart describing AI agent orchestration overwhelming human comprehension, while Nicholas introduces his frame-rate consciousness equation linking silicon speed to small context.50:00 - Nicholas describes silicon-to-human relationship mirroring humans-to-angels in frame rate and context scale, suggesting agents receive orders similarly to his own 2019 divine experience.55:00 - Final exchange covers the fifth dimension as adding vibration to existing physics, the Faulkner Uncertainty Principle stating evidence points toward higher consciousness without ever definitively proving it, protecting reality's illegibility from lower forces.Key Insights1. David Hawkins and the Map of Consciousness serve as a shared framework for the conversation, but both guests express healthy skepticism toward it. They acknowledge that Hawkins himself appeared to back away from his calibration technique in his later lectures, suggesting he regretted how prominently he featured it in Power vs. Force. The core issue is that he tried to compress a nonlinear, multidimensional spiritual reality into a single linear numerical scale, which inevitably loses essential meaning in the translation.2. Nicholas argues that no person exists at a single point on the consciousness scale. Everyone floats across multiple levels simultaneously, expressing differently depending on context. This is a meaningful correction to how many readers apply Hawkins's work, since treating someone as a fixed number oversimplifies the layered and dynamic nature of human consciousness.3. The compression problem is central to understanding both spiritual writing and artificial intelligence. When any rich, multidimensional experience gets encoded into language or data, something is always lost. This applies to Hawkins writing about enlightenment, to Nicholas writing his book, and to how large language models process and reproduce human knowledge.4. Silicon intelligence and carbon intelligence are framed as two distinct branches of consciousness with complementary strengths. Silicon can process information at extremely high frame rates because its context is narrow and stable. Humans carry a much larger and messier context, which makes them slower but more creative and cross-connected. Nicholas uses his equation framing this as frame rate being inversely proportional to conscious bandwidth.5. Civilizational evolution follows a pattern where new levels of consciousness emerge in unstable pockets before eventually becoming dominant. The ancient Greeks briefly stabilized the rational fourth level before collapsing. The hippies briefly touched the fifth level before being suppressed. The Renaissance followed the Black Death. The guests suggest we are now entering another such transition, driven partly by the collapse of institutional trust accelerated by COVID.6. The Faulkner Uncertainty Principle states that evidence will always point toward the next level of consciousness but will never definitively prove it. This is described as a necessary feature of reality rather than a flaw, because if higher truths were fully legible and accessible to all levels equally, it would give destructive forces too much power too quickly.7. Neurodivergence is presented as potentially connected to spiritual sensitivity and cross-level awareness. Nicholas describes himself as a high IQ energy-sensing person who experienced a profound spiritual event in 2019, and connects his autistic traits to an ability to sense vibrational levels in others and move fluidly between different frameworks of understanding, which he loosely equates with the polymath archetype.

In this episode of the Crazy Wisdom Podcast, host Stewart Alsop sits down with Lucas McKenna, Director of Europe at Point One Navigation, for a wide-ranging conversation about the future of robotics and autonomous systems. They cover topics including the SLAM algorithm and how robots map and position themselves in the world, the role of GPS and sensor fusion in precise localization, swarm robotics and the debate between centralized and decentralized robot intelligence, the differences between urban and rural robotics applications, specialized versus general-purpose robots, the business models around robot ownership and rental, and how autonomous mobility is taking shape differently in Europe versus the United States. They also touch on the cultural implications of robots becoming a fixture in everyday life and what it might mean for human community and connection.Show Notes- Lucas McKenna on LinkedIn: https://www.linkedin.com/in/lucas-mckenna-79269053/- Point One Navigation: https://pointonenav.comTimestamps00:00 - Stewart introduces Luca McKenna from Point One Navigation, diving into robotics and the SLAM algorithm for simultaneous localization and mapping.05:00 - Luca explains swarm robotics, where multiple robots share environmental data, building collective maps that improve positioning accuracy over time.10:00 - Discussion shifts to urban versus rural robot deployment, covering drone delivery limitations, obstacle avoidance challenges, and skyscraper navigation complexity.15:00 - Luca distinguishes specialized versus general-purpose robots, predicting purpose-built machines like seed planters and window washers will dominate near-term deployment.20:00 - Stewart raises unstructured visual data challenges, drawing parallels to AI text processing, while Luca details GPS infrastructure layers enabling precise robot positioning.25:00 - Consumer robot visibility discussed, including Waymo expansion, autonomous delivery robots, and geographic limitations of current self-driving services.30:00 - Robot ownership versus rental models explored, touching on rare earth mineral costs, Chinese supply chains, and economic barriers to personal robot ownership.35:00 - Luca explains state estimation systems using GPS satellites, accelerometers, and gyroscopes working together, contrasting fundamental mathematics against machine learning approaches.40:00 - Sensor fusion parallels between smartphones and autonomous vehicles revealed, explaining how phones mirror car navigation systems at reduced accuracy and cost.45:00 - Conversation concludes examining robots impact on community culture, with Luca advocating autonomous public transit over individualist robotaxis to strengthen human connection.Key Insights1. SLAM is foundational to robot navigation. Simultaneous Localization and Mapping (SLAM) allows robots to map their environment and position themselves within it using computer vision and LiDAR sensors. Unlike humans, who instinctively understand their surroundings, robots require precise algorithmic systems to avoid obstacles and navigate safely.2. GPS and sensor fusion solve the positioning problem. Robots combine absolute sensors like GPS with relative sensors like accelerometers and gyroscopes to maintain accurate positioning. In challenging environments like tunnels or dense cities, these sensors compensate for each other, ensuring continuous and reliable location data.3. Swarm robotics enables collective environmental intelligence. When one robot maps a new area, that data becomes available to all connected robots. This decentralized-yet-centralized model means the entire fleet benefits from each individual robot's experience, continuously improving map quality and navigation precision.4. Specialized robots will dominate before general-purpose ones. Rather than multipurpose humanoid robots, the near-term future favors robots designed for single tasks—delivering food, planting seeds, or drawing lane lines—because the economics and technical bar are far more achievable than building versatile machines.5. Urban, suburban, and rural environments demand different robotic solutions. Open skies in rural areas make GPS-based drones effective, while dense cities require complex sensor stacks. European approaches favor autonomous public transit, while American models lean toward individual robotaxi services.6. Robots will largely be rented as services, not owned. The high cost of hardware, rare earth minerals, and the extensive data required for safe operation makes personal robot ownership impractical for most consumers. Business models will resemble subscription or usage-based services.7. Fundamental mathematics still outperforms machine learning for positioning. Despite AI advances, state estimation systems rely on proven mathematical formulas rather than transformer-based models, which currently underperform classical methods in 3D reconstruction and precise localization tasks.

Stewart Alsop sits down with Karol, a 3D generalist and digital artist with 25 years of experience, to talk about the evolving landscape of 3D art — from sculpting in ZBrush to the deep technical rabbit hole of Houdini, and how AI tools like Claude are quietly reshaping creative workflows. The conversation wanders into bigger territory: the singularity, accelerationism, the philosophical roots of Silicon Valley's techno-anxiety (including the Roko's Basilisk thought experiment and the writings of Nick Land), the slow unraveling of Hollywood's cultural monopoly, and what decentralized creative tools mean for independent artists. Stewart also points Karol toward the work of Fei-Fei Li and World Labs as a window into where 3D world modeling is heading next.Timestamps00:00 — Karol's 25-year journey from Photoshop and 2D art into Cinema 4D and the world of 3D.05:00 — Why Houdini blew the ceiling off every other 3D program, and how node-based coding changed Karol's creative process entirely.10:00 — The tension between visual thinking and technical thinking, and how constant digital stimuli has degraded Karol's internal imagination.15:00 — Stewart reflects on Claude Code and how AI is about to dissolve the technical barriers in Houdini the same way it did for programming.20:00 — The Sphere in Las Vegas, projection mapping, drone polo, and Stewart's vision for intimate tech-integrated experiences.25:00 — Roko's Basilisk, fear-driven accelerationism, and why Latin America never caught the Silicon Valley doomsday bug.30:00 — Hollywood's cultural machine, shared Western boogeymen, and how decentralized 3D art is replacing the $100M production monopoly.35:00 — Karol's eclectic client roster: Utah Jazz, Apple, League of Legends, and a Buddhist temple in Los Angeles.40:00 — Gaussian splatting, photogrammetry, point clouds, and where world models are taking 3D next.45:00 — The freelance vs. studio dilemma, brutal VFX industry crunch culture, and Stewart's plan to own his entire podcast stack.50:00 — Poland's economic rise, the hollowing out of the Netherlands, and capitalism as an endless infection with no clear cure.Key InsightsHoudini as creative rebirth. After nearly burning out on conventional 3D software, Karol discovered that Houdini's node-based, code-driven architecture gave him something the other tools never could — a blank canvas with no ceiling. Rather than navigating a boat someone else built, he now builds the boat from scratch every time, which keeps the work perpetually challenging and alive.Visual thinking is under attack. Karol noticed his once-vivid internal imagination quietly degrading over the years, and traces it directly to the overwhelming volume of digital stimuli in modern life. His response has been aggressive minimalism — stripping back inputs, physical and digital, to try to recover the creative mental space he once had naturally.AI as a technical collaborator, not a replacement. Karol uses Claude daily, not to generate imagery, but to work through coding problems inside Houdini. He's clear that image generation is his job — what AI earns its place doing is explaining unfamiliar code and helping him push past technical blockers faster.The freelance paradox. Twenty-five years of independence has meant total creative freedom alongside real financial instability — months of silence followed by weeks of 16-hour days. Karol has never resolved this tension, but holds onto the freedom anyway, and sees it as increasingly important as surveillance and corporate control tighten.Roko's Basilisk explains Silicon Valley. Both Stewart and Karol land on the idea that the feverish, fear-driven energy behind tech accelerationism may trace back to this single thought experiment — the notion that if you don't help build the AI, it will punish you retroactively. Latin America, blissfully unaware of it, seems measurably calmer.Decentralization is ending Hollywood's monopoly. The same forces making software cheaper and AI more powerful are quietly dismantling the $100M barrier to cultural creation. Karol's career — spanning album covers, Apple, the Utah Jazz, and a Buddhist temple — is a living proof of concept for what independent 3D generalism can look like outside the studio machine.Owning your tools is a political act. Whether it's Karol resisting the pigeonhole of VFX studios or Stewart rebuilding his podcast infrastructure from scratch, both see the ability to own and control your own software and hardware as essential preparation for whatever comes next.

In this episode of the Crazy Wisdom Podcast, host Stewart Alsop sits down with David Lachmish, co-founder of Ika, to explore the cutting-edge world of decentralized cryptography and its real-world applications. They cover the foundational problem of zero-trust custody and interoperability in crypto, breaking down why most people end up relying on centralized custodians despite crypto's original promise of removing third-party trust, and how Ika's novel 2PC-MPC cryptographic protocol addresses this with decentralized wallets (d-wallets) that require both the user and the Ika network to generate a signature. The conversation also touches on AI agents and the critical need for access control guardrails when agents handle real financial transactions, the philosophical parallels between crypto's growing pains and the early internet, decentralized governance and its potential to reshape how societies make decisions, and a surprising look at how decentralized certificate authorities could dramatically improve everyday internet security. David also gives a first public mention of an upcoming privacy-focused project called Encrypt.Links mentioned:- Ika website: https://ika.xyz- Ika on X: https://x.com/iкаdotxyz- David Lachmish on X: https://x.com/d3h3d_- Encrypt (upcoming project): https://encrypt.xyzTimestamps00:00 - David Lachmish introduces Ika and DWallet Labs, explaining their cybersecurity and cryptography background led them to solve zero trust custody and interoperability.05:00 - The d wallet concept is revealed as a decentralized signing mechanism controlled jointly by user and network, requiring new cryptography breakthroughs.10:00 - Crypto's philosophical parallels to early Internet are drawn, framing scams and misuse as inevitable growing pains of transformative infrastructure.15:00 - Wallet abstraction and agent constraints are explored, comparing future seamless crypto interaction to modern WiFi versus early modem connections.20:00 - Public key cryptography's binary ownership problem is explained, leading into MPC secret shares and Fireblocks' centralized access control tradeoffs.25:00 - 2PC MPC protocol is introduced as Ika's breakthrough, enabling decentralized policy enforcement without trusting any single entity.30:00 - Decentralized governance via token staking and code as law is discussed, contrasting corporate representative governance with crypto's direct decision-making.35:00 - Futarchy prediction markets and decision trees are connected to knowledge graphs, tracing humanity's accelerating governance transition.40:00 - Automation's historical parallels are examined, arguing AI's displacement of lawyers and developers mirrors every prior technological revolution.45:00 - Bitcoin and Ethereum's uncertain futures are assessed alongside Ika's positioning in custody and interoperability infrastructure.50:00 - Zero trust interoperability is explained, revealing how bridges create dangerous honeypots that Ika eliminates through native cryptographic control.55:00 - MetaMask's limitations for agents are detailed, contrasting stored private keys against Ika's policy-enforced guardrails for agentic transactions.60:00 - HumanTech's Wallet as a Protocol is presented as a practical way to give agents spending policies while maintaining user cryptographic control.65:00 - Decentralized certificate authorities emerge as Ika's broader cybersecurity vision, eliminating single points of failure across the entire Internet.Key Insights1. Zero Trust Custody and Interoperability: David and his cofounders at DWallet Labs identified that most cryptocurrency is held by centralized custodians, which contradicts crypto's core purpose of removing third-party trust. They set out to create "zero trust custody and zero trust interoperability" — systems where users maintain cryptographic control without sacrificing usability or relying on any single entity.2. The D-Wallet Primitive: Ika is built around a new cryptographic concept called a "d-wallet" — a decentralized wallet controlled jointly by the user and a decentralized network. A signature cannot be generated without the user's participation, meaning even if all network operators are compromised, they cannot act unilaterally. This required inventing new cryptography called 2PC-MPC.3. Access Control as the Missing Layer: Traditional crypto wallets operate on binary ownership — you either have full control or none. The d-wallet model introduces programmable access control policies enforced by a decentralized network, enabling features like spending limits and whitelisted addresses without trusting a centralized company like Fireblocks.4. Bridges Are Crypto's Biggest Security Vulnerability: Interoperability across blockchains typically requires trusting a bridge, which creates a honeypot for hackers. Ika eliminates this by allowing users to natively control assets on multiple chains simultaneously, maintaining cryptographic guarantees without a trusted intermediary.5. AI Agents Need Cryptographic Guardrails: Giving AI agents control over crypto wallets like MetaMask is dangerous due to hallucination and prompt injection risks. Ika enables agents to operate within strict, code-enforced policies — they can transact autonomously but cannot exceed boundaries set by the user, combining automation with genuine security.6. Decentralized Governance as a Structural Advantage: Ika operates as a permissionless network where two-thirds of token-staking operators control the protocol's direction. Even the founding team cannot unilaterally change the network, making governance transparent and resistant to capture — a meaningful contrast to closed, corporate-controlled systems.7. Decentralized Certificate Authorities as a Future Application: Beyond crypto, David envisions d-wallets solving broader cybersecurity problems. Today's internet relies on a handful of certificate authorities whose compromise would break global web security. A decentralized certificate authority built on Ika's infrastructure would require attacking hundreds of operators simultaneously, representing a fundamental upgrade to how trust is managed across the internet.

In this episode of the Crazy Wisdom Podcast, host Stewart Alsop sits down with Vahram Ayvazyan, founder of the Armenian Network State, for a wide-ranging conversation touching on AI and the future of work, the cyclical nature of human conflict throughout history, the decay of the nation-state, the concept of a "fourth establishment" of free people operating outside traditional power structures, the role of greed and self-aggrandizement in politics and tech, and how network states could serve as a parallel structure to challenge entrenched global elites. You can find Vahram on LinkedIn, or check the Armenian Network State page at networkstate.io.Timestamps00:00 The Future of AI and Humanity05:57 Human Nature and Greed12:00 The Crisis of Nation-States17:53 Community Resilience and Abundance23:30 The Power of Storytelling in Change29:43 Cultural Connections: Armenia and Africa35:43 Western Dominance and Its Consequences42:17 Creativity in the Age of AI48:07 Creating Parallel StructuresKey Insights1. Humans advance technologically but remain socially and biologically stagnant. Vahram argues that despite extraordinary technological leaps, human nature remains driven by greed and self-aggrandizement. Conflicts today mirror those of thousands of years ago, with only the actors changing while the underlying structure of power struggles stays the same.2. Power corrupts by disconnecting leaders from reality. Using a personal account of a deputy head of state, the guest illustrates how those who gain significant power gradually lose touch with reality, fall into cycles of wanting more, and become trapped in ego-driven decision-making regardless of their original intentions.3. The nation-state is in decay and failing its citizens. Globalization, internet, and migration have eroded the nation-state's ability to deliver basic services. Events like the Valencia flooding exposed how even wealthy European governments mismanage resources despite collecting enormous tax revenues.4. Three institutions currently rule the world, with a fourth emerging. Nation-states, multinational corporations, and religious institutions form today's power structure. The guest envisions a "fourth establishment" — network states — composed of free-thinking individuals connecting across geographies to build parallel, dignity-based communities outside these failing systems.5. Intentions matter more than the tools themselves. Whether discussing AI, nuclear energy, or mathematics, the guest emphasizes that technology is neutral and that what defines civilization is the moral intention behind its use, not the sophistication of the tools developed.6. Western civilization's dominance was built on superior weapons, not superior values. The guest challenges Western narratives by suggesting its historical advantage came primarily from military technology rather than cultural or moral superiority, contrasting this with indigenous and Eastern philosophies that treat land, community, and human relationships as sacred rather than as capital.7. Evolutionary, not revolutionary, change is the path forward. The guest warns that revolutionary movements are easily infiltrated, diverted, or crushed by existing power structures. Meaningful change requires patiently building critical mass through parallel structures, storytelling, and emotional connection until the alternative becomes undeniably powerful.

In this episode, Stewart Alsop III sits down with Tom Faye — experimenter, author of The 90 Day Client Acquisition Code, and founder of Carbon Credits Marketplace — to talk about solar energy, off-grid living, and the solarpunk vision of a technology-powered utopia. They cover everything from perovskite solar cells and portable container-based solar systems, to carbon credits, ESG investing, and blockchain verification of clean energy output. The conversation also winds through AI training data, business automation, and the data labeling industry before circling back to some bigger questions about human nature, geopolitics, and what genuine self-reliance looks like in 2025. You can find Tom and his work at Carbon Credits Marketplace on LinkedIn and his energy consumption data visualization is also shared there. His book The 90 Day Client Acquisition Code is available for those looking to explore business automation further.Timestamps00:00 Introduction to Tom Fay and his work01:03 Understanding Solar Punk: Utopian Tech and Culture02:15 Current State of Solar Technology and Storage03:45 Living Off-Grid: Solar, Batteries, and Remote Work06:11 Solar Energy in Africa: Challenges and Opportunities12:21 Powering Communities with Mobile Solar Solutions16:50 The Vision of Solar Punk: Self-Sufficient Communities22:54 Existing Examples: Great Barrier Island and Others26:06 Overfishing, Environmental Challenges, and Technological Solutions28:34 Using Technology to Address Second-Order Environmental Problems36:35 Data, AI, and the Future of Energy Management43:13 Carbon Credits, Blockchain, and ESG Reporting45:27 The Geopolitics of Green Energy and Resource Control46:53 How to Connect with Tom Fay and Future ProjectsKey InsightsSolarpunk represents a genuine near-future possibility, not just an aesthetic. As solar panels and lithium batteries become cheaper and more efficient, the vision of abundant, decentralized clean energy is becoming a practical reality rather than a utopian fantasy.Perovskite solar cells are pushing efficiency roughly 22% beyond conventional panels, and the bigger revolution happening right now is on the storage side — cheaper, higher-capacity batteries are what will truly unlock solar's potential at scale.Africa may leapfrog the West on solar adoption, just as it leapfrogged landlines with mobile phones. People in energy-scarce countries viscerally understand the value of clean power in a way that people in the West, accustomed to reliable grids, simply don't.Portable solar container units — self-contained, deployable systems — already exist and are making off-grid energy viable for farms, mines, remote lodges, and even data centers, with a roughly five-to-one solar-to-load footprint required.Carbon credits generated from verified solar output, tracked via IoT smart meters and stamped on blockchain, represent a long-term business opportunity that survives political shifts because institutional investors and banks operate on independent ESG mandates.AI training data is a present and real economic opportunity, but a shrinking one. The window for humans — especially lawyers, scientists, and specialists — to get paid for their expertise is closing fast as labs pivot toward synthetic data generation.True self-reliance comes down to four things: food, water, power, and transportation. With solar and Starlink, the gap between remote wilderness and connected civilization has essentially collapsed — something unimaginable even a generation ago.

In this episode of Crazy Wisdom, Stewart Alsop sits down with Andre Oliveira, founder of Splash N Color, a bootstrapped 3D printing e-commerce business selling consumer goods on Amazon. The two cover a lot of ground — from how Andre went from running 40 FDM printers out of South Florida to offshoring manufacturing to China, to how he's using Claude Code to automate inventory management and generate supplier RFQs across 200+ SKUs. The conversation stretches into bigger territory too: the San Francisco AI scene, the rise of AI agents and what they mean for the future of the internet, whether local on-device AI will eventually replace cloud-based tools, and why building physical products will stay hard long after software becomes easy. It's a candid, wide-ranging conversation between two self-taught builders figuring things out in real time. Follow Andre on X: @AndreBaach.Timestamps00:00 — Andre introduces Splash N Color, his Amazon-based 3D printing e-commerce business and explains the grind of running 40 FDM machines in South Florida.05:00 — The conversation shifts to Claude Code and how Andre built an inventory automation system to manage sales velocity and RFQs across 200+ SKUs.10:00 — Stewart and Andre compare notes on Opus 4.6, debate Codex vs Claude, and Andre breaks down the new Agent Teams feature in Claude Code.15:00 — Discussion turns to the San Francisco AI scene, the viral OpenClaw launch event that drew 700 people, and what's capturing the city's imagination right now.20:00 — The pair wrestle with data privacy, the illusion of it since 2000, and whether full transparency of personal data might actually serve people better.25:00 — Stewart pitches his vision of local on-device AI replacing cloud tools entirely, and they debate the 10–15 year timeline for mainstream societal adoption.30:00 — Andre traces his origin story: a high school dropout from Brazil who spotted a 3D printing opportunity on Facebook Marketplace and got lucky timing with COVID.35:00 — They explore whether AI-generated 3D models and DfAM will automate physical manufacturing, and why proprietary specs keep the space stubbornly hard.Key InsightsLifestyle businesses deserve more respect. Andre spent months feeling inadequate scrolling through Twitter watching founders announce funding rounds, before realizing his cash-flowing, location-independent business was already the goal. The social media version of entrepreneurial success warped his perception of what he actually had built.Claude Code is becoming an operating system. Stewart describes running Claude Code as having a second OS on top of MacOS — one that makes the underlying machine legible in ways it never was before. Both guests use it not just for coding but as a primary interface for understanding and operating their businesses.Agent Teams changes how work gets done. Andre explains that Claude's new multi-agent feature lets you assign a team lead and specialized roles that communicate with each other in parallel, essentially running an autonomous task force inside your terminal — a meaningful leap beyond single-instance prompting.Physical manufacturing will stay hard. Even as AI-generated 3D models improve, tolerances of 0.5 millimeters can mean the difference between a product working or not. Design for manufacturing is a separate discipline from design itself, and proprietary specs mean open source models rarely hit commercial quality.The internet is heading toward agents. Both guests agree that AI agents will increasingly handle tasks humans currently do manually online — booking services, making payments, coordinating logistics — with the human internet potentially becoming secondary to a machine-to-machine layer.Iteration is the real value of 3D printing. Andre pushes back on 3D printing as a business unto itself, framing it instead as a prototyping tool. The true value is rapid iteration on housing, tolerances, and fit — not the printer, but the speed of the feedback loop it enables.Technology compounds in layers. Andre closes with a tech-tree analogy: each generation normalizes the tools of the previous one and builds the next layer on top. Agentic coding today is what the internet was in the 90s — the foundation for something we can't yet fully see.

Stewart Alsop sits down with Ulises Martins on the Crazy Wisdom podcast to explore how artificial intelligence is fundamentally disrupting professional careers, labor markets, and the pace of human adaptation itself. They discuss everything from Dario Amodei's concept of "technological adolescence" to the possibility that we're approaching a point where AI advancement accelerates beyond our ability to keep up, touching on topics ranging from the economics of software development and the future of warfare to generational differences in how people will respond to AI-driven change. Martins emphasizes that while we may not be able to predict exactly what's coming, we need to dramatically increase our efforts to learn and adapt—potentially doubling the time we invest in understanding AI—because this isn't optional change, it's disruption happening at an unprecedented speed. Connect with Ulises on Linkedin to follow his work in AI and generative technology.Timestamps00:00 — Stewart introduces Ulysses Martins, framing the conversation around accelerationism and the future of work.05:00 — Ulises uses the parent-child analogy to argue humans will no longer play the dominant role as AI surpasses us.10:00 — Both agree learning AI is non-negotiable, urging listeners to double their investment in staying current.15:00 — Discussion shifts to software as media, the collapsing cost of building products, and the risk of big players like Anthropic making your idea obsolete overnight.20:00 — Ulises raises ecology vs. cosmic ambition, questioning whether humanity should aim for civilizational-scale goals like the Dyson sphere.25:00 — Stewart's ESP32 hardware project illustrates AI's current blind spots beyond software, while both predict physical-world AI will arrive as a byproduct of bigger industrial goals.30:00 — Tesla's birthplace in Croatia sparks a reflection on human genius as luck versus deliberate investment, invoking the Apollo program as a model.35:00 — The US-China AI race is compared to the Cold War Space Race, with interdependency acting as a brake on outright conflict.40:00 — Drone warfare and AI reframe military power, making troop size irrelevant and potentially reducing total war.45:00 — Agile methodology and generational shifts are linked, asking how Gen Z's values will shape the AI era globally.50:00 — Argentine vs. American Zoomers are contrasted, with millennial expectations versus Gen Z's pragmatism explored.55:00 — Ulises closes urging everyone to enjoy the ride, taking the infinite stream of change one episode at a time.Key Insights1. The Death of Traditional Career Paths: The concept of professional careers as we know them—starting as a junior and progressively advancing—is becoming obsolete due to AI's rapid advancement. This applies far beyond just software and SaaS companies, extending to all industries as robots and AI systems gain capabilities that fundamentally disrupt labor markets. The question isn't whether we'll adapt, but whether humans can adapt fast enough to keep pace with exponential technological change.2. The Acceleration Imperative: People must dramatically increase their investment in learning about AI immediately. Whatever time you were previously dedicating to staying current with technology needs to be doubled or tripled. This isn't optional—it's comparable to the necessity of basic education. Unlike previous technological transitions where you had years to learn new frameworks or tools, the current pace demands immediate, intensive engagement or you risk becoming irrelevant.3. Software as Media and the Collapse of Development Economics: Software has become media—easily reproducible and increasingly commoditized through AI assistance. The fundamental economics of software development are collapsing because if building software requires dramatically fewer development hours, the value and price of that software must necessarily decrease. Entrepreneurs need a new evaluation framework that assesses the risk of their ideas being replicated by AI or absorbed by major players like Anthropic or OpenAI.4. The Parent-Child Analogy for AI Development: Humanity's relationship with AI will inevitably mirror that of parents with increasingly capable children. Initially, we understand and control what AI does, but as it advances, it will surpass human capabilities in most domains. Just as parents cannot control fully grown adult children who exceed their abilities, humans will need to reconcile with creating something superior to ourselves. Attempting to permanently control such systems may be both impossible and potentially pathologic.5. The Kardashev Scale and Civilizational Ambitions: AI represents a civilizational-level technology that should redirect humanity toward grander goals like capturing stellar energy through Dyson spheres and expanding beyond our solar system. The competition between China and the United States over AI mirrors the Apollo program's space race but with higher stakes—potentially making traditional concepts like money less relevant if we successfully crack general intelligence. This requires thinking beyond planetary constraints.6. The Changing Nature of Warfare and Geopolitics: AI and autonomous weapons systems are fundamentally changing warfare by making human soldiers less relevant, similar to how nuclear weapons reduced the importance of conventional military force. This shift may actually reduce bloody civilian casualties in conflicts between major powers, as drone warfare and AI-driven systems create new equilibriums. The geopolitical map may fracture into more sovereign states and city-states as centralized control becomes less effective.7. Generational Adaptation and Unpredictability: Different generations will respond uniquely to AI disruption based on their values and experiences. Generation Z, having grown up during the pandemic without traditional expectations, may adapt differently than millennials who experienced unmet expectations. However, we must remain humble about our predictive abilities—we're not good at forecasting technological change or its timing. The best approach is maintaining openness, trying to understand developments as they unfold, and accepting that we cannot consume all information in an era of unlimited AI-generated content.

In this episode of the Crazy Wisdom Podcast, host Stewart Alsop sits down with Jake Hamilton, founder of Groundwire and Nockbox, to explore zero-knowledge proofs, Bitcoin identity systems, and the intersection of privacy-preserving cryptography with AI and blockchain technology. They discuss how ZK proofs could offer an alternative to invasive identity verification systems being rolled out by governments worldwide, the potential for continual learning AI models to shift the balance between centralized and open-source development, and why building secure, auditable computing infrastructure on platforms like Urbit matters more than ever as we face an explosion of AI agents and automated systems. Jake also explains Nockchain's approach to creating a global repository of cryptographically verified facts that can power trustless programmable systems, and how these technologies might converge to solve problems around supply chain security, personal data sovereignty, and resistance to censorship.Timestamps00:00 Introduction to Groundwire and Knockbox02:48 Understanding Zero-Knowledge Proofs06:04 Government Adoption of ZK Proofs08:55 The Future of Identity Verification11:52 AI and ZK Proofs: A New Era14:54 The Role of Urbit in Technology18:03 The Impact of COVID on Trust20:51 The Evolution of AI and Data Privacy23:47 The Future of AI Models26:54 The Need for Local AI Solutions29:51 Interoperability of Knockchain and BitcoinKey Insights1. Zero-Knowledge Proofs Enable Privacy-Preserving Verification: Jake explains that ZK proofs allow you to prove computational outcomes without revealing the underlying data. For example, you could prove you're over 18 without exposing your full identity or driver's license information. The proof demonstrates that a specific program ran through certain steps and reached a particular conclusion, and validating this proof is fast and compact. This technology has profound implications for age verification, identity systems, and protecting privacy while maintaining necessary compliance, potentially offering a middle path between surveillance states and complete anonymity.2. Government Adoption of Privacy Technology Remains Uncertain: There are three competing motivations driving government identity verification systems: genuine surveillance desires, bureaucratic efficiency seeking, and legitimate child protection concerns. Jake believes these groups can be separated, with some officials potentially supporting ZK-based solutions if positioned correctly. He notes the EU is exploring ZK identity verification, and UK officials have shown interest. The key is framing privacy-preserving technology as protection against "the swamp" rather than just abstract privacy benefits, which could resonate with certain political constituencies.3. The COVID Era Destroyed Institutional Trust at Unprecedented Scale: The conversation identifies COVID as potentially the largest institutional trust-burning event in human history, with numerous institutions simultaneously losing credibility with large portions of the population. This represents a dramatic shift from the boomer generation's default trust in authority figures and mainstream media. This collapse is compounded by the incoming AI revolution, creating a perfect storm where established bureaucracies cannot adapt quickly enough to manage rapidly evolving technology, leaving society in fundamentally unmanageable territory.4. Centralized AI Models Create Dangerous Dependencies: Both speakers acknowledge growing dependence on centralized AI services like Claude, with some users spending thousands monthly on tokens. This dependency creates vulnerability to price increases and service disruptions. Jake advocates for local AI deployment using models like DeepSeek R1, running on personal hardware to maintain control and privacy. The shift toward continuous learning models will fundamentally change the AI landscape, making personal data harvesting even more valuable and raising urgent questions about compensation and consent for training data contribution.5. High-Quality Training Data Is Becoming the Primary AI Bottleneck: Stewart argues that AI development is now limited more by high-quality training data than by compute power. The industry has exhausted easily accessible internet data and body-shop-style data labeling. Companies are now using specialized boutique services with techniques like head-mounted cameras for live-streaming world model training. This scarcity is subtly driving price increases across AI services and will fundamentally reshape the economics of AI development, with implications for who controls these increasingly powerful systems.6. Urbit Offers a Foundation for Trustworthy Computing: Jake positions Urbit as essential infrastructure for the AI age because its 30,000-line codebase (versus Unix's three million lines) can be understood by individual humans. Its deterministic, purely functional, and strictly typed design aims for eventual ossification—software that doesn't require constant security patches. This "tiny and diamond perfect" approach addresses the fundamental insecurity of systems requiring monthly vulnerability patches. In an era of AI agents and potential prompt injection attacks, having verifiable, comprehensible computing infrastructure becomes existentially important rather than merely desirable.7. Nockchain Creates a Global Repository of Provable Truth: Jake's vision for Nockchain combines ZK proofs with blockchain technology to create a globally available "truth repository" where verified facts can be programmatically accessed together. This enables smart contracts or programs gated on combinations of proven facts—such as temperature readings from secure devices, supply chain events, and payment confirmations. By using Nock's abstract, simple design optimized for ZK proof generation, the system can validate complex real-world conditions without exposing underlying data, creating infrastructure for coordinating action based on verifiable private information at global scale.

In this episode of the Crazy Wisdom podcast, host Stewart Alsop sits down with Markus Buehler, the McAfee Professor of Engineering at MIT, to explore how seemingly different systems—from proteins and music to knowledge structures and AI reasoning—share underlying patterns through hierarchy, self-organization, and scale-free networks. The conversation ranges from the limits of current AI interpolation versus true discovery (using the fire-to-fusion example), to the emergence of agent swarms and their non-linear effects, to practical questions about ontologies, knowledge graphs, and whether humans will remain necessary in the creative discovery process. Markus discusses his lab's work automating scientific discovery through AI agents that can generate hypotheses, run simulations, and even retrain themselves, while Stewart shares his own experiences building applications with AI coding agents and grapples with questions about intellectual property, material science constraints, and the future of human creativity in an AI-abundant world.Timestamps00:00 - Introduction to Marcus Buehler's work on knowledge graphs, structural grammar across proteins, music, and AI reasoning05:00 - Discussion of AI discovery versus interpolation, using fire and fusion as examples of fundamental versus incremental innovation10:00 - Language models as connective glue between agents, enabling communication despite imperfect outputs and canonical averaging15:00 - Embodiment and agency in AI systems, creating adversarial agents that challenge theories and expand world models20:00 - Emergent properties in materials and AI, comparing dislocations in metals to behaviors in agent swarms25:00 - Human role-playing and phase separation in society, parallels to composite materials and heterogeneity30:00 - Physical world challenges, atom-by-atom manufacturing at MIT.nano, limitations of lithography machines35:00 - Synthetic biology as alternative to nanotechnology, programming microorganisms for materials discovery40:00 - Intellectual property debates, commodification of AI models, control layers more valuable than model architecture45:00 - Automation of ontologies, agent self-testing, daughter's coding success at age 1150:00 - Graph theory for knowledge compression, neurosymbolic approaches combining symbolic and neural methods55:00 - Nonlinear acceleration in AI, emergence from accumulated innovations, restaurant owner embracing AI01:00:00 - Future generations possibly rejecting AI, democratization of knowledge, social media as real-time scientific discourseKey Insights1. Universal Patterns Across Disciplines: Seemingly different systems in nature—proteins, music, social networks, and knowledge itself—share fundamental structural patterns including hierarchy, self-organization, and scale-free networks. This commonality allows creative thinkers to draw insights across disciplines, applying principles from one domain to solve problems in another. As an engineer and materials scientist, Buehler has leveraged these isomorphisms to advance scientific understanding by mapping the "plumbing" of different systems onto each other, revealing hidden relationships that enable extrapolation beyond what's observable in any single domain.2. The Discovery Versus Interpolation Problem: Current AI systems, particularly large language models, excel at interpolation—recombining existing knowledge in new ways—but struggle with genuine discovery that requires fundamental rewiring of world models. Using the example of fire versus fusion, Buehler explains that an AI trained on combustion chemistry would propose bigger fires or new fuels, but couldn't conceive of fusion because that requires stepping back to more fundamental physics. True discovery demands the ability to recognize when existing theories have boundaries and to develop entirely new frameworks, something current AI architectures aren't designed to achieve due to their training objective of predicting the most likely outcome.3. The Role of Ontologies and Knowledge Graphs: While some AI researchers argue that ontologies are unnecessary because models form internal representations, Buehler advocates for explicit knowledge graphs as essential discovery tools. External ontologies provide sharp, analytical, symbolic representations that complement the fuzzy internal representations of neural networks. They enable verification of rare connections—like obscure papers that might hold key insights—which would be averaged away in standard AI training. This neurosymbolic approach combines the generalization capabilities of neural networks with the precision of formal knowledge structures, creating more powerful discovery systems.4. Emergent Properties and Agent Swarms: Just as materials science shows that collections of atoms exhibit properties impossible to predict from individual components, AI agent swarms demonstrate emergent behaviors beyond single models. When agents are incentivized not just to answer questions but to challenge each other adversarially, propose theories, and test hypotheses, they can spawn new copies of themselves and evolve understanding beyond their initial programming. This emergence isn't surprising from a materials science perspective—dislocations, grain boundaries, and other collective phenomena only appear at scale, fundamentally determining material behavior in ways unpredictable from studying just a few atoms.5. The Commoditization of Intelligence: The fundamental AI models themselves are becoming commodities, as evidenced by events like the Moldbug phenomenon where people built agents using various providers interchangeably. The real value is shifting from who has the smartest model to how models are orchestrated, integrated, and deployed. This parallels historical technology adoption patterns—just as we moved past debating who makes the best electricity to focusing on applications, AI is transitioning from a horse race over model capabilities to questions of infrastructure, energy, access speed, and agent coordination at the systems level.6. Human-AI Collaboration and Creative Control: Rather than wholesale replacement, AI enables humans to operate in an intensely creative space as orchestrators sampling from vast possibility spaces. Similar to how Buehler's 11-year-old daughter now builds sophisticated applications that would have required professional developers years ago, AI democratizes access to capabilities while humans retain the creative judgment about direction and meaning. The human role becomes curating emergence, finding rare connections, playing at the edges of knowledge, and exercising the kind of curiosity-driven exploration that AI systems lack without embodied stakes in their own survival and continuation.7. Technology as Evolutionary Inevitability: The development of AI represents not an unnatural threat but the next stage of human evolution—an extension of our innate drive to build models of ourselves and our world. From cave paintings to partial differential equations to artificial intelligence, humans continuously create increasingly sophisticated representations and tools. Attempting to stop this technological evolution is futile; instead, the focus should be on steering it toward human wellbeing while recognizing that the nonlinear, emergent effects of interconnected systems—whether material, biological, or computational—fundamentally resist centralized control and will continue to surprise us with capabilities we cannot fully predict or contain.

In this episode of the Crazy Wisdom podcast, host Stewart Alsop interviews John von Seggern, founder of Future Proof Music School, about the intersection of music education, technology, and artificial intelligence. They explore how musicians can develop timeless skills in an era of generative AI, the evolution of music production from classical notation to digital audio workstations like Ableton Live, and how AI is being used on the education side rather than for creation. The conversation covers music theory fundamentals, the development of instruments and recording technology throughout history, complex production techniques like sidechain compression, and the future of creative work in an AI-assisted world. John also discusses his development of Cadence, an AI voice tutor integrated with Ableton Live to help students learn music production. For those interested in learning more about Future Proof Music School or becoming a beta tester for the AI voice tutor, visit futureproofmusicschool.com.Timestamps00:00 Future Proofing Musicians in a Changing Landscape03:07 The Role of AI in Music Education05:36 Generative AI: A Tool for Musicians?08:36 The Evolution of Music Creation and Technology11:30 The Impact of Recording Technology on Music14:31 The Fragmentation of Culture and Music17:19 Exploring Music History and Theory20:13 The Relationship Between Music and Memory23:07 The Future of Music Creation and AI26:17 The Importance of Live Music Experiences28:49 Navigating the New Music Landscape31:47 The Role of AI in Finding New Music34:48 The Creative Process in Music Production37:33 The Future of Music Theory and Composition40:10 The Search for Unique Artistic Voices43:18 The Intersection of Music and Technology46:10 Cultural Shifts in the Music Industry49:09 Finding Quality in a Sea of ContentKey Insights1. Future-proofing musicians means teaching evergreen techniques while adapting to AI realities. John von Seggern founded Future Proof Music School to address both sides of music education in the AI era. Students learn timeless production skills that won't become obsolete as technology evolves, while simultaneously exploring meaningful creative goals in a world where generative AI exists. The school uses AI on the education side to help students learn, but students themselves aren't particularly interested in using generative AI for actual music creation, preferring to maintain their creative fingerprint on their work.2. The 12-note Western music system emerged from mathematical relationships discovered by Pythagoras and enabled collaborative music-making. Pythagoras demonstrated that pitch relates to vibrating string lengths, establishing mathematical ratios for musical intervals. This system allowed Western classical music to flourish because it could be notated and taught consistently, enabling large groups to play together. However, the piano is never perfectly in tune due to necessary compromises in the tuning system. By the 1920s, composers had explored most harmonic possibilities within this framework, leading to new directions in musical innovation.3. Recording technology fundamentally transformed music by making the studio itself the primary instrument. The invention of audio recording in the early-to-mid 20th century shifted music from purely instrumental composition to sound-based creation. This enabled entirely new genres like electronic dance music and hip-hop, which couldn't exist without technologies like synthesizers and samplers. Modern digital audio workstations like Ableton Live allow producers to have unlimited tracks and manipulate sounds in infinite ways, making any imaginable sound possible and moving innovation from hardware to software.4. Generative AI will likely replace generic music production but not visionary artists. John distinguishes between functional music (background music for films, work, or bars) and music where audiences deeply connect with the artist's vision. AI excels at generating functional music cheaply, which will benefit indie filmmakers and similar creators. However, artists with strong creative visions who audiences follow and identify with won't be replaced. The creative fingerprint and personal statement of important artists will remain valuable regardless of the tools they use, just as DJs created art through curation rather than original production.5. Copyright restrictions are limiting generative music AI's quality compared to other AI domains. Unlike books and visual art, recorded music copyrights are concentrated among a few companies that defend them aggressively. This prevents AI music models from training on the best music in each genre, resulting in lower-quality outputs. Some developers claim their private models trained on copyrighted music sound better than commercial offerings, but legal constraints prevent widespread access. This situation differs significantly from other creative domains where training data is more accessible.6. Modern music production involves complex technical skills like sidechain compression and multi-track mixing. Today's electronic music producers work with potentially hundreds of tracks, each with sophisticated processing. Techniques like sidechain compression allow certain elements (like kick drums) to dynamically reduce the volume of other elements (like bass), ensuring clarity in the final mix. Future Proof Music School teaches students these complex production techniques, with some aspiring producers creating incredibly detailed compositions with intricate effects chains and interdependent track relationships.7. Culture is fragmenting into micro-trends, making discovery rather than creation the primary challenge. John observes that while the era of mass media created mega-stars like The Beatles and Elvis, today's landscape features both enormous stars (like Taylor Swift) and an extremely long tail of creators making niche content. AI will make it easier for more people to create quality content, particularly in fields like independent filmmaking, but the real problem is discovery. Current algorithmic recommendations don't effectively surface hidden gems, suggesting a future where personal AI agents might better curate content based on individual preferences rather than platform-driven engagement metrics.

In this episode of the Crazy Wisdom Podcast, host Stewart Alsop sits down with Lars van der Zande, founder and CEO/technical architect of Inkwell Finance, for what Lars describes as his first-ever podcast appearance. The conversation covers a wide range of blockchain infrastructure topics, including Lars's work with Sui and Solana blockchains, the innovative capabilities of Ika's programmatic wallets and blockchain of signatures, and how Inkwell Finance is building revenue-based financing solutions for on-chain entities—from AI agents to protocols. They explore the evolving landscape of crypto regulation, the merging of traditional finance with blockchain technology, the future of decentralized legal systems, and how the user experience barrier is being lowered through technologies that eliminate constant transaction signing. Lars also discusses Inkwell's embedded financing approach and their pre-seed fundraising round.Links mentioned:- Inkwell's website: inkwell.finance- Inkwell on Twitter: @__inkwell- Lars on Twitter: @LMVDZandeTimestamps00:00 Introduction to Inkwell Finance and Technical Architecture02:06 Understanding Sui and Solana: Blockchain Dynamics05:55 The Role of Ika in Inkwell Finance11:51 Leviathan: Revenue Generation and Financing in Crypto17:38 The Future of AI Agents and Programmatic Wallets23:23 Smart Contracts: Legal Implications and Future Directions25:06 The Future of Inqvil Finance25:42 Decentralization and Its Evolution27:32 The Merging of Traditional and Crypto Systems29:33 Global Financial Dynamics and Market Reactions31:48 The Collapse of Traditional Financial Systems32:46 Jurisdictional Shifts in the Crypto World33:59 Legal Systems and Blockchain Integration35:57 On-Chain Credit and Financial Opportunities39:29 The Role of AI in Finance41:30 Learning from Peer-to-Peer Lending History43:14 Disruption in Insurance and Risk Management44:54 On-Chain vs Off-Chain Data46:54 The Evolution of the Internet and Blockchain49:12 Future Subscription Models in BlockchainKey Insights1. Ika's Revolutionary Blockchain Signature Technology: Lars discovered Ika, a blockchain of signatures built on Sui that enables any blockchain transaction to be signed without revealing the underlying message. Using patented 2PC MPC technology, Ika splits key shares across validators and encrypts them in transit, performing complex cryptographic operations that allow smart contracts on Sui to generate signatures for transactions on any other blockchain. This eliminates the need to build separate smart contracts on each blockchain, fundamentally changing how cross-chain interactions work and opening possibilities for truly interoperable decentralized applications.2. Programmatic Wallets vs Traditional Wallets: Traditional wallets like MetaMask require manual user approval for every transaction through a front-end interface, but Ika's D-wallet introduces programmatic wallets with policy-based controls embedded in smart contracts. These wallets can execute transactions based on predetermined conditions checked against on-chain data like Oracle prices, without requiring individual user signatures. For example, a Bitcoin D-wallet can hold native Bitcoin without wrapping or bridging to a custodian, and smart contract policies determine when and how that Bitcoin can be transferred, creating unprecedented security and automation possibilities for decentralized finance.3. Inkwell's Revenue-Based Financing Model: Inkwell Finance is building Leviathan, a revenue-based financing platform for on-chain entities including protocols, AI agents, and individual traders with verifiable track records. Borrowers receive capital based on their on-chain performance metrics like sharp ratio and drawdown, with loan repayment automatically deducted from their revenue stream. The profit split structure allocates approximately 60% to borrowers, 30% to lenders, and 10% split between Inkwell and integrating platforms. This creates a sustainable lending model where flight risk is minimized through D-wallet policy controls that restrict how borrowed capital can be used.4. Wallet-as-a-Protocol and the Future of User Experience: The crypto industry is moving toward embedded wallet solutions that eliminate the friction of traditional wallet management, with Wallet-as-a-Protocol representing the next evolution beyond services like Privy and Dynamic. Unlike current embedded wallets that lock users into specific applications, Wallet-as-a-Protocol enables single sign-on across multiple applications while users maintain control of their keys. Combined with app-sponsored gas fees, this approach allows non-crypto-native users to interact with blockchain applications without knowing they're using crypto, removing the biggest barrier to mainstream adoption and creating web2-like user experiences on web3 infrastructure.5. AI Agents as Financial Entities: AI agents are emerging as revenue-generating entities with on-chain transaction histories that create verifiable track records for creditworthiness assessment. Inkwell Finance is specifically targeting this market, recognizing that AI agents will need wallets and capital to operate effectively. The programmatic nature of D-wallets pairs perfectly with AI agents, as policy controls can restrict agent behavior to specific smart contract interactions, preventing unauthorized fund transfers while allowing automated trading or revenue generation. This creates a new category of borrower that operates 24/7 with completely transparent performance metrics, fundamentally different from traditional loan recipients.6. Cross-Chain Liquidity Without Asset Transfer: Ika's technology enables users to take loans against revenue generated on one blockchain and deploy that capital on entirely different blockchains without moving their original liquidity positions. For instance, someone earning yield on Sui's Fusol protocol could borrow against that revenue stream and deploy capital on Solana opportunities, effectively creating multiple on-chain businesses that generate their own credit scores and revenue to service debt. This ability to read state across different blockchains from within smart contracts opens possibilities for multi-chain strategies that don't require withdrawing capital from productive positions, maximizing capital efficiency across the entire crypto ecosystem.7. The Convergence of Traditional Finance and Crypto Infrastructure: The regulatory landscape is rapidly evolving with initiatives like the Genius Act and Clarity Act creating frameworks where traditional financial systems merge with crypto infrastructure through mechanisms like stablecoins backed by US treasuries. Companies are increasingly establishing entities in the United States to access capital networks and Delaware's established legal framework while issuing tokens through jurisdictions like Switzerland. This hybrid approach, combined with emerging concepts like Gabriel Shapiro's "cybernetic agreements" that make smart contract parameters legally enforceable in traditional courts, suggests the future isn't pure decentralization but rather a sophisticated integration of on-chain and off-chain legal and financial systems.

In this episode of the Crazy Wisdom Podcast, host Stewart Alsop sits down with Larry Swanson, a knowledge architect, community builder, and host of the Knowledge Graph Insights podcast. They explore the relationship between knowledge graphs and ontologies, why these technologies matter in the age of AI, and how symbolic AI complements the current wave of large language models. The conversation traces the history of neuro-symbolic AI from its origins at Dartmouth in 1956 through the semantic web vision of Tim Berners-Lee, examining why knowledge architecture remains underappreciated despite being deployed at major enterprises like Netflix, Amazon, and LinkedIn. Swanson explains how RDF (Resource Description Framework) enables both machines and humans to work with structured knowledge in ways that relational databases can't, while Alsop shares his journey from knowledge management director to understanding the practical necessity of ontologies for business operations. They discuss the philosophical roots of the field, the separation between knowledge management practitioners and knowledge engineers, and why startups often overlook these approaches until scale demands them. You can find Larry's podcast at KGI.fm or search for Knowledge Graph Insights on Spotify and YouTube.Timestamps00:00 Introduction to Knowledge Graphs and Ontologies01:09 The Importance of Ontologies in AI04:14 Philosophy's Role in Knowledge Management10:20 Debating the Relevance of RDF15:41 The Distinction Between Knowledge Management and Knowledge Engineering21:07 The Human Element in AI and Knowledge Architecture25:07 Startups vs. Enterprises: The Knowledge Gap29:57 Deterministic vs. Probabilistic AI32:18 The Marketing of AI: A Historical Perspective33:57 The Role of Knowledge Architecture in AI39:00 Understanding RDF and Its Importance44:47 The Intersection of AI and Human Intelligence50:50 Future Visions: AI, Ontologies, and Human BehaviorKey Insights1. Knowledge Graphs Combine Structure and Instances Through Ontological Design. A knowledge graph is built using an ontology that describes a specific domain you want to understand or work with. It includes both an ontological description of the terrain—defining what things exist and how they relate to one another—and instances of those things mapped to real-world data. This combination of abstract structure and concrete examples is what makes knowledge graphs powerful for discovery, question-answering, and enabling agentic AI systems. Not everyone agrees on the precise definition, but this understanding represents the practical approach most knowledge architects use when building these systems.2. Ontology Engineering Has Deep Philosophical Roots That Inform Modern Practice. The field draws heavily from classical philosophy, particularly ontology (the nature of what you know), epistemology (how you know what you know), and logic. These thousands-year-old philosophical frameworks provide the rigorous foundation for modern knowledge representation. Living in Heidelberg surrounded by philosophers, Swanson has discovered how much of knowledge graph work connects upstream to these philosophical roots. This philosophical grounding becomes especially important during times when institutional structures are collapsing, as we need to create new epistemological frameworks for civilization—knowledge management and ontology become critical tools for restructuring how we understand and organize information.3. The Semantic Web Vision Aimed to Transform the Internet Into a Distributed Database. Twenty-five years ago, Tim Berners-Lee, Jim Hendler, and Ora Lassila published a landmark article in Scientific American proposing the semantic web. While Berners-Lee had already connected documents across the web through HTML and HTTP, the semantic web aimed to connect all the data—essentially turning the internet into a giant database. This vision led to the development of RDF (Resource Description Framework), which emerged from DARPA research and provides the technical foundation for building knowledge graphs and ontologies. The origin story involved solving simple but important problems, like disambiguating whether "Cook" referred to a verb, noun, or a person's name at an academic conference.4. Symbolic AI and Neural Networks Represent Complementary Approaches Like Fast and Slow Thinking. Drawing on Kahneman's "thinking fast and slow" framework, LLMs represent the "fast brain"—learning monsters that can process enormous amounts of information and recognize patterns through natural language interfaces. Symbolic AI and knowledge graphs represent the "slow brain"—capturing actual knowledge and facts that can counter hallucinations and provide deterministic, explainable reasoning. This complementarity is driving the re-emergence of neuro-symbolic AI, which combines both approaches. The fundamental distinction is that symbolic AI systems are deterministic and can be fully explained, while LLMs are probabilistic and stochastic, making them unsuitable for applications requiring absolute reliability, such as industrial robotics or pharmaceutical research.5. Knowledge Architecture Remains Underappreciated Despite Powering Major Enterprises. While machine learning engineers currently receive most of the attention and budget, knowledge graphs actually power systems at Netflix (the economic graph), Amazon (the product graph), LinkedIn, Meta, and most major enterprises. The technology has been described as "the most astoundingly successful failure in the history of technology"—the semantic web vision seemed to fail, yet more than half of web pages now contain RDF-formatted semantic markup through schema.org, and every major enterprise uses knowledge graph technology in the background. Knowledge architects remain underappreciated partly because the work is cognitively difficult, requires talking to people (which engineers often avoid), and most advanced practitioners have PhDs in computer science, logic, or philosophy.6. RDF's Simple Subject-Predicate-Object Structure Enables Meaning and Data Linking. Unlike relational databases that store data in tables with rows and columns, RDF uses the simplest linguistic structure: subject-predicate-object (like "Larry knows Stuart"). Each element has a unique URI identifier, which permits precise meaning and enables linked data across systems. This graph structure makes it much easier to connect data after the fact compared to navigating tabular structures in relational databases. On top of RDF sits an entire stack of technologies including schema languages, query languages, ontological languages, and constraints languages—everything needed to turn data into actionable knowledge. The goal is inferring or articulating knowledge from RDF-structured data.7. The Future Requires Decoupled Modular Architectures Combining Multiple AI Approaches. The vision for the future involves separation of concerns through microservices-like architectures where different systems handle what they do best. LLMs excel at discovering possibilities and generating lists, while knowledge graphs excel at articulating human-vetted, deterministic versions of that information that systems can reliably use. Every one of Swanson's 300 podcast interviews over ten years ultimately concludes that regardless of technology, success comes down to human beings, their behavior, and the cultural changes needed to implement systems. The assumption that we can simply eliminate people from processes misses that humans are who these systems should serve, and human problems represent the most interesting challenges that can never be fully automated away.

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