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Crazy Wisdom: Daily Summaries Delivered
by Stewart Alsop III | AI, Consciousness & Technology
In his series "Crazy Wisdom," Stewart Alsop explores cutting-edge topics, particularly in the realm of technology, such as Urbit and artificial intelligence. Alsop embarks on a quest for meaning, engaging with others to expand his own understanding of reality and that of his audience. The topics covered in "Crazy Wisdom" are diverse, ranging from emerging technologies to spirituality, philosophy, and general life experiences. Alsop's unique approach aims to make connections between seemingly unrelated subjects, tying together ideas in unconventional ways.
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- 4 days ago1h 4m
Episode #579: Sun Tzu in the Server Room: Cyber Warfare Without Firing a Bullet
Stewart Alsop sits down with his friend John Mercer for a wide-ranging conversation that explores the surprising connections between ancient warfare and modern cybersecurity. Drawing on analogies from Roman and Greek military history, they examine how concepts like defense-in-depth—originally developed to protect the expanding Roman Empire's vulnerable borders—now apply to protecting businesses and critical infrastructure in the digital age. The discussion moves from historical military strategy to the urgent challenges facing organizations today, including AI-powered vulnerability discovery, automated penetration testing, the explosion of zero-day exploits, and the particular vulnerability of underfunded institutions like schools and hospitals. Throughout their conversation, Mercer shares his perspective as someone who is simultaneously a "short-term pessimist and long-term optimist" about AI security, while Alsop brings his characteristic curiosity about how these technologies will reshape everything from network architecture to the fundamental nature of offense and defense in cyberspace. Key InsightsHere are 7 key insights from this episode:1. Defense in depth, a core cybersecurity principle, has historical roots in late Roman Empire military strategy. When Rome shifted from expansion to defending its borders in the third century, they developed multilayered defensive positions where if one fort or territory fell, additional fortifications could quickly respond and help reclaim lost ground. This same principle applies to modern cybersecurity, where multiple layers of security controls ensure that if one layer is compromised, additional protections remain intact to prevent catastrophic breaches.2. AI agents require careful containment similar to dangerous animals in cages, and the responsibility for their actions falls on those who deploy them rather than the AI itself. Using micro virtual machines for sandboxing represents a smart approach because these miniaturized environments contain only the specific resources, data, and access permissions that AI agents need to complete their tasks. This prevents AI from accessing entire file systems or sensitive credentials, limiting potential damage if the agent malfunctions or goes off the rails.3. The current AI landscape is discovering software vulnerabilities at unprecedented rates, creating both enormous risk and tremendous opportunity. While this creates short term pessimism because attackers can find and exploit zero day vulnerabilities faster than ever before, it generates long term optimism because AI could eventually help create bug free software. Within perhaps ten years, critical systems like the Linux kernel could theoretically become completely free of security vulnerabilities through AI assisted code review and remediation.4. Modern cyber warfare could paralyze entire civilizations without firing a single bullet by targeting critical infrastructure systems. A sophisticated attack could simultaneously disable power grids, water systems, banking networks, ATMs, and the interlocking logistics systems that deliver food from farms to grocery stores. These SCADA systems and industrial control systems are often among the most vulnerable targets because they run on older technology, yet their compromise could create catastrophic societal consequences that rival or exceed traditional military warfare.5. The zero day vulnerability market operates as a literal black market where nation states participate as buyers, stockpiling these exploits like weapons for potential use in cyber warfare or targeted operations. Professional vulnerability researchers traditionally followed responsible disclosure processes, reporting bugs to companies for patches before public disclosure, often receiving substantial financial rewards. However, bad actors sell zero days on black markets almost always for cryptocurrency, and nation states have demonstrated willingness to chain multiple zero days together for sophisticated attacks against specific targets.6. Frontier AI companies face a difficult tension between AI safety and enabling legitimate security research. When companies like Anthropic restrict advanced models from helping with cybersecurity or biological research to prevent weaponization, they inadvertently push good actors toward Chinese models that lack such restrictions and cost significantly less. The cybersecurity community needs access to powerful unfiltered AI to patch open source projects and secure critical infrastructure, as the entire economy depends on software being safe and effective for everything from food delivery logistics to city utilities.7. The future of automated cybersecurity will involve AI red teams constantly attacking test environments while AI blue teams automatically remediate discovered vulnerabilities and deploy patches in a continuous
- 1 weeks ago52 min
Episode #578: Slow Art in a Scroll Economy: Gage on Rebuilding San Francisco’s Art Soul
In this episode of the Crazy Wisdom Podcast, host Stewart Alsop sits down with Gage to explore the intersection of classical art and San Francisco's tech scene. Gage, who describes his work as "neo-Renaissance" art—imagining what Michelangelo might create if he were from the nineties—discusses his mission to revive interdisciplinary arts in San Francisco through Mass Art and an incubation program aimed at putting the city back on the cultural map. The conversation ranges from the loss of craft training in modern art schools and the failure of traditional institutions to the challenges of creating meaningful work in an algorithm-driven culture that prioritizes immediate gratification over pieces that take years to complete. Gage shares his experiences running the Arts and Music Floor at Frontier Tower, organizing events like the Peptide Rave and UFB robot boxing, and his broader vision for breaking down the silos between San Francisco's tech community and its artists by creating immersive experiences that help each group understand and value what the other has to offer. Key Insights1. San Francisco is experiencing a significant decline in arts institutions, with no long-running art schools remaining as of September, including the closure of CCA which will reopen with reduced capacity under Vanderbilt investment. This institutional failure represents a fundamental break from the Renaissance workshop model where artists learned under masters in multi-year apprenticeships, creating a situation where most talented painters today are entirely self-taught through lengthy trial and error rather than structured mentorship.2. The Renaissance emerged from a rediscovery of Greek and Roman culture following the Dark Ages, when artists relearned three-point perspective and mathematical principles that had been completely lost. This pattern reveals that art quality directly correlates with cultural golden ages, and conversely, periods of cultural decline produce noticeably inferior artwork. The Renaissance succeeded because craftsman culture was rebuilt through workshops where young artists spent their entire lives developing skills, supported by substantial church funding and a society that valued beauty as essential to the good life.3. Modern art education has shifted from craft to ideation, creating work that is primarily referential to previous art movements rather than emerging from personal creative exploration. Since modernism and Picasso's break with pre-modern art, each subsequent movement has largely been about reacting against the previous generation rather than developing unique perspectives. This results in art that lacks the depth that comes from spending years with a single piece, developing personal symbolic language, and filtering diverse influences through an individual creative lens that requires substantial time investment to develop.4. The fundamental disconnect between San Francisco's tech community and arts community stems from tech workers' lack of exposure to humanities during their training, not from any inherent antagonism. When tech professionals are given simple art experiences like live painting with jazz bands and projection mapping, they respond with profound enthusiasm because they have never had context for understanding art's value. Breaking down this silo requires welcoming tech people into art spaces and providing experiential context that demonstrates art's meaning and impact.5. Large-scale art events serve as highly effective and inexpensive vehicles for making technology meaningful to regular people, as demonstrated by events like the Peptide Rave and UFB robot boxing which achieved New York Times coverage with budgets under ten thousand dollars. These events function as trojan horses that allow tech companies to gain visibility where people actually engage with their work in meaningful ways. When artists lead experience design without excessive steering from tech companies, they create novel presentations that draw attention and make potentially scary technologies feel accessible and interesting to broad audiences.6. Modern algorithmic culture has fragmented attention away from the mass simultaneous experiences of radio and television eras, making inspiration more decentralized and preventing cultural movements from reaching hundreds of millions of people at once. The most popular content creators today, primarily streamers and YouTubers, largely produce commentary rather than truly creative work, representing cult of personality where people outsource their thinking rather than cultivating their own creative experiences. This shift away from music and traditional arts as the primary cool factor for young people represents a cyclical change in how cultural trends move through society.7. Genuine creativity emerges from living a full life while intentiona
- 2 weeks ago59 min
Episode #577: The Machine Gun vs. The Sniper Rifle: How LLMs Changed Data Analytics Forever
Stewart Alsop sits down with Teo Ionita, founder of Ideaticon, a data consulting company that builds data platforms for businesses. Their conversation explores how data engineering has evolved from the traditional analyst-engineer-scientist triad into something fundamentally different with the advent of LLMs, which Teo argues are better at working with SQL primitives than humans—essentially turning data work into a management problem where humans need to understand systems theoretically rather than mechanically. They discuss everything from clickstream data and behavioral analytics to the nature of consciousness in AI systems, touching on why the rationalist community's existential risk narratives might be more about regulatory capture than actual danger, and exploring Teo's theory that LLMs are the ultimate expression of "technique"—existing only in the interaction between human and machine, like golems summoned by magic words. Key Insights1. Data engineering has evolved from a single role into a triad of specialized positions: data analysts who focus on querying clean data and generating insights, analytics engineers who transform and prepare data using tools like DBT, and data engineers who handle data ingestion and pipeline infrastructure. This evolution occurred as companies realized they needed different specialists to handle the growing complexity of data systems, with each role focusing on a distinct part of the data lifecycle from collection through to consumption.2. LLMs have fundamentally transformed data analytics by replacing traditional dashboard consumption with natural language queries. Instead of carefully crafted SQL queries that require precision like a sniper rifle, LLMs operate like machine guns firing multiple queries simultaneously until they hit the target. Anthropic recently announced Claude will create dashboards directly, marking a significant shift in how people interact with data. However, SQL remains the most efficient language for data retrieval because it was originally designed to be close to natural language for business users.3. Companies of any size can benefit from data engineering, even at the individual level. The speaker manages data analytics for his own one-person app, using Claude to analyze user activity, subscription data, and engagement patterns. This represents a dramatic shift from knowledge management, which typically only becomes necessary around 500 employees. With LLMs, anyone can perform sophisticated data analysis regardless of company size, democratizing access to insights that previously required dedicated teams.4. The relationship between humans and LLMs requires a new type of theoretical understanding rather than mechanical coding knowledge. Success with AI agents depends on understanding systems at a high level, knowing what each component should do without necessarily understanding the detailed implementation. This mirrors how managers operate, maintaining a theoretical framework while delegating execution to specialists. The speaker and one colleague completed a massive data migration in six months that would have taken a team of ten people over a year by maintaining this theoretical understanding while letting agents handle implementation details.5. We are losing the ability to develop fuzzy knowledge and intuition about systems because people no longer write code by hand or go through the empirical learning process. This creates a critical challenge: how do we teach people to develop the theoretical understanding and pattern recognition needed to effectively manage AI agents when they never experience the hands-on work that traditionally built that intuition? This skill of knowing what is relevant and what is not, of understanding the proper shape of a system, remains essential but increasingly difficult to transmit to new practitioners.6. Clickstream or behavioral data has become crucial for training LLMs to replicate human work patterns. Meta and other companies are now tracking every interaction their employees have with systems, similar to how advertising companies historically tracked user behavior to optimize ad performance. This data helps create models that understand how people actually work, shop, and interact with systems. The speaker specializes in this type of data collection and analysis, which represents the convergence of traditional analytics with the new demands of AI training.7. LLMs represent the ultimate expression of technique as defined by philosopher Jacques Ellul, existing only in the moment of human-machine interaction. The models are essentially dormant weights stored on servers until activated by human prompts, at which point they become animated by the data and goals provided by users. This relationship suggests we may not escape technique through AI but instead deepen our entanglement with it, as every interacti
- 3 weeks ago1h 3m
Episode #576: From a $12K Monthly Bill to Zero: Owning Your AI Stack and Your Data
In this episode of the Crazy Wisdom Podcast, host Stewart Alsop sits down with Devin Elliot, founder of Monetic xyz, to explore the evolving landscape of AI development, decentralized infrastructure, and the future of software engineering. The conversation ranges from Devin's decision to move away from Anthropic's services—cutting his monthly spend from $12,000 to zero—to his early adoption of "vibe coding" in 2022 and his extensive experience building with cursor and custom AI infrastructure. They discuss the technical architecture behind privacy-preserving systems, the value of embedders and fine-tuning, the economics of frontier AI companies, and why Devin believes the internet is becoming increasingly inhospitable to humans while agents take over. Along the way, Devin shares insights from his decades in engineering, his background in crypto since 2011, and his vision for a self-custodial future where users control their own data through content-addressed systems and encrypted safe tensor files rather than centralized databases. Key Insights1. Devin Elliott transitioned from traditional engineering to vibe coding in 2022 after fifteen years of experience, recognizing it as a fundamental cycle shift rather than resisting it. He reduced his Anthropic spending from $12,000 monthly to zero, partly due to disagreement with their public communications and partly because he could migrate his entire codebase to other platforms. His approach was strategic: he deliberately chose Cursor as a bridge tool despite knowing it would cost more in tokens, prioritizing speed and continuity over constant tool-switching. By staying focused on one platform, he avoided the productivity drain of repeatedly learning new interfaces while others chased the latest options.2. Elliott's token usage was extraordinarily high, placing him in the top 0.2% of Cursor users with 25 billion tokens processed. His heavy consumption stemmed from spending tokens on priming context rather than reusing prompts or relying heavily on rules. His background as an architect rather than a pure engineer meant he focused on understanding architectural patterns and system design rather than memorizing specific function implementations. By investing heavily in context priming, he achieved near one-shot execution where the AI could implement complex features like WebSocket servers correctly and efficiently without multiple iterations.3. The lack of engineering standards in software development has always been problematic, but AI coding amplifies this issue significantly. Most software engineers cannot explain fundamental architectural concepts like model-view-viewmodel or understand why different frameworks embody different state-handling philosophies. Engineers often debate which framework to choose without understanding the underlying engineering principles that make each suitable for specific purposes. If buildings were constructed like software is written, they would be structurally unsound and dangerous. This knowledge gap becomes more critical with AI coding because the quality of output depends heavily on understanding architectural patterns to guide the AI effectively.4. The future of the internet will likely become inhospitable to human users as bots gain increasingly efficient access at lower levels than human interfaces allow. Elliott observed this in DeFi through MEV (miner extractable value) where bots could intercept and extract value from transactions before humans could complete them. He predicts the internet will evolve into a space primarily for agent-to-agent interaction, with humans interacting through local AI systems that venture into this hostile environment on their behalf. These personal agents would operate through proxy environments that can be attacked or compromised without exposing the user's actual systems or data.5. Centralized AI services like Anthropic face fundamental architectural problems that decentralized approaches can solve more effectively. The traditional Silicon Valley model of centralizing user data in company databases to deliver personalized services cannot achieve true privacy preservation or meaningful personalization. This centralization model failed during the Internet of Things wave when consumers rejected giving their data to companies like Samsung just to receive basic functionality from their own devices. Elliott built his entire system around self-custodial, privacy-preserving architecture using local GPU racks and custom models, demonstrating that individuals and businesses can achieve AI capabilities without surrendering data to centralized providers.6. Elliott developed a novel approach using embedders and content-addressed systems instead of traditional databases to handle data more efficiently and securely. Embedders are essentially LLMs with different final layers that convert text to vecto
- Oct 5, 20261h 7m
Episode #575: The Imagination Bottleneck: When Technology Finally Catches Up to Human Creativity
In this episode of the Crazy Wisdom Podcast, host Stewart Alsop sits down with UI designer Peter Petrash to explore the evolving landscape of user interface design and technology. Peter walks through his journey from discovering the early web on GeoCities to working with gaming companies in the nineties, and how his background shaped his current perspective on design. The conversation moves from the technical foundations of web design to deeper philosophical territory, examining how AI is transforming creativity, whether machines can truly understand taste and relevance, and what happens when human imagination becomes the bottleneck instead of technology. They discuss everything from self-driving cars in San Francisco and urban design principles to the future of rendering engines, the meaning crisis in an age of infinite content generation, and why formally defining goals often backfires—all while considering what it means to design interfaces when the full breadth of imagination can finally be unleashed on screen. Key Insights7 Key Insights from Peter Petrash's Episode:1. The evolution of UI design has come full circle from requiring deep technical knowledge to becoming accessible through AI coding tools. Petrash describes starting in the Geocities era when designers had to master HTML, CSS, deployment, and package management just to get anything online. Today, tools like Claude handle most of the technical overhead, lowering barriers to entry. However, this democratization creates a new challenge where design principles and aesthetic judgment become more critical differentiators than coding ability.2. Visual design operates on fundamental principles rooted in human cognition, not just subjective taste. Petrash explains gestalt principles of visual perception, such as the law of proximity where elements placed close together are perceived as related, and common fate where animated movement helps brains track relationships. These principles represent an assembly code for visual design that could theoretically be formalized and validated, similar to how code is verified, though this remains largely unexplored territory for AI systems.3. The convergence of rendering technologies HTML, SVG, 3D, shaders, and now AI video generation points toward a future where any imaginable visual can be rendered on screen. Petrash describes emerging AI video models that can generate interactive, realistic environments without traditional rendering engines or lighting calculations. This represents a fundamental shift where the technical constraints that historically limited designers are disappearing, making imagination and taste the primary bottlenecks rather than technical capability.4. Voice interfaces and multimodal AI create opportunities for capturing nuanced human communication that text misses. Petrash notes that when speaking spontaneously, humans convey uncertainty through pauses, vocal modulation, and hesitation patterns that current AI systems largely ignore through transcription to text. He envisions AI that could detect these subtle cues and adjust responses accordingly, offering educational context when uncertainty is detected or providing more confident direction when the user seems assured.5. The professionalization of UI design creates a constant critical awareness of environmental design flaws that can become psychologically taxing. Petrash describes how training in user experience design makes designers perpetually scrutinize their surroundings, noticing poor design choices like Norman Doors that have push bars but require pulling. This occupational hazard of seeing flaws everywhere extends beyond digital interfaces to urban planning, architecture, and physical products, requiring conscious effort to disengage from this analytical mode.6. The democratization of creative tools through AI is creating an attention economy where imagination itself may become currency, but also risks imaginative exhaustion. Petrash reflects on how technology has finally caught up to human imagination after decades of the reverse being true, but now finds himself coming up dry creatively as everyone's imagination is simultaneously unleashed on platforms like Twitter. This creates a paradoxical situation where infinite creative possibility collides with limited human attention and the challenge of generating meaningful novelty rather than novelty for its own sake.7. Goal setting and life planning tools built on AI fundamentally misunderstand how human motivation and meaning work, revealing limits in artificial intelligence despite mathematical prowess. Petrash describes how apps attempting to help users identify and achieve goals through explicit articulation and reminders often fail because formalizing goals in writing strips away essential implicit elements. This Jungian insight suggests that bringing things fully into intellec
- Sep 28, 202647 min
Episode #574: Evals, Ontologies and the Unmappable World of Business
In this episode of the Crazy Wisdom Podcast, host Stewart Alsop sits down with Ryan Marsh of thestack.io to explore what it really takes to build production AI systems. They discuss how production AI has evolved from simple prompt-to-API demos into complex systems requiring evaluation suites, human-in-the-loop feedback mechanisms, and sophisticated approaches to handling context and data retrieval. The conversation covers the challenges of domain mapping, the fundamental difficulty of translating messy human business processes into structured systems, and the role of ontologies in AI development. Ryan and Stewart also examine the limitations of LLMs, the debate between specialization versus generalization in AI models, consciousness and cognition in system design, and the regulatory landscape facing AI companies. They touch on infrastructure constraints, the democratization of AI through open source models, and whether we'll eventually hit a ceiling where human intelligence can no longer distinguish between increasingly capable AI models. Key Insights1. Production AI systems today fundamentally differ from demos through their reliance on comprehensive evaluation suites that function like unit tests to measure and maintain quality, though they cannot be as deterministic. The key distinction is that production systems require clearly defined metrics for what good looks like, along with feedback mechanisms that allow the system to evolve over time. Without this foundational understanding of success metrics and continuous improvement processes, a system is not truly production-ready regardless of how many users it serves.2. The fundamental challenge in building production AI systems is not the technology itself but rather mapping business domains into structured formats that models can work with effectively. This problem of translating messy, subjective human processes and language into precise specifications has plagued software engineering for decades. Different people within organizations use the same words to mean completely different things, and humans naturally operate with assumed context and imprecision that must be explicitly defined for AI systems to function reliably.3. Large language models excel at generalization but struggle with specialization, which creates friction in production environments where specific outputs or styles are required. While they can code in any programming language, getting them to write code exactly the way a particular engineer wants remains extremely difficult. This explains why professional documentation and specialized coding tasks often require extensive prompting and fighting with the models, as they naturally gravitate toward their trained patterns rather than highly specific user preferences.4. Modern production AI systems primarily solve classification problems wrapped in natural language interfaces rather than requiring true open-ended cognition. The models work best when they can leverage reasoning over provided information to make verifiable decisions, but they still lack common sense despite their vast knowledge. Success comes from teaching models everything about your specific domain and what good and bad outcomes look like, rather than relying solely on their general intelligence.5. Context retrieval in production AI systems is fundamentally a data storage, search, and retrieval problem that has been solved many different ways throughout computing history. The appropriate solution depends entirely on the type of data being accessed, whether through vector databases, graph databases, relational databases, or even simple text search. The harnesses and frameworks for orchestrating AI agents have matured significantly, making the real challenge the quality and structure of the data being fed to these systems.6. Human-in-the-loop feedback mechanisms are essential for production AI because models will inevitably encounter situations they have not been trained to handle. When confidence is low or novel scenarios appear, systems should flag these for human review rather than proceeding blindly. The feedback provided during these interventions must be captured and scored so it can be incorporated into the permanent behavior of the system, creating a continuous improvement cycle similar to how model vendors perform reinforcement learning on their base models.7. The rush to regulation in the AI industry is driven primarily by the fact that these companies are currently completely exposed to existing consumer protection and liability laws with no legal precedents to protect them. If AI agents cause harm, companies could be sued into oblivion under current law. By establishing compliance frameworks through regulation, these companies can create carve-outs and exemptions that limit their liability when they follow prescribed rules, similar to how heavily regulated industries like airl
- Sep 21, 202654 min
Episode #573: Beyond the Hype: Building Robots for Actual Humans
Stewart Alsop sits down with Antoine Marcel, CEO and cofounder of Flourish, to explore the future of home robotics and what he calls "solarpunk" automation. Antoine discusses why Flourish is building wheeled robots with arms instead of following the humanoid hype, arguing that the expensive legs and teleoperation approach isn't practical for real consumers today. The conversation ranges from the philosophy of robots that disappear when you're home (rather than becoming constant companions) to the bureaucratic nightmares of modern life that robotics might actually solve. Antoine explains how their $3,500 robots can be trained in thirty minutes using just your phone to handle personalized household tasks, and why saving people two hours a day—not just ten minutes—is the threshold for consumer adoption. They also dig into whether Paris will become a solarpunk or cyberpunk city, why Apple represents solarpunk design principles, and how the first 50 Flourish units are about to ship worldwide at flourish-robots.com. Key Insights1. The core innovation behind Flourish is building mobile-based robots with arms but no legs, moving on wheels rather than attempting humanoid locomotion. This design choice stems from practical considerations since even humanoid robots cannot reliably handle stairs, and legs represent approximately twenty thousand dollars of the cost in humanoid platforms. By eliminating legs, the company can focus on delivering functional value to consumers today rather than developing research products. The founder emphasizes that accessibility and usefulness are paramount, rejecting the demonstration-focused approach that dominates the robotics industry where companies raise billions on teleoperated demos that never ship to actual consumers.2. The founder's background in automation across logistics, software, and sales led him to recognize that the most painful tasks in life occur outside the computer. After automating digital work processes, he realized that returning home after a long workday to clean, cook, and tidy remained exhausting and unaddressed by existing technology. This insight, combined with advances in physical AI over the past three years, drove him to develop the first robot prototype that runs in his own home, constantly testing and iterating based on real-world use cases like navigating doorways and picking up scattered items from floors.3. The company is building toward a solarpunk vision rather than cyberpunk future, where robots disappear from conscious awareness and operate silently in the background. The founder envisions robots emerging from closets when people leave home, performing cleaning and tidying tasks, then retreating before residents return. This approach contrasts sharply with the prevalent humanoid robot vision featuring teleoperated machines with cameras potentially monitored by remote operators. The goal is reducing technology's presence in daily life to create more time for family, creativity, and meaningful activities rather than introducing intrusive robotic presences into living spaces.4. Consumer robotics faces fundamental challenges that explain why almost no companies successfully sell to end users despite massive investment in the sector. Industrial robotics offers clearer paths to revenue, while consumer products require extensive market education, safety certifications, and must genuinely change lives rather than provide marginal improvements. The founder argues that saving people ten or thirty minutes daily is insufficient and forgettable, but saving two hours daily creates transformative value that sustains product adoption. This high bar for meaningful impact explains why the consumer robotics market remains largely unaddressed despite technological capabilities.5. The business model centers on teaching robots specific tasks through a thirty-minute training process using a smartphone, then fine-tuning AI models for those particular workflows. Rather than pursuing general physical AI that can handle any task, Flourish enables users to create custom automations for their specific needs, like retrieving items from the refrigerator or collecting clothes for the washer. This approach acknowledges that general physical AI is not yet ready while still delivering practical value through task-specific learning. The company plans to launch fifty units at thirty-five hundred dollars, shipping worldwide, with rapid sellout expected due to existing demand.6. The robot extends vertically to reach from floor level to table height, enabling tasks like cleaning surfaces and accessing shelves while maintaining a compact mobile base that can navigate through doorways. Current limitations include lack of waterproofing, preventing dishwashing tasks, and challenges with delicate object manipulation requiring advanced tactile sensing. The founder uses first-generation
- Sep 14, 20261h 2m
Episode #572: The Horizontal Truth: Why Everything You Know About Movement is Wrong
In this episode of the Crazy Wisdom Podcast, host Stewart Alsop sits down with Neil Bortolus, founder of Esteem Biomechanics and Stewart's personal trainer for the past year and a half, to explore the intersection of functional movement, human biology, and emerging technology. Their conversation ranges from the principles of biomechanics and the evolutionary blueprint of human movement—standing, walking, running, and throwing—to the dangers of modern movement practices that prioritize aesthetics over function, the role of fascia and connective tissue in chronic pain, and why most gym exercises fail to respect our biological needs. They tackle bigger questions about where technology is taking us, from AI and LLMs replacing human cognitive work to transhumanism and whether we're headed toward a cyberpunk dystopia or a solarpunk regenerative future, all while emphasizing the importance of first principles thinking, the need for humans to remain in the loop, and creating environments that promote biological and mental regeneration rather than degeneration. Visit Neil's website atesteembiomechanics.comTimestamps00:00 Stewart introduces Neil Bortolus, founder of Esteem Biomechanics and his trainer for a year and a half, discussing their interesting conversations about movement and health05:00 Discussion shifts to negative and positive pressure in the body, how athletes naturally create proper core tension, and the differences between training high-performance athletes versus clients with cerebral palsy or stroke recovery10:00 Stewart shares his journey through yoga and discovering modern postural yoga's surprising origins in late 1800s British calisthenics mixed with Indian anticolonial movements, questioning why modern movement practices are fundamentally flawed15:00 Neil explains functional patterns' first principles approach, emphasizing standing walking running and throwing as evolutionary movement patterns that should guide all exercise design20:00 Conversation explores Brian Johnson's technology-focused longevity experiments versus optimizing natural biological function, questioning whether peak biology or technology interventions lead to better health outcomes25:00 Neil describes environmental factors like Southern California and Hawaii providing optimal conditions for human health through fresh food, sunlight, and natural grounding environments year-round30:00 Discussion of solarpunk versus cyberpunk futures, examining how technology could either support biological health or create dependency, with Stewart sharing his Invisalign experience as cautionary tale35:00 Neil and Stewart analyze education systems, influencer marketing, and Jacques Ellul's concept of technique autonomously taking over human goals and replacing both muscle and mental labor40:00 Neil questions Stewart's first principles for approaching technology, leading to discussion about using AI to accomplish tasks faster while maintaining human oversight and decision-making authority45:00 Stewart explains the black box problem with LLMs and neural networks, admitting he's built applications he can't fully understand, questioning whether understanding mechanisms matters if outputs work50:00 Debate about benevolent AI governance using El Salvador's transformation as analogy, questioning whether society should surrender control to AI systems for farming or other critical infrastructure55:00 Final discussion on determinism versus free will, alignment problems in AI development, and Neil's concept of regenerative versus degenerative environments for optimal human biological and mental healthKey Insights1. Movement training must be adapted to individual needs and capabilities rather than following a one-size-fits-all approach. Neil explains that working with clients ranging from those with cerebral palsy and stroke recovery to high-performance athletes requires fundamentally different techniques. While stroke and cerebral palsy clients experience every improvement as monumental, such as being able to walk a mile, elite athletes already possess optimal biomechanics and need only tiny refinements. The challenge lies in keeping athletes motivated through detailed adjustments while helping severely impaired clients build basic functions from the ground up.2. Modern fitness and yoga practices are built on flawed first principles that prioritize aesthetics over functional movement. The conversation reveals how modern postural yoga was actually invented in the late 1800s through a combination of British calisthenics and Indian anticolonial movements, not ancient wisdom as commonly believed. Similarly, conventional gym exercises and bodybuilding focus on how bodies look rather than how they function evolutionarily. The key insight from Functi
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About Crazy Wisdom
In his series "Crazy Wisdom," Stewart Alsop explores cutting-edge topics, particularly in the realm of technology, such as Urbit and artificial intelligence. Alsop embarks on a quest for meaning, engaging with others to expand his own understanding of reality and that of his audience. The topics covered in "Crazy Wisdom" are diverse, ranging from emerging technologies to spirituality, philosophy, and general life experiences. Alsop's unique approach aims to make connections between seemingly unrelated subjects, tying together ideas in unconventional ways.
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