Crazy Wisdom

Crazy Wisdom
  • 4.9 (69)
  • Philosophy
  • Updated Weekly

Exploring the intersection of artificial intelligence, consciousness, philosophy, and technology with thinkers, builders, and seekers. Hosted by Stewart Alsop III — conversations spanning AI agents, Advaita Vedanta, geopolitics, cryptography, network states, and the future of sovereign technology. 660+ episodes and counting.

  1. 1d ago

    Episode #556: From Meow Wolf to Synthetic Landscapes: Designing Conservation Through Deep Time

    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.

    56 min

  2. 4d ago

    Episode #555: Bonds Without Borders: Tokenization, Sovereignty, and the Truth of Markets

    In this episode of Crazy Wisdom, Stewart Alsop sits down with Akin Kadioglu, cofounder of Bondi Finance, to unpack the wild world of tokenized corporate bonds and what it actually takes to bring traditional finance onto the blockchain. They trace the regulatory maze from Bermuda's segregated accounts structure to the global competition between nation states racing to build the best tokenization frameworks, then widen the lens to cover the Genius Act and stablecoin politics, why America's biggest companies have stopped going public, the techno feudalism reshaping Silicon Valley, China's strategy of copying and scaling rather than innovating, and a deep dive into emerging market bonds, default risk, and why countries like Turkey, Mexico, and Indonesia might be more investable than people assume. Find Akin on Twitter at @kadiogluakin, and check out his work at Bondi Finance, bondifinance.io. Timestamps 00:00 Tokenization of corporate bonds and Bermuda's regulatory structure05:00 Global tokenization frameworks and the Genius Act's impact on stablecoins10:00 Anthropic's secondary markets, private capital, and why big companies avoid IPOs15:00 Techno feudalism, Silicon Valley's clergy class, and China's distillation strategy20:00 RISC-V, open source robotics, and the AI monopoly risk25:00 American gridlock, constitutional spirit, and crypto as freedom from centralization30:00 Argentina's 2001 default, dollar pegging, and Milei's deficit cuts35:00 Carry trades, US treasury rates, and inflation in emerging economies40:00 Sovereign versus corporate bonds and tokenization's $38 trillion opportunity45:00 Investment grade versus junk bonds and zero default risk explained50:00 Bond credit ratings, Yankee and Samurai bonds, and top emerging market picks Key Insights Tokenization's biggest obstacle isn't technology, it's sovereignty. Akin argues that nation states resist giving tokenized assets the same ownership rights as traditional securities because they're hesitant to cede authority to neutral blockchains, even when the underlying infrastructure already works.The Genius Act protected banks more than it empowered crypto. By separating yield bearing stablecoins from non yield bearing ones, regulators effectively let banks keep customers from earning interest outside traditional savings accounts, a quiet but consequential win for legacy finance.America's biggest companies are opting out of public markets. Stripe, Anthropic, OpenAI, and SpaceX have stayed private far longer than past generations of breakout companies, raising real questions about whether venture capital has replaced the public markets that once defined American finance.Silicon Valley's elite increasingly resemble a modern clergy. Akin frames the founders and labs that gatekeep advanced AI knowledge as inheritors of a medieval power structure, where access to "secret knowledge" converts directly into capital and influence over everyone else.China wins by scaling, not innovating. Rather than leading at the frontier, China consistently lets American labs take the first step, then copies and mass produces at a fraction of the cost, a strategy Akin sees playing out in everything from manufacturing to AI models.Not all bonds carry the same kind of risk. Akin draws a sharp distinction between bonds with zero tail risk, like US treasuries denominated in their own currency, and corporate or foreign currency sovereign bonds, where default is always possible no matter how strong the issuer looks.Emerging market ratings can be misleading. A BB rated company in an emerging market may have a lower default rate than a BBB rated US company, since emerging market firms typically need far more financial maturity just to access public bond markets in the first place.

    1h 2m

  3. Jun 15

    Episode #554: When Fluency Lies: The Knowledge Problem at the Heart of AI

    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.

    59 min

  4. Jun 12

    Episode #553: The Connection Economy: What Recruiting Teaches Us About Human Value

    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.

    35 min

  5. Jun 8

    Episode #552: The Unbanked Advantage: How Nigeria's Financial Chaos Made It Crypto-Ready

    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.

    53 min

  6. Jun 5

    Episode #551: From Trash to Tools: The Open Hardware Revolution Powering Solarpunk Science

    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.

    59 min

  7. Jun 1

    Episode #550: From Armies to Algorithms: Why the Biggest Player No Longer Wins

    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.

    55 min

  8. May 29

    Ep549_From MS-DOS to Vibe Coding: How Non-Technical Founders Build Complex Software

    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.

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About

Exploring the intersection of artificial intelligence, consciousness, philosophy, and technology with thinkers, builders, and seekers. Hosted by Stewart Alsop III — conversations spanning AI agents, Advaita Vedanta, geopolitics, cryptography, network states, and the future of sovereign technology. 660+ episodes and counting.

Information

  • Channel
    Crazy Wisdom
  • Creator
    Stewart Alsop III | AI, Consciousness & Technology
  • Years Active
    2018 - 2026
  • Episodes
    24
  • Rating
    Explicit
  • Copyright
    © 2026 Stewart Alsop
  • Show Website
    Crazy Wisdom

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