Podcasts about Robotics

Tensions rise as Discovery Mountain debates a tech-free challenge—can the town unplug without unraveling?

This week, the boys grabbed some beers and kept it positive while they fired off some mini-reviews before featuring a conversation about “Eternal Sunshine of the Spotless Mind”. As part of the random year generator series, 2004 was a great year for movies, with over 50 $100m movies and many likable ones. While “Eternal Sunshine” didn't gross in the top 70, it may be the year's greatest film. Props to Michel Gondry and Charlie Kaufman for giving Jim Carrey and Kate Winslet some juicy roles and incredibly shifty worlds! As for the mini-reviews, the boys can't speak highly enough of Gore Verbinski's “Good Luck, Have Fun, Don't Die”, starring Sam Rockwell, and the intense and captivating “If I Had Legs I'd Kick You”, and the Academy Award-nominated “It Was Just An Accident”. Grab some beers and join us!  linktr.ee/theloveofcinema - Check out our YouTube page!  Our phone number is 646-484-9298. It accepts texts or voice messages.  0:00 Intro; 04:19 “If I Had Legs I'd Kick You” mini-review; 12:10 “Good Luck, Have Fun, Don't Die” mini-review; 18:24 “It Was Just An Accident” mini-review; 22:20 2004 Year in Review; 39:01 Films of 2004: “Eternal Sunshine of the Spotless Mind”; 1:16:10 What You Been Watching?; 1:23:05 Next Week's Episode Teaser Additional Cast/Crew: Michel Gondry, Charlie Kaufman, Pierre Busmuth, David Cross, Elijah Wood, Mark Ruffalo, Kirsten Dunst, Tom Wilkinson, Sam Rockwell, Gore Verbinski, Michael Pena, Zazie Beetz, Haley Lu Richardson, Juno Temple, Jafar Panahi, Rose Byrne, Conan O'Brien, A$AP Rocky. Hosts: Dave Green, Jeff Ostermueller, John Say Edited & Produced by Dave Green. Beer Sponsor: Carlos Barrozo Music Sponsor: Dasein Dasein on Spotify: https://open.spotify.com/artist/77H3GPgYigeKNlZKGx11KZ 
Dasein on Apple Music: https://music.apple.com/us/artist/dasein/1637517407 Recommendations: Fallout, Star Trek: Starfleet Academy, They Live, Paradise, John Carpenter, The Muppet Series, Bedknobs and Broomsticks, The Pitt, Blue Moon, A Knight of the Seven Kingdoms.  Additional Tags: Old Man Marley, Home Alone, Shawshenk Redemption, Gordon Ramsay, Thelma Schoonmaker, Stephen King's It, The Tenant, Rosemary's Baby, The Pianist, Cul-de-Sac, AI, The New York City Marathon, Apartments, Tenants, Rent Prices, Zohran Mamdani, Andrew Cuomo, Curtis Sliwa, Amazon, Robotics, AMC, IMAX Issues, Tron, The Dallas Cowboys, Short-term memory loss, Warner Brothers, Paramount, Netflix, AMC Times Square, Tom Cruise, George Clooney, MGM, Amazon Prime, Marvel, Sony, Conclave, Here, Venom: The Last Dance, Casablanca, The Wizard of Oz, Oscars, Academy Awards, BFI, BAFTA, BAFTAS, British Cinema. England, Vienna, Leopoldstadt, The Golden Globes, Past Lives, Apple Podcasts, West Side Story, Adelaide, Australia, Queensland, New South Wales, Melbourne, The British, England, The SEC, Ronald Reagan, Stock Buybacks, Marvel, MCU, DCEU, Film, Movies, Southeast Asia, The Phillippines, Vietnam, America, The US, Academy Awards, WGA Strike, SAG-AFTRA, SAG Strike, Peter Weir, Jidaigeki, chambara movies, sword fight, samurai, ronin, Meiji Restoration, plague, HBO Max, Amazon Prime, casket maker, Seven Samurai, Roshomon, Sergio Leone, Clint Eastwood, Stellan Skarsgard, the matt and mark movie show.The Southern District's Waratah Championship, Night of a Thousand Stars, The Pan Pacific Grand Prix (The Pan Pacifics), Jeff Bezos, Rupert Murdoch, Larry Ellison, David Ellison, Elon Musk, Mark Zuckerberg.   

There's been backlash against Amazon's Ring doorbells after the company put out a commercial showing how footage from their devices can help find lost pets. Kristen Thomasen is the University of Windsor's Chair in Law, Robotics, and Society and she talks about why people should feel concerned about their privacy in their own neighbourhoods and what further guardrails need to be in place around surveillance technology

The integration of Artificial Intelligence (AI) into post-injury rehabilitation is transforming recovery paradigms by enabling personalized, adaptive, and efficient rehabilitation pathways tailored to individual patient needs. This podcast reviews the current advances in AI applications that facilitate assessment, monitoring, and optimization of rehabilitation programs following injuries. Through machine learning algorithms, wearable sensors, and predictive analytics, AI enhances the precision of therapy plans, tracks patient progress in real-time, and predicts recovery trajectories. The discussion includes the benefits of AI-driven rehabilitation, including improved functional outcomes, reduced recovery times, and increased patient engagement. It also addresses challenges such as data privacy, algorithmic bias, and integration with clinical workflows.  1. Transforming recovery paradigms Traditional post‑injury rehab relies on periodic in‑person assessments, therapist intuition, and standardized protocols that only partially account for individual variability. AI is shifting this model toward: Continuous, data‑driven care: Instead of snapshots in clinic, rehab can be informed by near real‑time streams of kinematic, physiological, and behavioral data from wearables, smart devices, and robot interfaces. Dynamic adaptation: Therapy intensity, task difficulty, and exercise selection can be automatically adjusted based on ongoing performance, fatigue, and recovery trends, rather than fixed schedules. Precision rehabilitation: Algorithms can identify which patients are likely to respond to specific interventions (e.g., constraint‑induced movement therapy vs robotics) and tailor plans accordingly. This moves rehabilitation from a "one‑size‑fits‑many" paradigm toward precision, context‑aware therapy, analogous to precision oncology but focused on function and participation. 2. Assessment, monitoring, and optimization AI for assessment Sensor‑based movement analysis: Machine learning models process accelerometer, IMU, EMG, and pressure data to quantify gait symmetry, joint kinematics, balance, and fine motor control with higher resolution than visual observation alone. Automated scoring: AI can approximate or support standardized scales (e.g., Fugl‑Meyer, Berg Balance Scale) by mapping sensor features or video-derived pose estimates to clinical scores, reducing inter‑rater variability and saving clinician time. Continuous monitoring Home and community tracking: Wearable and ambient sensors enable monitoring of daily steps, walking speed, arm use, posture, and adherence to exercises outside the clinic, feeding rich longitudinal datasets into AI models. Real‑time alerts: Algorithms can detect abnormal patterns—such as increased fall risk, reduced limb use, or signs of over‑exertion—and flag the clinician or adjust digital therapy content automatically. Optimization and decision support Predictive models: Using historical data, AI can forecast functional gains, plateau points, or risk of complications (e.g., falls, readmission), supporting individualized goal‑setting and resource allocation. Reinforcement learning and "digital twins": Emerging work in neurorehabilitation treats rehab as a sequential decision problem, using model‑based reinforcement learning and patient "digital twins" to recommend optimal timing, dosing, and progression of interventions over weeks to months.​ 3. Technologies: ML, wearables, analytics Machine learning algorithms: Supervised ML classifies movement quality (normal vs compensatory), detects exercise type from sensor streams, and estimates clinical scores. Unsupervised learning clusters patients into phenotypes (e.g., gait patterns after stroke), revealing subgroups that respond differently to certain therapies. Reinforcement learning and contextual bandits explore which therapy adjustments yield the best long‑term functional outcomes for a given individual.​ Wearable sensors and robotics: Inertial sensors, EMG, pressure insoles, and exoskeleton sensors capture high‑frequency movement and muscle activity data during training. Robotic devices (upper‑limb exoskeletons, gait trainers) coupled with AI can modulate assistance, resistance, or task difficulty in real time based on performance and predicted fatigue. Predictive and prescriptive analytics: Predictive analytics estimate trajectories (e.g., time to independent walking, expected upper‑limb function) to inform shared decisions with patients and families. Prescriptive analytics recommend therapy intensity, modality mix, and scheduling to maximize functional gains under resource constraints. 4. Benefits: outcomes, efficiency, engagement Improved functional outcomes: Studies report better motor recovery, gait quality, and ADL performance when AI‑assisted training is used—especially when robotics and intelligent feedback are involved. Reduced recovery time and resource use: More precise dosing and earlier identification of non‑responders can reduce ineffective sessions, shorten time to key milestones, and support safe earlier discharge with robust remote follow‑up. Increased adherence and engagement: AI‑driven digital rehab platforms use gamification, adaptive difficulty, and personalized feedback to keep patients engaged in home programs, improving adherence compared to static paper instructions. Support for clinicians: Instead of replacing therapists, AI can offload repetitive measurement tasks, highlight concerning trends, and offer data‑driven suggestions, allowing clinicians to focus on relational, motivational, and complex decision‑making aspects of care. 5. Challenges and ethical considerations Data privacy and security: Rehab AI often relies on continuous collection of sensitive motion, physiological, and sometimes audio/video data, raising questions about consent, storage, secondary use, and breach risk. Approaches like federated learning and on‑device processing are being explored to reduce centralization of identifiable data while still enabling model training. Algorithmic bias and fairness: If training data under‑represent older adults, women, certain racial/ethnic groups, or people with severe disability, AI models may misestimate performance or risk for those groups, potentially widening disparities in rehab access and outcomes. Ongoing auditing, diverse datasets, and participatory design with patients and clinicians are needed to ensure equitable performance. Integration with clinical workflows: Many AI tools are developed in research settings and are not yet seamlessly integrated into EHRs, scheduling systems, or therapist documentation workflows. Poorly integrated tools risk adding documentation burden or "alert fatigue," reducing adoption. Successful implementations co‑design interfaces with frontline therapists and physicians. Regulation, liability, and trust: It remains unclear in many jurisdictions how to regulate adaptive rehab algorithms (as medical devices, clinical decision support, or wellness tools) and who is liable when AI‑informed plans cause harm.​ Transparent, explainable models and clear communication to patients about the role of AI are critical for maintaining trust. 6. Case studies and emerging trends Remote and hybrid digital rehabilitation: AI‑driven platforms providing home‑based stroke, orthopedic, or Parkinson's rehab with clinician dashboards are improving adherence and extending care beyond brick‑and‑mortar clinics. Collaborative AI for precision neurorehabilitation: Frameworks combining patient‑clinician goal setting, digital twins, and reinforcement learning exemplify "collaborative AI" that augments rather than replaces therapists.​ Multimodal personalization: Integration of movement data, EMG, heart rate, sleep, and self‑reported pain/fatigue is enabling more nuanced adaptation to daily fluctuations in capacity. Conversational AI for education and coaching: Early work is assessing tools like ChatGPT as low‑risk supports for exercise education and motivation, though they are not yet precise enough to replace professional plan design AI is moving rehab toward patient‑centered, continuously adapting, and data‑rich care, but realizing this promise depends on addressing privacy, bias, workflow, and regulatory challenges in partnership with clinicians and patients.

What does it take to operate where humans can't survive?Live from Florence at the Baker Hughes Annual Meeting, hosts David de Roode and Victoria Beard Queen sit down with Oceaneering CEO and President Rod Larson for a high-impact mini episode.From subsea ROVs and autonomy to crisis resilience and leading 14,000 people worldwide, Rod shares how a 60-year-old diving company evolved into a global robotics powerhouse—serving offshore energy, NASA, defense, and even theme parks.Pressure, corrosion, uptime, leadership under fire, this one goes deep.Full episode coming soon.00:00 Why Oil & Gas Still Powers Modern Life00:37 Show + Sponsors02:00 Rod Larson Intro (Oceaneering CEO)03:08 From Diving to ROVs & Robotics04:11 Beyond Oil & Gas05:45 Engineering the Deep08:16 ROV Fleets & Upgrades09:23 Hiring & Culture10:54 Leading 14,000 Globally13:09 Rod's Career Journey16:11 Career Advice17:26 Leading Through Crisis20:19 Resiliency & Risk Planning22:34 AI & Energy Insights24:13 Rapid-Fire25:46 Wrap-Up

On today’s program, I’m speaking with Tessa Pugh, coach of Wildbot Robotics, the Columbus Junction High School robotics team. Also joining us are team captain Kai Allec and lieutenant Gannon Holladay. We’re speaking about the robotics’ team’s

Ted Thatcher discusses market unease and the state of the broad economy. He's hopeful for the year still, breaking down the latest GDP report and what he thinks investors are missing. Ted covers why he thinks Amazon (AMZN) is an appealing buy here, arguing that the opportunity for AWS within AI is underappreciated, and why it can hit the “singularity” in the robotics revolution first.======== Schwab Network ========Empowering every investor and trader, every market day.Options involve risks and are not suitable for all investors. Before trading, read the Options Disclosure Document. http://bit.ly/2v9tH6DSubscribe to the Market Minute newsletter - https://schwabnetwork.com/subscribeDownload the iOS app - https://apps.apple.com/us/app/schwab-network/id1460719185Download the Amazon Fire Tv App - https://www.amazon.com/TD-Ameritrade-Network/dp/B07KRD76C7Watch on Sling - https://watch.sling.com/1/asset/191928615bd8d47686f94682aefaa007/watchWatch on Vizio - https://www.vizio.com/en/watchfreeplus-exploreWatch on DistroTV - https://www.distro.tv/live/schwab-network/Follow us on X – https://twitter.com/schwabnetworkFollow us on Facebook – https://www.facebook.com/schwabnetworkFollow us on LinkedIn - https://www.linkedin.com/company/schwab-network/About Schwab Network - https://schwabnetwork.com/about

Farmers considering the latest technologies in robotics and artificial intelligence have to determine: is it safe?

Industrial and logistics automation continues to expand, yet many robots still struggle with tasks that humans perform effortlessly. A major limitation has been the absence of a true sense of touch. XELA Robotics focuses on tactile sensing technology that can be integrated into existing robot hands and grippers, giving machines the ability to feel pressure, contact, and subtle variations in objects. This capability allows robots to handle items more precisely, safely, and reliably in complex environments.Rather than manufacturing complete robotic arms, the company develops tactile sensor systems that are embedded into a wide range of end effectors. These sensors provide detailed feedback about contact forces, object position, and surface characteristics. With this information, robots can adjust their grip, detect misalignment, and avoid damaging delicate components. The result is a more human‑like interaction with physical objects, which is essential for advanced automation in factories and warehouses.Applications in Factory and Warehouse AutomationIn factory environments, many tasks require precise insertion, alignment, and handling of components. Visual systems alone can struggle with small tolerances or occluded parts. By adding tactile sensing from XELA Robotics, robots can detect whether a connector, memory module, or other component is properly aligned and seated. Force feedback enables fine adjustments during insertion, reducing the risk of damage and increasing process reliability. This is particularly valuable in electronics manufacturing and other high‑precision assembly operations.Warehouse automation presents a different set of challenges. Robots are often required to grasp items they have never encountered before, with varying shapes, weights, and textures. Tactile sensors allow a robot to feel how heavy an object is, how hard or soft it is, and whether it is slipping from its grasp. Grip forces can then be adjusted dynamically to prevent drops while avoiding excessive pressure. This adaptability supports more robust pick‑and‑place operations and enables automation of tasks that previously depended on human dexterity.Customization, Integration, and DeploymentXELA Robotics works with customers to integrate tactile sensors into specific robot hands and grippers. The process typically begins with an understanding of the target application, the type of end effector being used, and the performance requirements. Sensor modules are then selected or customized to fit the geometry and functional needs of the system. Software tools and interfaces are provided to make it easier to interpret tactile data and incorporate it into control strategies.Deployment timelines vary by use case but can often be achieved within a few months. During this period, testing and refinement are carried out to ensure that the tactile feedback is being used effectively. The company's ability to tailor solutions to individual applications is a key strength, allowing enterprises to address unique handling challenges without redesigning entire robotic platforms. The cost of the tactile sensing solution is positioned as a small fraction of the overall robot system, making it an attractive investment relative to the gains in automation and reliability.Economic Impact and Operational BenefitsMany of the tasks targeted by tactile sensing are still performed by human workers, particularly in warehouses and manual assembly lines. By enabling robots to handle more complex and delicate operations, companies can automate a larger share of their workflows. This can lead to significant labor savings, extended operating hours, and improved consistency. Automated systems can run around the clock, do not require sick leave, and reduce exposure to repetitive or ergonomically challenging tasks.Analytics derived from tactile data provide additional value. Robots can determine whether the correct number of items has been grasped, whether the right object has been picked, and how often certain motions occur. This information supports quality control, process optimization, and predictive maintenance. As product lines change, the same tactile sensors can be used to adapt to new items, reducing the need for frequent hardware changes.ConclusionXELA Robotics advances automation by giving robots a practical sense of touch through integrated tactile sensing technology. By enabling more precise handling, better alignment, and adaptive gripping, these systems expand what robots can reliably accomplish in factories and warehouses. The combination of customizable hardware, supporting software, and strong economic benefits positions tactile sensing as a foundational capability for the next generation of robotic automation.Interview by Don Baine, The Gadget Professor.Sponsored by: Get $5 to protect your credit card information online with Privacy. Amazon Prime gives you more than just free shipping. Get free music, TV shows, movies, videogames and more. Secure your connection and unlock a faster, safer internet by signing up for PureVPN today.

Industrial and logistics automation continues to expand, yet many robots still struggle with tasks that humans perform effortlessly. A major limitation has been the absence of a true sense of touch. XELA Robotics focuses on tactile sensing technology that can be integrated into existing robot hands and grippers, giving machines the ability to feel pressure, contact, and subtle variations in objects. This capability allows robots to handle items more precisely, safely, and reliably in complex environments.Rather than manufacturing complete robotic arms, the company develops tactile sensor systems that are embedded into a wide range of end effectors. These sensors provide detailed feedback about contact forces, object position, and surface characteristics. With this information, robots can adjust their grip, detect misalignment, and avoid damaging delicate components. The result is a more human‑like interaction with physical objects, which is essential for advanced automation in factories and warehouses.Applications in Factory and Warehouse AutomationIn factory environments, many tasks require precise insertion, alignment, and handling of components. Visual systems alone can struggle with small tolerances or occluded parts. By adding tactile sensing from XELA Robotics, robots can detect whether a connector, memory module, or other component is properly aligned and seated. Force feedback enables fine adjustments during insertion, reducing the risk of damage and increasing process reliability. This is particularly valuable in electronics manufacturing and other high‑precision assembly operations.Warehouse automation presents a different set of challenges. Robots are often required to grasp items they have never encountered before, with varying shapes, weights, and textures. Tactile sensors allow a robot to feel how heavy an object is, how hard or soft it is, and whether it is slipping from its grasp. Grip forces can then be adjusted dynamically to prevent drops while avoiding excessive pressure. This adaptability supports more robust pick‑and‑place operations and enables automation of tasks that previously depended on human dexterity.Customization, Integration, and DeploymentXELA Robotics works with customers to integrate tactile sensors into specific robot hands and grippers. The process typically begins with an understanding of the target application, the type of end effector being used, and the performance requirements. Sensor modules are then selected or customized to fit the geometry and functional needs of the system. Software tools and interfaces are provided to make it easier to interpret tactile data and incorporate it into control strategies.Deployment timelines vary by use case but can often be achieved within a few months. During this period, testing and refinement are carried out to ensure that the tactile feedback is being used effectively. The company's ability to tailor solutions to individual applications is a key strength, allowing enterprises to address unique handling challenges without redesigning entire robotic platforms. The cost of the tactile sensing solution is positioned as a small fraction of the overall robot system, making it an attractive investment relative to the gains in automation and reliability.Economic Impact and Operational BenefitsMany of the tasks targeted by tactile sensing are still performed by human workers, particularly in warehouses and manual assembly lines. By enabling robots to handle more complex and delicate operations, companies can automate a larger share of their workflows. This can lead to significant labor savings, extended operating hours, and improved consistency. Automated systems can run around the clock, do not require sick leave, and reduce exposure to repetitive or ergonomically challenging tasks.Analytics derived from tactile data provide additional value. Robots can determine whether the correct number of items has been grasped, whether the right object has been picked, and how often certain motions occur. This information supports quality control, process optimization, and predictive maintenance. As product lines change, the same tactile sensors can be used to adapt to new items, reducing the need for frequent hardware changes.ConclusionXELA Robotics advances automation by giving robots a practical sense of touch through integrated tactile sensing technology. By enabling more precise handling, better alignment, and adaptive gripping, these systems expand what robots can reliably accomplish in factories and warehouses. The combination of customizable hardware, supporting software, and strong economic benefits positions tactile sensing as a foundational capability for the next generation of robotic automation.Interview by Don Baine, The Gadget Professor.Sponsored by: Get $5 to protect your credit card information online with Privacy. Amazon Prime gives you more than just free shipping. Get free music, TV shows, movies, videogames and more. Secure your connection and unlock a faster, safer internet by signing up for PureVPN today.

Feb 20, 2026: AI is already deciding who gets hired, promoted, and fired — and there are almost no rules governing how it does any of that. In this episode, I'm building those rules. I call them the Five Laws of AI in the Workplace, constructed in the spirit of Asimov's Three Laws of Robotics — rigorous enough to pressure-test, honest enough to admit where they fall short. We cover the Law of Transparency — why 30 million job applicants in 2024 were evaluated by algorithms they never knew existed. The Law of Human Primacy — why a human rubber-stamping an AI decision isn't the same as a human making one. The Law of Honest Attribution — why AI washing is one of the most underreported forms of corporate dishonesty happening right now. The Law of True Cost Accounting — why the real costs of workforce cuts don't disappear, they just move to taxpayers and communities. And the Law of Reversibility — the full Klarna story, and why 31% of companies that made AI-driven layoffs ended up worse off than if they'd never done it.

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 ...

On the show this week, Gavin Kenneally, CEO and co-founder of Ghost Robotics, discusses the journey of the company from its inception to becoming a leader in legged robotics. He highlights the unique challenges and advantages of legged robots compared to wheeled counterparts, emphasizing their ability to navigate difficult terrains. The discussion also covers the importance of customer feedback in product development, the ruggedization of robots for military applications, and the future of robotics, including the potential for two-armed robots. Gavin shares insights on the impact of global events on robotics innovation and the company's commitment to keeping people out of harm's way. Learn more about Ghost Robotics: https://www.ghostrobotics.io ### – SPONSOR – Download the 2026 State of the Robotics Industry Report: https://www.therobotreport.com/state-of-robotics-industry-report-2026/

Fluent Fiction - Dutch: Bram's Blunder: Fashion Faux Pas to High-Tech Innovation Find the full episode transcript, vocabulary words, and more:fluentfiction.com/nl/episode/2026-02-20-08-38-20-nl Story Transcript:Nl: Bram stond midden in de drukke expohal van High-Tech City.En: Bram stood in the middle of the bustling expo hall of High-Tech City.Nl: Rondom hem waren geluiden van pratende mensen, piepende robots en flikkerende schermen.En: Around him were sounds of people talking, beeping robots, and flickering screens.Nl: Het was winter en de lucht was fris toen hij naar binnen kwam, zijn adem in wolkjes voor zich uitblazend.En: It was winter, and the air was crisp when he entered, his breath puffing out in little clouds.Nl: Hij had hoge verwachtingen van de dag.En: He had high expectations for the day.Nl: Bram droomde ervan om een groot uitvinder te worden en hoopte dat een keynote speech hem de inspiratie kon geven die hij zocht.En: Bram dreamed of becoming a great inventor and hoped that a keynote speech could give him the inspiration he was looking for.Nl: Maar Bram had een klein probleempje.En: But Bram had a small problem.Nl: Hij was een beetje afwezig.En: He was a little absent-minded.Nl: Terwijl hij door de mensenmassa liep, raakte hij in de war door alle borden en richtingen.En: As he walked through the crowd, he got confused by all the signs and directions.Nl: Voor hij het wist, zat hij niet in een zaal voor een tech keynote, maar bij een modeshow.En: Before he knew it, he wasn't in a hall for a tech keynote but at a fashion show.Nl: Hij keek om zich heen en zag dat de andere bezoekers bij hem op de rij opgewonden fluisterden, wijzen en hun camera's vastpakten.En: He looked around and saw the other visitors in his row whisper excitedly, pointing and grabbing their cameras.Nl: Op dat moment begon de show.En: At that moment, the show began.Nl: Op het podium liepen glanzende robot-modellen heen en weer.En: On the stage, shiny robot models walked back and forth.Nl: In de lucht zweefden drones die de modellen belichtten met heldere lichten.En: Drones hovered in the air, lighting up the models with bright lights.Nl: Bram keek verbaasd, maar ook gefascineerd.En: Bram looked amazed, but also fascinated.Nl: Hij besloot te blijven zitten en te zien wat er gebeurde; nieuwsgierigheid nam de overhand.En: He decided to stay seated and see what happened; curiosity got the better of him.Nl: Terwijl de show vorderde, gebeurde er iets onverwachts.En: As the show progressed, something unexpected happened.Nl: Een van de robot-modellen struikelde en viel om.En: One of the robot models stumbled and fell.Nl: Er klonk een lichte gil uit het publiek.En: A light gasp came from the audience.Nl: Toen de robot viel, werd er plotseling overal olie gemorst.En: When the robot fell, oil was suddenly spilled everywhere.Nl: De vloeiende beweging van de blauwe vloeistof over de podiumvloer trok Brams aandacht.En: The flowing movement of the blue fluid across the stage floor caught Bram's attention.Nl: Plots had Bram een idee.En: Suddenly, Bram had an idea.Nl: Zijn gedachten draaiden als tandwielen in een machine.En: His thoughts whirred like gears in a machine.Nl: Wat als hij een gadget kon maken dat zulke rommel vanzelf opruimde?En: What if he could make a gadget that cleaned up such messes automatically?Nl: Iets dat snel, efficiënt en handig was?En: Something that was fast, efficient, and handy?Nl: Hij kon het voor zich zien: een klein autonoom apparaat dat vlekken opspoorde en ze direct schoonmaakte.En: He could see it in his mind: a small autonomous device that detected stains and cleaned them up immediately.Nl: Met een glimlach op zijn gezicht en nieuwe inspiratie pakte Bram een notitieboekje uit zijn tas.En: With a smile on his face and newfound inspiration, Bram took a notebook out of his bag.Nl: Midden in het applaus van de menigte begon hij snel zijn idee op te schrijven.En: Amidst the applause of the crowd, he quickly began to jot down his idea.Nl: Het was raar, bedacht hij, hoe een vergissing zo brilliant kon eindigen.En: It was strange, he thought, how a mistake could end so brilliantly.Nl: In de dagen die volgden, dacht Bram steeds meer aan zijn nieuwe project.En: In the days that followed, Bram thought more and more about his new project.Nl: Hij was in de war geweest, verdwaald in de exohal.En: He had been confused, lost in the expo hall.Nl: Maar nu kon hij alleen maar dankbaar zijn voor dat onverwachte moment.En: But now, he could only be grateful for that unexpected moment.Nl: Hij leerde iets belangrijks: soms komt de beste inspiratie uit de meest onverwachte hoeken.En: He learned something important: sometimes the best inspiration comes from the most unexpected corners.Nl: En met die gedachte keerde hij terug naar zijn werkplaats, klaar om te creëren en te ontdekken.En: And with that thought, he returned to his workshop, ready to create and discover.Nl: Zijn avontuur op de expo had een blijvende indruk achtergelaten.En: His adventure at the expo had left a lasting impression.Nl: Bram had nu vertrouwen in het onbekende.En: Bram now had confidence in the unknown.Nl: En dat vertrouwen, zo wist hij nu, was de sleutel tot ware innovatie.En: And that confidence, he now knew, was the key to true innovation. Vocabulary Words:bustling: drukkepuffing: uitblazendabsent-minded: afwezigconfused: in de warfascinated: gefascineerdcuriosity: nieuwsgierigheidstumbled: struikeldegasp: gilspill: morsenfluid: vloeistofwhirred: draaidengears: tandwielengadget: apparaatautonomous: autonoomstains: vlekkenjot down: opschrijvengrateful: dankbaarunexpected: onverwachteimpression: indrukinnovation: innovatieexpo: expokeynote: keynotedirections: richtingenwhisper: fluisterenmodels: modellenhovered: zweefdenapplause: applausmess: rommelefficient: efficiënthandy: handig

The future of agriculture will utilize the development and application of robotic technology.  Jaisimha Rao explains a robotic machine that uses AI and cameras to distinguish between crops and weeds, then sprays herbicides specifically on weeds. There is potential of humanoid robots in agricultureJ. Their AI system identifies weeds using visual recognition, contrasting it with text-based AI models like ChatGPT. The system involves collecting and annotating weed images by agronomists in India, which are then used to train the AI model. Once trained, the AI can recognize specific weed species and control the dual-tank system to apply the appropriate herbicides in a single pass. All mechanical manufacturing for their robots is conducted in the US, with only cameras being sourced from India. www.niqorobotics.com 

The February 20 edition of the AgNet News Hour focused squarely on agricultural automation, return on investment, and whether California is ready to truly support innovation in the field. Hosts Nick Papagni and Josh McGill broadcast on a drying Friday morning following recent storms, but the real spotlight was on cutting-edge technology unveiled at the 2026 World Ag Expo. The featured guest was Paul Mikesell, founder and CEO of Carbon Robotics, the company behind the LaserWeeder and the newly announced Autonomous Tractor Kit (ATK). Mikesell shared how he built the first version of the laser weeding system in his backyard after years of working with artificial intelligence in Silicon Valley. His goal was simple: apply advanced AI to solve real-world farming problems — specifically herbicide resistance, rising labor costs, and environmental concerns. The LaserWeeder uses AI-powered cameras and high-precision lasers to identify and eliminate weeds without chemicals. According to Mikesell, growers are seeing up to 80 percent savings on weed control while improving crop health and market timing. The key, he emphasized, is ROI. Farmers want automation to pay for itself in one to three years — not five or ten. That financial reality has shaped Carbon Robotics' business model and rapid global expansion into 15 countries. Beyond weed control, the company introduced Carbon ATK, an autonomous tractor kit that can convert existing tractors into self-driving units. Unlike other autonomous systems that shut down when encountering unexpected obstacles, Carbon's system allows remote operators to take control instantly, ensuring full workdays in the field. The technology is designed to handle tillage, spraying, and other field operations with real-time AI oversight. But the conversation also highlighted regulatory challenges in California. While self-driving vehicles operate on public streets in San Francisco, autonomous tractors face gray areas under state labor and safety regulations. Mikesell called for clearer policies that allow farmers to adopt the best tools available without unnecessary roadblocks. Papagni and McGill underscored the broader takeaway: automation must make financial sense for growers. With labor costs high and margins tight, farmers cannot afford technology that doesn't deliver quick, measurable returns. As AI continues to evolve rapidly, adaptability and affordability will determine which companies succeed. The episode closed with a call for common-sense leadership and stronger support for agriculture in California. As automation advances, the question remains — will policy keep pace with innovation?

Show Notes 20 February 2026Story 1: Watch a robot swarm “bloom” like a gardenSource: ArsTechnica.comLink: https://arstechnica.com/science/2026/01/watch-a-robot-swarm-bloom-like-a-garden/See research paper here: https://www.science.org/doi/10.1126/scirobotics.ady7233See video here: https://www.youtube.com/watch?v=80QqrFmvIr0&t=2sStory 2: Electric semi-trucks could serve as mobile AI data centers, a Belgian startup proposes Source: Interesting EngineeringLink: https://interestingengineering.com/ai-robotics/belgian-startup-data-centers-electric-semi-trucks See also: https://windrose.tech/Story 3: World-first - supercomputer discovered this invisible flaw in all jet enginesSource: Slashgear.com Link: https://www.slashgear.com/2093053/frontier-supercomputer-jet-engine-blade-simulation/ Story 4: Bubble Bots: Simple biocompatible microrobots autonomously target tumors Source: CaltechLink: https://researchimpact.caltech.edu/research-news/bubble-bots-simple-biocompatible-microrobots-autonomously-target-tumors See research paper here: https://www.nature.com/articles/s41565-025-02109-6 Honorable Mentions Story: Microbes in Space Mutated and Developed a Remarkable Ability Source: ScienceAlert.comLink: https://www.sciencealert.com/microbes-in-space-mutated-and-developed-a-remarkable-ability Story: Scientists watch microscopic plant 'mouths' breathing in real time with palm-sized tool Source: Live ScienceLink: https://www.livescience.com/planet-earth/plants/scientists-watch-microscopic-plant-mouths-breathing-in-real-time-with-palm-sized-tool Story: This Startup's Air‑Powered Fuel Could Rewrite the Future of EnergySource: Inc. MagazineLink: https://www.inc.com/maria-jose-gutierrez-chavez/this-startups-air-powered-fuel-could-rewrite-the-future-of-energy/91296983 Story: Psychedelics may rewire the brain to treat PTSD. Scientists are finally beginning to understand how.Source: LiveScience.comLink: https://www.livescience.com/health/mind/psychedelics-may-rewire-the-brain-to-treat-ptsd-scientists-are-finally-beginning-to-understand-how

Robotic surgery has moved from novelty to norm, and in this episode of Behind the Knife, Drs. James Jung and Joey Lew sit down with urologic pioneer and Medtronic CMO Dr. Jim Porter to dissect how we got here, what the data really say about “the death of laparoscopy,” and where competing robotic platforms like Hugo may take the field next. From ergonomics and education to economics and global access, they tackle both the hype and the hard questions around robotics as the future of minimally invasive surgery.Hosts: ·      James Jung, MD, PhD, Assistant Professor of Surgery, Duke University·      Joey Lew, MD, MFA, Surgical resident PGY-3, Duke University, @lew__actuallyLearning Goals: By the end of this episode, listeners will be able to:·      Describe key clinical, ergonomic, and educational drivers behind the rapid adoption of robotic surgery in the United States and globally.·      Summarize current evidence comparing robotic and laparoscopic approaches for common procedures, including where outcomes are equivalent, inferior, or clearly superior.·      Explain how surgeon ergonomics, trainee experience, and video-based learning influence practice patterns and learning curves in minimally invasive surgery.·      Discuss the role of cost, reimbursement structures, and market competition (e.g., Medtronic Hugo vs da Vinci) in shaping robotic adoption across different health systems.·      Anticipate how next-generation, task- or organ-specific robotic platforms may further change standards of care in minimally invasive surgery.References:·      Violante T, Ferrari D, Novelli M, Larson DW. The Death of Laparoscopy - Volume 2: A Revised Prognosis. A retrospective study. Ann Surg. 2025 Jun 16. doi: 10.1097/SLA.0000000000006792. Epub ahead of print. PMID: 40518997. https://pubmed.ncbi.nlm.nih.gov/40518997/·      Yu Yoshida, Yoshiro Itatani, Takehito Yamamoto, Ryosuke Okamura, Koya Hida, Kazutaka Obama, Single-incision plus one robot-assisted surgery (SIPORS) using the Hugo robotic-assisted surgery (RAS) system for rectal cancer, Annals of Coloproctology, 10.3393/ac.2025.00787.0112, 41, 6, (586-591), (2025). https://pubmed.ncbi.nlm.nih.gov/41486916/Please visit https://behindtheknife.org to access other high-yield surgical education podcasts, videos and more.  If you liked this episode, check out our recent episodes here: https://behindtheknife.org/listenBehind the Knife Premium:General Surgery Oral Board Review Course: https://behindtheknife.org/premium/general-surgery-oral-board-reviewTrauma Surgery Video Atlas: https://behindtheknife.org/premium/trauma-surgery-video-atlasDominate Surgery: A High-Yield Guide to Your Surgery Clerkship: https://behindtheknife.org/premium/dominate-surgery-a-high-yield-guide-to-your-surgery-clerkshipDominate Surgery for APPs: A High-Yield Guide to Your Surgery Rotation: https://behindtheknife.org/premium/dominate-surgery-for-apps-a-high-yield-guide-to-your-surgery-rotationVascular Surgery Oral Board Review Course: https://behindtheknife.org/premium/vascular-surgery-oral-board-audio-reviewColorectal Surgery Oral Board Review Course: https://behindtheknife.org/premium/colorectal-surgery-oral-board-audio-reviewSurgical Oncology Oral Board Review Course: https://behindtheknife.org/premium/surgical-oncology-oral-board-audio-reviewCardiothoracic Oral Board Review Course: https://behindtheknife.org/premium/cardiothoracic-surgery-oral-board-audio-reviewDownload our App:Apple App Store: https://apps.apple.com/us/app/behind-the-knife/id1672420049Android/Google Play: https://play.google.com/store/apps/details?id=com.btk.app&hl=en_US

A viral video shakes Discovery Mountain. Can the freshmen rebuild what was broken and find their focus again?

Tickets for AIEi Miami and AIE Europe are live, with first wave speakers announced!From pioneering software-defined networking to backing many of the most aggressive AI model companies of this cycle, Martin Casado and Sarah Wang sit at the center of the capital, compute, and talent arms race reshaping the tech industry. As partners at a16z investing across infrastructure and growth, they've watched venture and growth blur, model labs turn dollars into capability at unprecedented speed, and startups raise nine-figure rounds before monetization.Martin and Sarah join us to unpack the new financing playbook for AI: why today's rounds are really compute contracts in disguise, how the “raise → train → ship → raise bigger” flywheel works, and whether foundation model companies can outspend the entire app ecosystem built on top of them. They also share what's underhyped (boring enterprise software), what's overheated (talent wars and compensation spirals), and the two radically different futures they see for AI's market structure.We discuss:* Martin's “two futures” fork: infinite fragmentation and new software categories vs. a small oligopoly of general models that consume everything above them* The capital flywheel: how model labs translate funding directly into capability gains, then into revenue growth measured in weeks, not years* Why venture and growth have merged: $100M–$1B hybrid rounds, strategic investors, compute negotiations, and complex deal structures* The AGI vs. product tension: allocating scarce GPUs between long-term research and near-term revenue flywheels* Whether frontier labs can out-raise and outspend the entire app ecosystem built on top of their APIs* Why today's talent wars ($10M+ comp packages, $B acqui-hires) are breaking early-stage founder math* Cursor as a case study: building up from the app layer while training down into your own models* Why “boring” enterprise software may be the most underinvested opportunity in the AI mania* Hardware and robotics: why the ChatGPT moment hasn't yet arrived for robots and what would need to change* World Labs and generative 3D: bringing the marginal cost of 3D scene creation down by orders of magnitude* Why public AI discourse is often wildly disconnected from boardroom reality and how founders should navigate the noiseShow Notes:* “Where Value Will Accrue in AI: Martin Casado & Sarah Wang” - a16z show* “Jack Altman & Martin Casado on the Future of Venture Capital”* World Labs—Martin Casado• LinkedIn: https://www.linkedin.com/in/martincasado/• X: https://x.com/martin_casadoSarah Wang• LinkedIn: https://www.linkedin.com/in/sarah-wang-59b96a7• X: https://x.com/sarahdingwanga16z• https://a16z.com/Timestamps00:00:00 – Intro: Live from a16z00:01:20 – The New AI Funding Model: Venture + Growth Collide00:03:19 – Circular Funding, Demand & “No Dark GPUs”00:05:24 – Infrastructure vs Apps: The Lines Blur00:06:24 – The Capital Flywheel: Raise → Train → Ship → Raise Bigger00:09:39 – Can Frontier Labs Outspend the Entire App Ecosystem?00:11:24 – Character AI & The AGI vs Product Dilemma00:14:39 – Talent Wars, $10M Engineers & Founder Anxiety00:17:33 – What's Underinvested? The Case for “Boring” Software00:19:29 – Robotics, Hardware & Why It's Hard to Win00:22:42 – Custom ASICs & The $1B Training Run Economics00:24:23 – American Dynamism, Geography & AI Power Centers00:26:48 – How AI Is Changing the Investor Workflow (Claude Cowork)00:29:12 – Two Futures of AI: Infinite Expansion or Oligopoly?00:32:48 – If You Can Raise More Than Your Ecosystem, You Win00:34:27 – Are All Tasks AGI-Complete? Coding as the Test Case00:38:55 – Cursor & The Power of the App Layer00:44:05 – World Labs, Spatial Intelligence & 3D Foundation Models00:47:20 – Thinking Machines, Founder Drama & Media Narratives00:52:30 – Where Long-Term Power Accrues in the AI StackTranscriptLatent.Space - Inside AI's $10B+ Capital Flywheel — Martin Casado & Sarah Wang of a16z[00:00:00] Welcome to Latent Space (Live from a16z) + Meet the Guests[00:00:00] Alessio: Hey everyone. Welcome to the Latent Space podcast, live from a 16 z. Uh, this is Alessio founder Kernel Lance, and I'm joined by Twix, editor of Latent Space.[00:00:08] swyx: Hey, hey, hey. Uh, and we're so glad to be on with you guys. Also a top AI podcast, uh, Martin Cado and Sarah Wang. Welcome, very[00:00:16] Martin Casado: happy to be here and welcome.[00:00:17] swyx: Yes, uh, we love this office. We love what you've done with the place. Uh, the new logo is everywhere now. It's, it's still getting, takes a while to get used to, but it reminds me of like sort of a callback to a more ambitious age, which I think is kind of[00:00:31] Martin Casado: definitely makes a statement.[00:00:33] swyx: Yeah.[00:00:34] Martin Casado: Not quite sure what that statement is, but it makes a statement.[00:00:37] swyx: Uh, Martin, I go back with you to Netlify.[00:00:40] Martin Casado: Yep.[00:00:40] swyx: Uh, and, uh, you know, you create a software defined networking and all, all that stuff people can read up on your background. Yep. Sarah, I'm newer to you. Uh, you, you sort of started working together on AI infrastructure stuff.[00:00:51] Sarah Wang: That's right. Yeah. Seven, seven years ago now.[00:00:53] Martin Casado: Best growth investor in the entire industry.[00:00:55] swyx: Oh, say[00:00:56] Martin Casado: more hands down there is, there is. [00:01:00] I mean, when it comes to AI companies, Sarah, I think has done the most kind of aggressive, um, investment thesis around AI models, right? So, worked for Nom Ja, Mira Ia, FEI Fey, and so just these frontier, kind of like large AI models.[00:01:15] I think, you know, Sarah's been the, the broadest investor. Is that fair?[00:01:20] Venture vs. Growth in the Frontier Model Era[00:01:20] Sarah Wang: No, I, well, I was gonna say, I think it's been a really interesting tag, tag team actually just ‘cause the, a lot of these big C deals, not only are they raising a lot of money, um, it's still a tech founder bet, which obviously is inherently early stage.[00:01:33] But the resources,[00:01:36] Martin Casado: so many, I[00:01:36] Sarah Wang: was gonna say the resources one, they just grow really quickly. But then two, the resources that they need day one are kind of growth scale. So I, the hybrid tag team that we have is. Quite effective, I think,[00:01:46] Martin Casado: what is growth these days? You know, you don't wake up if it's less than a billion or like, it's, it's actually, it's actually very like, like no, it's a very interesting time in investing because like, you know, take like the character around, right?[00:01:59] These tend to [00:02:00] be like pre monetization, but the dollars are large enough that you need to have a larger fund and the analysis. You know, because you've got lots of users. ‘cause this stuff has such high demand requires, you know, more of a number sophistication. And so most of these deals, whether it's US or other firms on these large model companies, are like this hybrid between venture growth.[00:02:18] Sarah Wang: Yeah. Total. And I think, you know, stuff like BD for example, you wouldn't usually need BD when you were seed stage trying to get market biz Devrel. Biz Devrel, exactly. Okay. But like now, sorry, I'm,[00:02:27] swyx: I'm not familiar. What, what, what does biz Devrel mean for a venture fund? Because I know what biz Devrel means for a company.[00:02:31] Sarah Wang: Yeah.[00:02:32] Compute Deals, Strategics, and the ‘Circular Funding' Question[00:02:32] Sarah Wang: You know, so a, a good example is, I mean, we talk about buying compute, but there's a huge negotiation involved there in terms of, okay, do you get equity for the compute? What, what sort of partner are you looking at? Is there a go-to market arm to that? Um, and these are just things on this scale, hundreds of millions, you know, maybe.[00:02:50] Six months into the inception of a company, you just wouldn't have to negotiate these deals before.[00:02:54] Martin Casado: Yeah. These large rounds are very complex now. Like in the past, if you did a series A [00:03:00] or a series B, like whatever, you're writing a 20 to a $60 million check and you call it a day. Now you normally have financial investors and strategic investors, and then the strategic portion always still goes with like these kind of large compute contracts, which can take months to do.[00:03:13] And so it's, it's very different ties. I've been doing this for 10 years. It's the, I've never seen anything like this.[00:03:19] swyx: Yeah. Do you have worries about the circular funding from so disease strategics?[00:03:24] Martin Casado: I mean, listen, as long as the demand is there, like the demand is there. Like the problem with the internet is the demand wasn't there.[00:03:29] swyx: Exactly. All right. This, this is like the, the whole pyramid scheme bubble thing, where like, as long as you mark to market on like the notional value of like, these deals, fine, but like once it starts to chip away, it really Well[00:03:41] Martin Casado: no, like as, as, as, as long as there's demand. I mean, you know, this, this is like a lot of these sound bites have already become kind of cliches, but they're worth saying it.[00:03:47] Right? Like during the internet days, like we were. Um, raising money to put fiber in the ground that wasn't used. And that's a problem, right? Because now you actually have a supply overhang.[00:03:58] swyx: Mm-hmm.[00:03:59] Martin Casado: And even in the, [00:04:00] the time of the, the internet, like the supply and, and bandwidth overhang, even as massive as it was in, as massive as the crash was only lasted about four years.[00:04:09] But we don't have a supply overhang. Like there's no dark GPUs, right? I mean, and so, you know, circular or not, I mean, you know, if, if someone invests in a company that, um. You know, they'll actually use the GPUs. And on the other side of it is the, is the ask for customer. So I I, I think it's a different time.[00:04:25] Sarah Wang: I think the other piece, maybe just to add onto this, and I'm gonna quote Martine in front of him, but this is probably also a unique time in that. For the first time, you can actually trace dollars to outcomes. Yeah, right. Provided that scaling laws are, are holding, um, and capabilities are actually moving forward.[00:04:40] Because if you can put translate dollars into capabilities, uh, a capability improvement, there's demand there to martine's point. But if that somehow breaks, you know, obviously that's an important assumption in this whole thing to make it work. But you know, instead of investing dollars into sales and marketing, you're, you're investing into r and d to get to the capability, um, you know, increase.[00:04:59] And [00:05:00] that's sort of been the demand driver because. Once there's an unlock there, people are willing to pay for it.[00:05:05] Alessio: Yeah.[00:05:06] Blurring Lines: Models as Infra + Apps, and the New Fundraising Flywheel[00:05:06] Alessio: Is there any difference in how you built the portfolio now that some of your growth companies are, like the infrastructure of the early stage companies, like, you know, OpenAI is now the same size as some of the cloud providers were early on.[00:05:16] Like what does that look like? Like how much information can you feed off each other between the, the two?[00:05:24] Martin Casado: There's so many lines that are being crossed right now, or blurred. Right. So we already talked about venture and growth. Another one that's being blurred is between infrastructure and apps, right? So like what is a model company?[00:05:35] Mm-hmm. Like, it's clearly infrastructure, right? Because it's like, you know, it's doing kind of core r and d. It's a horizontal platform, but it's also an app because it's um, uh, touches the users directly. And then of course. You know, the, the, the growth of these is just so high. And so I actually think you're just starting to see a, a, a new financing strategy emerge and, you know, we've had to adapt as a result of that.[00:05:59] And [00:06:00] so there's been a lot of changes. Um, you're right that these companies become platform companies very quickly. You've got ecosystem build out. So none of this is necessarily new, but the timescales of which it's happened is pretty phenomenal. And the way we'd normally cut lines before is blurred a little bit, but.[00:06:16] But that, that, that said, I mean, a lot of it also just does feel like things that we've seen in the past, like cloud build out the internet build out as well.[00:06:24] Sarah Wang: Yeah. Um, yeah, I think it's interesting, uh, I don't know if you guys would agree with this, but it feels like the emerging strategy is, and this builds off of your other question, um.[00:06:33] You raise money for compute, you pour that or you, you pour the money into compute, you get some sort of breakthrough. You funnel the breakthrough into your vertically integrated application. That could be chat GBT, that could be cloud code, you know, whatever it is. You massively gain share and get users.[00:06:49] Maybe you're even subsidizing at that point. Um, depending on your strategy. You raise money at the peak momentum and then you repeat, rinse and repeat. Um, and so. And that wasn't [00:07:00] true even two years ago, I think. Mm-hmm. And so it's sort of to your, just tying it to fundraising strategy, right? There's a, and hiring strategy.[00:07:07] All of these are tied, I think the lines are blurring even more today where everyone is, and they, but of course these companies all have API businesses and so they're these, these frenemy lines that are getting blurred in that a lot of, I mean, they have billions of dollars of API revenue, right? And so there are customers there.[00:07:23] But they're competing on the app layer.[00:07:24] Martin Casado: Yeah. So this is a really, really important point. So I, I would say for sure, venture and growth, that line is blurry app and infrastructure. That line is blurry. Um, but I don't think that that changes our practice so much. But like where the very open questions are like, does this layer in the same way.[00:07:43] Compute traditionally has like during the cloud is like, you know, like whatever, somebody wins one layer, but then another whole set of companies wins another layer. But that might not, might not be the case here. It may be the case that you actually can't verticalize on the token string. Like you can't build an app like it, it necessarily goes down just because there are no [00:08:00] abstractions.[00:08:00] So those are kinda the bigger existential questions we ask. Another thing that is very different this time than in the history of computer sciences is. In the past, if you raised money, then you basically had to wait for engineering to catch up. Which famously doesn't scale like the mythical mammoth. It take a very long time.[00:08:18] But like that's not the case here. Like a model company can raise money and drop a model in a, in a year, and it's better, right? And, and it does it with a team of 20 people or 10 people. So this type of like money entering a company and then producing something that has demand and growth right away and using that to raise more money is a very different capital flywheel than we've ever seen before.[00:08:39] And I think everybody's trying to understand what the consequences are. So I think it's less about like. Big companies and growth and this, and more about these more systemic questions that we actually don't have answers to.[00:08:49] Alessio: Yeah, like at Kernel Labs, one of our ideas is like if you had unlimited money to spend productively to turn tokens into products, like the whole early stage [00:09:00] market is very different because today you're investing X amount of capital to win a deal because of price structure and whatnot, and you're kind of pot committing.[00:09:07] Yeah. To a certain strategy for a certain amount of time. Yeah. But if you could like iteratively spin out companies and products and just throw, I, I wanna spend a million dollar of inference today and get a product out tomorrow.[00:09:18] swyx: Yeah.[00:09:19] Alessio: Like, we should get to the point where like the friction of like token to product is so low that you can do this and then you can change the Right, the early stage venture model to be much more iterative.[00:09:30] And then every round is like either 100 k of inference or like a hundred million from a 16 Z. There's no, there's no like $8 million C round anymore. Right.[00:09:38] When Frontier Labs Outspend the Entire App Ecosystem[00:09:38] Martin Casado: But, but, but, but there's a, there's a, the, an industry structural question that we don't know the answer to, which involves the frontier models, which is, let's take.[00:09:48] Anthropic it. Let's say Anthropic has a state-of-the-art model that has some large percentage of market share. And let's say that, uh, uh, uh, you know, uh, a company's building smaller models [00:10:00] that, you know, use the bigger model in the background, open 4.5, but they add value on top of that. Now, if Anthropic can raise three times more.[00:10:10] Every subsequent round, they probably can raise more money than the entire app ecosystem that's built on top of it. And if that's the case, they can expand beyond everything built on top of it. It's like imagine like a star that's just kind of expanding, so there could be a systemic. There could be a, a systemic situation where the soda models can raise so much money that they can out pay anybody that bills on top of ‘em, which would be something I don't think we've ever seen before just because we were so bottlenecked in engineering, and this is a very open question.[00:10:41] swyx: Yeah. It's, it is almost like bitter lesson applied to the startup industry.[00:10:45] Martin Casado: Yeah, a hundred percent. It literally becomes an issue of like raise capital, turn that directly into growth. Use that to raise three times more. Exactly. And if you can keep doing that, you literally can outspend any company that's built the, not any company.[00:10:57] You can outspend the aggregate of companies on top of [00:11:00] you and therefore you'll necessarily take their share, which is crazy.[00:11:02] swyx: Would you say that kind of happens in character? Is that the, the sort of postmortem on. What happened?[00:11:10] Sarah Wang: Um,[00:11:10] Martin Casado: no.[00:11:12] Sarah Wang: Yeah, because I think so,[00:11:13] swyx: I mean the actual postmortem is, he wanted to go back to Google.[00:11:15] Exactly. But like[00:11:18] Martin Casado: that's another difference that[00:11:19] Sarah Wang: you said[00:11:21] Martin Casado: it. We should talk, we should actually talk about that.[00:11:22] swyx: Yeah,[00:11:22] Sarah Wang: that's[00:11:23] swyx: Go for it. Take it. Take,[00:11:23] Sarah Wang: yeah.[00:11:24] Character.AI, Founder Goals (AGI vs Product), and GPU Allocation Tradeoffs[00:11:24] Sarah Wang: I was gonna say, I think, um. The, the, the character thing raises actually a different issue, which actually the Frontier Labs will face as well. So we'll see how they handle it.[00:11:34] But, um, so we invest in character in January, 2023, which feels like eons ago, I mean, three years ago. Feels like lifetimes ago. But, um, and then they, uh, did the IP licensing deal with Google in August, 2020. Uh, four. And so, um, you know, at the time, no, you know, he's talked publicly about this, right? He wanted to Google wouldn't let him put out products in the world.[00:11:56] That's obviously changed drastically. But, um, he went to go do [00:12:00] that. Um, but he had a product attached. The goal was, I mean, it's Nome Shair, he wanted to get to a GI. That was always his personal goal. But, you know, I think through collecting data, right, and this sort of very human use case, that the character product.[00:12:13] Originally was and still is, um, was one of the vehicles to do that. Um, I think the real reason that, you know. I if you think about the, the stress that any company feels before, um, you ultimately going one way or the other is sort of this a GI versus product. Um, and I think a lot of the big, I think, you know, opening eyes, feeling that, um, anthropic if they haven't started, you know, felt it, certainly given the success of their products, they may start to feel that soon.[00:12:39] And the real. I think there's real trade-offs, right? It's like how many, when you think about GPUs, that's a limited resource. Where do you allocate the GPUs? Is it toward the product? Is it toward new re research? Right? Is it, or long-term research, is it toward, um, n you know, near to midterm research? And so, um, in a case where you're resource constrained, um, [00:13:00] of course there's this fundraising game you can play, right?[00:13:01] But the fund, the market was very different back in 2023 too. Um. I think the best researchers in the world have this dilemma of, okay, I wanna go all in on a GI, but it's the product usage revenue flywheel that keeps the revenue in the house to power all the GPUs to get to a GI. And so it does make, um, you know, I think it sets up an interesting dilemma for any startup that has trouble raising up until that level, right?[00:13:27] And certainly if you don't have that progress, you can't continue this fly, you know, fundraising flywheel.[00:13:32] Martin Casado: I would say that because, ‘cause we're keeping track of all of the things that are different, right? Like, you know, venture growth and uh, app infra and one of the ones is definitely the personalities of the founders.[00:13:45] It's just very different this time I've been. Been doing this for a decade and I've been doing startups for 20 years. And so, um, I mean a lot of people start this to do a GI and we've never had like a unified North star that I recall in the same [00:14:00] way. Like people built companies to start companies in the past.[00:14:02] Like that was what it was. Like I would create an internet company, I would create infrastructure company, like it's kind of more engineering builders and this is kind of a different. You know, mentality. And some companies have harnessed that incredibly well because their direction is so obviously on the path to what somebody would consider a GI, but others have not.[00:14:20] And so like there is always this tension with personnel. And so I think we're seeing more kind of founder movement.[00:14:27] Sarah Wang: Yeah.[00:14:27] Martin Casado: You know, as a fraction of founders than we've ever seen. I mean, maybe since like, I don't know the time of like Shockly and the trade DUR aid or something like that. Way back in the beginning of the industry, I, it's a very, very.[00:14:38] Unusual time of personnel.[00:14:39] Sarah Wang: Totally.[00:14:40] Talent Wars, Mega-Comp, and the Rise of Acquihire M&A[00:14:40] Sarah Wang: And it, I think it's exacerbated by the fact that talent wars, I mean, every industry has talent wars, but not at this magnitude, right? No. Yeah. Very rarely can you see someone get poached for $5 billion. That's hard to compete with. And then secondly, if you're a founder in ai, you could fart and it would be on the front page of, you know, the information these days.[00:14:59] And so there's [00:15:00] sort of this fishbowl effect that I think adds to the deep anxiety that, that these AI founders are feeling.[00:15:06] Martin Casado: Hmm.[00:15:06] swyx: Uh, yes. I mean, just on, uh, briefly comment on the founder, uh, the sort of. Talent wars thing. I feel like 2025 was just like a blip. Like I, I don't know if we'll see that again.[00:15:17] ‘cause meta built the team. Like, I don't know if, I think, I think they're kind of done and like, who's gonna pay more than meta? I, I don't know.[00:15:23] Martin Casado: I, I agree. So it feels so, it feel, it feels this way to me too. It's like, it is like, basically Zuckerberg kind of came out swinging and then now he's kind of back to building.[00:15:30] Yeah,[00:15:31] swyx: yeah. You know, you gotta like pay up to like assemble team to rush the job, whatever. But then now, now you like you, you made your choices and now they got a ship.[00:15:38] Martin Casado: I mean, the, the o other side of that is like, you know, like we're, we're actually in the job hiring market. We've got 600 people here. I hire all the time.[00:15:44] I've got three open recs if anybody's interested, that's listening to this for investor. Yeah, on, on the team, like on the investing side of the team, like, and, um, a lot of the people we talk to have acting, you know, active, um, offers for 10 million a year or something like that. And like, you know, and we pay really, [00:16:00] really well.[00:16:00] And just to see what's out on the market is really, is really remarkable. And so I would just say it's actually, so you're right, like the really flashy one, like I will get someone for, you know, a billion dollars, but like the inflated, um, uh, trickles down. Yeah, it is still very active today. I mean,[00:16:18] Sarah Wang: yeah, you could be an L five and get an offer in the tens of millions.[00:16:22] Okay. Yeah. Easily. Yeah. It's so I think you're right that it felt like a blip. I hope you're right. Um, but I think it's been, the steady state is now, I think got pulled up. Yeah. Yeah. I'll pull up for[00:16:31] Martin Casado: sure. Yeah.[00:16:32] Alessio: Yeah. And I think that's breaking the early stage founder math too. I think before a lot of people would be like, well, maybe I should just go be a founder instead of like getting paid.[00:16:39] Yeah. 800 KA million at Google. But if I'm getting paid. Five, 6 million. That's different but[00:16:45] Martin Casado: on. But on the other hand, there's more strategic money than we've ever seen historically, right? Mm-hmm. And so, yep. The economics, the, the, the, the calculus on the economics is very different in a number of ways. And, uh, it's crazy.[00:16:58] It's cra it's causing like a, [00:17:00] a, a, a ton of change in confusion in the market. Some very positive, sub negative, like, so for example, the other side of the, um. The co-founder, like, um, acquisition, you know, mark Zuckerberg poaching someone for a lot of money is like, we were actually seeing historic amount of m and a for basically acquihires, right?[00:17:20] That you like, you know, really good outcomes from a venture perspective that are effective acquihires, right? So I would say it's probably net positive from the investment standpoint, even though it seems from the headlines to be very disruptive in a negative way.[00:17:33] Alessio: Yeah.[00:17:33] What's Underfunded: Boring Software, Robotics Skepticism, and Custom Silicon Economics[00:17:33] Alessio: Um, let's talk maybe about what's not being invested in, like maybe some interesting ideas that you would see more people build or it, it seems in a way, you know, as ycs getting more popular, it's like access getting more popular.[00:17:47] There's a startup school path that a lot of founders take and they know what's hot in the VC circles and they know what gets funded. Uh, and there's maybe not as much risk appetite for. Things outside of that. Um, I'm curious if you feel [00:18:00] like that's true and what are maybe, uh, some of the areas, uh, that you think are under discussed?[00:18:06] Martin Casado: I mean, I actually think that we've taken our eye off the ball in a lot of like, just traditional, you know, software companies. Um, so like, I mean. You know, I think right now there's almost a barbell, like you're like the hot thing on X, you're deep tech.[00:18:21] swyx: Mm-hmm.[00:18:22] Martin Casado: Right. But I, you know, I feel like there's just kind of a long, you know, list of like good.[00:18:28] Good companies that will be around for a long time in very large markets. Say you're building a database, you know, say you're building, um, you know, kind of monitoring or logging or tooling or whatever. There's some good companies out there right now, but like, they have a really hard time getting, um, the attention of investors.[00:18:43] And it's almost become a meme, right? Which is like, if you're not basically growing from zero to a hundred in a year, you're not interesting, which is just, is the silliest thing to say. I mean, think of yourself as like an introvert person, like, like your personal money, right? Mm-hmm. So. Your personal money, will you put it in the stock market at 7% or you put it in this company growing five x in a very large [00:19:00] market?[00:19:00] Of course you can put it in the company five x. So it's just like we say these stupid things, like if you're not going from zero to a hundred, but like those, like who knows what the margins of those are mean. Clearly these are good investments. True for anybody, right? True. Like our LPs want whatever.[00:19:12] Three x net over, you know, the life cycle of a fund, right? So a, a company in a big market growing five X is a great investment. We'd, everybody would be happy with these returns, but we've got this kind of mania on these, these strong growths. And so I would say that that's probably the most underinvested sector.[00:19:28] Right now.[00:19:29] swyx: Boring software, boring enterprise software.[00:19:31] Martin Casado: Traditional. Really good company.[00:19:33] swyx: No, no AI here.[00:19:34] Martin Casado: No. Like boring. Well, well, the AI of course is pulling them into use cases. Yeah, but that's not what they're, they're not on the token path, right? Yeah. Let's just say that like they're software, but they're not on the token path.[00:19:41] Like these are like they're great investments from any definition except for like random VC on Twitter saying VC on x, saying like, it's not growing fast enough. What do you[00:19:52] Sarah Wang: think? Yeah, maybe I'll answer a slightly different. Question, but adjacent to what you asked, um, which is maybe an area that we're not, uh, investing [00:20:00] right now that I think is a question and we're spending a lot of time in regardless of whether we pull the trigger or not.[00:20:05] Um, and it would probably be on the hardware side, actually. Robotics, right? And the robotics side. Robotics. Right. Which is, it's, I don't wanna say that it's not getting funding ‘cause it's clearly, uh, it's, it's sort of non-consensus to almost not invest in robotics at this point. But, um, we spent a lot of time in that space and I think for us, we just haven't seen the chat GPT moment.[00:20:22] Happen on the hardware side. Um, and the funding going into it feels like it's already. Taking that for granted.[00:20:30] Martin Casado: Yeah. Yeah. But we also went through the drone, you know, um, there's a zip line right, right out there. What's that? Oh yeah, there's a zip line. Yeah. What the drone, what the av And like one of the takeaways is when it comes to hardware, um, most companies will end up verticalizing.[00:20:46] Like if you're. If you're investing in a robot company for an A for agriculture, you're investing in an ag company. ‘cause that's the competition and that's surprising. And that's supply chain. And if you're doing it for mining, that's mining. And so the ad team does a lot of that type of stuff ‘cause they actually set up to [00:21:00] diligence that type of work.[00:21:01] But for like horizontal technology investing, there's very little when it comes to robots just because it's so fit for, for purpose. And so we kinda like to look at software. Solutions or horizontal solutions like applied intuition. Clearly from the AV wave deep map, clearly from the AV wave, I would say scale AI was actually a horizontal one for That's fair, you know, for robotics early on.[00:21:23] And so that sort of thing we're very, very interested. But the actual like robot interacting with the world is probably better for different team. Agree.[00:21:30] Alessio: Yeah, I'm curious who these teams are supposed to be that invest in them. I feel like everybody's like, yeah, robotics, it's important and like people should invest in it.[00:21:38] But then when you look at like the numbers, like the capital requirements early on versus like the moment of, okay, this is actually gonna work. Let's keep investing. That seems really hard to predict in a way that is not,[00:21:49] Martin Casado: I think co, CO two, kla, gc, I mean these are all invested in in Harvard companies. He just, you know, and [00:22:00] listen, I mean, it could work this time for sure.[00:22:01] Right? I mean if Elon's doing it, he's like, right. Just, just the fact that Elon's doing it means that there's gonna be a lot of capital and a lot of attempts for a long period of time. So that alone maybe suggests that we should just be investing in robotics just ‘cause you have this North star who's Elon with a humanoid and that's gonna like basically willing into being an industry.[00:22:17] Um, but we've just historically found like. We're a huge believer that this is gonna happen. We just don't feel like we're in a good position to diligence these things. ‘cause again, robotics companies tend to be vertical. You really have to understand the market they're being sold into. Like that's like that competitive equilibrium with a human being is what's important.[00:22:34] It's not like the core tech and like we're kind of more horizontal core tech type investors. And this is Sarah and I. Yeah, the ad team is different. They can actually do these types of things.[00:22:42] swyx: Uh, just to clarify, AD stands for[00:22:44] Martin Casado: American Dynamism.[00:22:45] swyx: Alright. Okay. Yeah, yeah, yeah. Uh, I actually, I do have a related question that, first of all, I wanna acknowledge also just on the, on the chip side.[00:22:51] Yeah. I, I recall a podcast that where you were on, i, I, I think it was the a CC podcast, uh, about two or three years ago where you, where you suddenly said [00:23:00] something, which really stuck in my head about how at some point, at some point kind of scale it makes sense to. Build a custom aic Yes. For per run.[00:23:07] Martin Casado: Yes.[00:23:07] It's crazy. Yeah.[00:23:09] swyx: We're here and I think you, you estimated 500 billion, uh, something.[00:23:12] Martin Casado: No, no, no. A billion, a billion dollar training run of $1 billion training run. It makes sense to actually do a custom meic if you can do it in time. The question now is timelines. Yeah, but not money because just, just, just rough math.[00:23:22] If it's a billion dollar training. Then the inference for that model has to be over a billion, otherwise it won't be solvent. So let's assume it's, if you could save 20%, which you could save much more than that with an ASIC 20%, that's $200 million. You can tape out a chip for $200 million. Right? So now you can literally like justify economically, not timeline wise.[00:23:41] That's a different issue. An ASIC per model, which[00:23:44] swyx: is because that, that's how much we leave on the table every single time. We, we, we do like generic Nvidia.[00:23:48] Martin Casado: Exactly. Exactly. No, it, it is actually much more than that. You could probably get, you know, a factor of two, which would be 500 million.[00:23:54] swyx: Typical MFU would be like 50.[00:23:55] Yeah, yeah. And that's good.[00:23:57] Martin Casado: Exactly. Yeah. Hundred[00:23:57] swyx: percent. Um, so, so, yeah, and I mean, and I [00:24:00] just wanna acknowledge like, here we are in, in, in 2025 and opening eyes confirming like Broadcom and all the other like custom silicon deals, which is incredible. I, I think that, uh, you know, speaking about ad there's, there's a really like interesting tie in that obviously you guys are hit on, which is like these sort, this sort of like America first movement or like sort of re industrialized here.[00:24:17] Yeah. Uh, move TSMC here, if that's possible. Um, how much overlap is there from ad[00:24:23] Martin Casado: Yeah.[00:24:23] swyx: To, I guess, growth and, uh, investing in particularly like, you know, US AI companies that are strongly bounded by their compute.[00:24:32] Martin Casado: Yeah. Yeah. So I mean, I, I would view, I would view AD as more as a market segmentation than like a mission, right?[00:24:37] So the market segmentation is, it has kind of regulatory compliance issues or government, you know, sale or it deals with like hardware. I mean, they're just set up to, to, to, to, to. To diligence those types of companies. So it's a more of a market segmentation thing. I would say the entire firm. You know, which has been since it is been intercepted, you know, has geographical biases, right?[00:24:58] I mean, for the longest time we're like, you [00:25:00] know, bay Area is gonna be like, great, where the majority of the dollars go. Yeah. And, and listen, there, there's actually a lot of compounding effects for having a geographic bias. Right. You know, everybody's in the same place. You've got an ecosystem, you're there, you've got presence, you've got a network.[00:25:12] Um, and, uh, I mean, I would say the Bay area's very much back. You know, like I, I remember during pre COVID, like it was like almost Crypto had kind of. Pulled startups away. Miami from the Bay Area. Miami, yeah. Yeah. New York was, you know, because it's so close to finance, came up like Los Angeles had a moment ‘cause it was so close to consumer, but now it's kind of come back here.[00:25:29] And so I would say, you know, we tend to be very Bay area focused historically, even though of course we've asked all over the world. And then I would say like, if you take the ring out, you know, one more, it's gonna be the US of course, because we know it very well. And then one more is gonna be getting us and its allies and Yeah.[00:25:44] And it goes from there.[00:25:45] Sarah Wang: Yeah,[00:25:45] Martin Casado: sorry.[00:25:46] Sarah Wang: No, no. I agree. I think from a, but I think from the intern that that's sort of like where the companies are headquartered. Maybe your questions on supply chain and customer base. Uh, I, I would say our customers are, are, our companies are fairly international from that perspective.[00:25:59] Like they're selling [00:26:00] globally, right? They have global supply chains in some cases.[00:26:03] Martin Casado: I would say also the stickiness is very different.[00:26:05] Sarah Wang: Yeah.[00:26:05] Martin Casado: Historically between venture and growth, like there's so much company building in venture, so much so like hiring the next PM. Introducing the customer, like all of that stuff.[00:26:15] Like of course we're just gonna be stronger where we have our network and we've been doing business for 20 years. I've been in the Bay Area for 25 years, so clearly I'm just more effective here than I would be somewhere else. Um, where I think, I think for some of the later stage rounds, the companies don't need that much help.[00:26:30] They're already kind of pretty mature historically, so like they can kind of be everywhere. So there's kind of less of that stickiness. This is different in the AI time. I mean, Sarah is now the, uh, chief of staff of like half the AI companies in, uh, in the Bay Area right now. She's like, ops Ninja Biz, Devrel, BizOps.[00:26:48] swyx: Are, are you, are you finding much AI automation in your work? Like what, what is your stack.[00:26:53] Sarah Wang: Oh my, in my personal stack.[00:26:54] swyx: I mean, because like, uh, by the way, it's the, the, the reason for this is it is triggering, uh, yeah. We, like, I'm hiring [00:27:00] ops, ops people. Um, a lot of ponders I know are also hiring ops people and I'm just, you know, it's opportunity Since you're, you're also like basically helping out with ops with a lot of companies.[00:27:09] What are people doing these days? Because it's still very manual as far as I can tell.[00:27:13] Sarah Wang: Hmm. Yeah. I think the things that we help with are pretty network based, um, in that. It's sort of like, Hey, how do do I shortcut this process? Well, let's connect you to the right person. So there's not quite an AI workflow for that.[00:27:26] I will say as a growth investor, Claude Cowork is pretty interesting. Yeah. Like for the first time, you can actually get one shot data analysis. Right. Which, you know, if you're gonna do a customer database, analyze a cohort retention, right? That's just stuff that you had to do by hand before. And our team, the other, it was like midnight and the three of us were playing with Claude Cowork.[00:27:47] We gave it a raw file. Boom. Perfectly accurate. We checked the numbers. It was amazing. That was my like, aha moment. That sounds so boring. But you know, that's, that's the kind of thing that a growth investor is like, [00:28:00] you know, slaving away on late at night. Um, done in a few seconds.[00:28:03] swyx: Yeah. You gotta wonder what the whole, like, philanthropic labs, which is like their new sort of products studio.[00:28:10] Yeah. What would that be worth as an independent, uh, startup? You know, like a[00:28:14] Martin Casado: lot.[00:28:14] Sarah Wang: Yeah, true.[00:28:16] swyx: Yeah. You[00:28:16] Martin Casado: gotta hand it to them. They've been executing incredibly well.[00:28:19] swyx: Yeah. I, I mean, to me, like, you know, philanthropic, like building on cloud code, I think, uh, it makes sense to me the, the real. Um, pedal to the metal, whatever the, the, the phrase is, is when they start coming after consumer with, uh, against OpenAI and like that is like red alert at Open ai.[00:28:35] Oh, I[00:28:35] Martin Casado: think they've been pretty clear. They're enterprise focused.[00:28:37] swyx: They have been, but like they've been free. Here's[00:28:40] Martin Casado: care publicly,[00:28:40] swyx: it's enterprise focused. It's coding. Right. Yeah.[00:28:43] AI Labs vs Startups: Disruption, Undercutting & the Innovator's Dilemma[00:28:43] swyx: And then, and, but here's cloud, cloud, cowork, and, and here's like, well, we, uh, they, apparently they're running Instagram ads for Claudia.[00:28:50] I, on, you know, for, for people on, I get them all the time. Right. And so, like,[00:28:54] Martin Casado: uh,[00:28:54] swyx: it, it's kind of like this, the disruption thing of, uh, you know. Mo Open has been doing, [00:29:00] consumer been doing the, just pursuing general intelligence in every mo modality, and here's a topic that only focus on this thing, but now they're sort of undercutting and doing the whole innovator's dilemma thing on like everything else.[00:29:11] Martin Casado: It's very[00:29:11] swyx: interesting.[00:29:12] Martin Casado: Yeah, I mean there's, there's a very open que so for me there's like, do you know that meme where there's like the guy in the path and there's like a path this way? There's a path this way. Like one which way Western man. Yeah. Yeah.[00:29:23] Two Futures for AI: Infinite Market vs AGI Oligopoly[00:29:23] Martin Casado: And for me, like, like all the entire industry kind of like hinges on like two potential futures.[00:29:29] So in, in one potential future, um, the market is infinitely large. There's perverse economies of scale. ‘cause as soon as you put a model out there, like it kind of sublimates and all the other models catch up and like, it's just like software's being rewritten and fractured all over the place and there's tons of upside and it just grows.[00:29:48] And then there's another path which is like, well. Maybe these models actually generalize really well, and all you have to do is train them with three times more money. That's all you have to [00:30:00] do, and it'll just consume everything beyond it. And if that's the case, like you end up with basically an oligopoly for everything, like, you know mm-hmm.[00:30:06] Because they're perfectly general and like, so this would be like the, the a GI path would be like, these are perfectly general. They can do everything. And this one is like, this is actually normal software. The universe is complicated. You've got, and nobody knows the answer.[00:30:18] The Economics Reality Check: Gross Margins, Training Costs & Borrowing Against the Future[00:30:18] Martin Casado: My belief is if you actually look at the numbers of these companies, so generally if you look at the numbers of these companies, if you look at like the amount they're making and how much they, they spent training the last model, they're gross margin positive.[00:30:30] You're like, oh, that's really working. But if you look at like. The current training that they're doing for the next model, their gross margin negative. So part of me thinks that a lot of ‘em are kind of borrowing against the future and that's gonna have to slow down. It's gonna catch up to them at some point in time, but we don't really know.[00:30:47] Sarah Wang: Yeah.[00:30:47] Martin Casado: Does that make sense? Like, I mean, it could be, it could be the case that the only reason this is working is ‘cause they can raise that next round and they can train that next model. ‘cause these models have such a short. Life. And so at some point in time, like, you know, they won't be able to [00:31:00] raise that next round for the next model and then things will kind of converge and fragment again.[00:31:03] But right now it's not.[00:31:04] Sarah Wang: Totally. I think the other, by the way, just, um, a meta point. I think the other lesson from the last three years is, and we talk about this all the time ‘cause we're on this. Twitter X bubble. Um, cool. But, you know, if you go back to, let's say March, 2024, that period, it felt like a, I think an open source model with an, like a, you know, benchmark leading capability was sort of launching on a daily basis at that point.[00:31:27] And, um, and so that, you know, that's one period. Suddenly it's sort of like open source takes over the world. There's gonna be a plethora. It's not an oligopoly, you know, if you fast, you know, if you, if you rewind time even before that GPT-4 was number one for. Nine months, 10 months. It's a long time. Right.[00:31:44] Um, and of course now we're in this era where it feels like an oligopoly, um, maybe some very steady state shifts and, and you know, it could look like this in the future too, but it just, it's so hard to call. And I think the thing that keeps, you know, us up at [00:32:00] night in, in a good way and bad way, is that the capability progress is actually not slowing down.[00:32:06] And so until that happens, right, like you don't know what's gonna look like.[00:32:09] Martin Casado: But I, I would, I would say for sure it's not converged, like for sure, like the systemic capital flows have not converged, meaning right now it's still borrowing against the future to subsidize growth currently, which you can do that for a period of time.[00:32:23] But, but you know, at the end, at some point the market will rationalize that and just nobody knows what that will look like.[00:32:29] Alessio: Yeah.[00:32:29] Martin Casado: Or, or like the drop in price of compute will, will, will save them. Who knows?[00:32:34] Alessio: Yeah. Yeah. I think the models need to ask them to, to specific tasks. You know? It's like, okay, now Opus 4.5 might be a GI at some specific task, and now you can like depreciate the model over a longer time.[00:32:45] I think now, now, right now there's like no old model.[00:32:47] Martin Casado: No, but let, but lemme just change that mental, that's, that used to be my mental model. Lemme just change it a little bit.[00:32:53] Capital as a Weapon vs Task Saturation: Where Real Enterprise Value Gets Built[00:32:53] Martin Casado: If you can raise three times, if you can raise more than the aggregate of anybody that uses your models, that doesn't even matter.[00:32:59] It doesn't [00:33:00] even matter. See what I'm saying? Like, yeah. Yeah. So, so I have an API Business. My API business is 60% margin, or 70% margin, or 80% margin is a high margin business. So I know what everybody is using. If I can raise more money than the aggregate of everybody that's using it, I will consume them whether I'm a GI or not.[00:33:14] And I will know if they're using it ‘cause they're using it. And like, unlike in the past where engineering stops me from doing that.[00:33:21] Alessio: Mm-hmm.[00:33:21] Martin Casado: It is very straightforward. You just train. So I also thought it was kind of like, you must ask the code a GI, general, general, general. But I think there's also just a possibility that the, that the capital markets will just give them the, the, the ammunition to just go after everybody on top of ‘em.[00:33:36] Sarah Wang: I, I do wonder though, to your point, um, if there's a certain task that. Getting marginally better isn't actually that much better. Like we've asked them to it, to, you know, we can call it a GI or whatever, you know, actually, Ali Goi talks about this, like we're already at a GI for a lot of functions in the enterprise.[00:33:50] Um. That's probably those for those tasks, you probably could build very specific companies that focus on just getting as much value out of that task that isn't [00:34:00] coming from the model itself. There's probably a rich enterprise business to be built there. I mean, could be wrong on that, but there's a lot of interesting examples.[00:34:08] So, right, if you're looking the legal profession or, or whatnot, and maybe that's not a great one ‘cause the models are getting better on that front too, but just something where it's a bit saturated, then the value comes from. Services. It comes from implementation, right? It comes from all these things that actually make it useful to the end customer.[00:34:24] Martin Casado: Sorry, what am I, one more thing I think is, is underused in all of this is like, to what extent every task is a GI complete.[00:34:31] Sarah Wang: Mm-hmm.[00:34:32] Martin Casado: Yeah. I code every day. It's so fun.[00:34:35] Sarah Wang: That's a core question. Yeah.[00:34:36] Martin Casado: And like. When I'm talking to these models, it's not just code. I mean, it's everything, right? Like I, you know, like it's,[00:34:43] swyx: it's healthcare.[00:34:44] It's,[00:34:44] Martin Casado: I mean, it's[00:34:44] swyx: Mele,[00:34:45] Martin Casado: but it's every, it is exactly that. Like, yeah, that's[00:34:47] Sarah Wang: great support. Yeah.[00:34:48] Martin Casado: It's everything. Like I'm asking these models to, yeah, to understand compliance. I'm asking these models to go search the web. I'm asking these models to talk about things I know in the history, like it's having a full conversation with me while I, I engineer, and so it could be [00:35:00] the case that like, mm-hmm.[00:35:01] The most a, you know, a GI complete, like I'm not an a GI guy. Like I think that's, you know, but like the most a GI complete model will is win independent of the task. And we don't know the answer to that one either.[00:35:11] swyx: Yeah.[00:35:12] Martin Casado: But it seems to me that like, listen, codex in my experience is for sure better than Opus 4.5 for coding.[00:35:18] Like it finds the hardest bugs that I work in with. Like, it is, you know. The smartest developers. I don't work on it. It's great. Um, but I think Opus 4.5 is actually very, it's got a great bedside manner and it really, and it, it really matters if you're building something very complex because like, it really, you know, like you're, you're, you're a partner and a brainstorming partner for somebody.[00:35:38] And I think we don't discuss enough how every task kind of has that quality.[00:35:42] swyx: Mm-hmm.[00:35:43] Martin Casado: And what does that mean to like capital investment and like frontier models and Submodels? Yeah.[00:35:47] Why “Coding Models” Keep Collapsing into Generalists (Reasoning vs Taste)[00:35:47] Martin Casado: Like what happened to all the special coding models? Like, none of ‘em worked right. So[00:35:51] Alessio: some of them, they didn't even get released.[00:35:53] Magical[00:35:54] Martin Casado: Devrel. There's a whole, there's a whole host. We saw a bunch of them and like there's this whole theory that like, there could be, and [00:36:00] I think one of the conclusions is, is like there's no such thing as a coding model,[00:36:04] Alessio: you know?[00:36:04] Martin Casado: Like, that's not a thing. Like you're talking to another human being and it's, it's good at coding, but like it's gotta be good at everything.[00:36:10] swyx: Uh, minor disagree only because I, I'm pretty like, have pretty high confidence that basically open eye will always release a GPT five and a GT five codex. Like that's the code's. Yeah. The way I call it is one for raisin, one for Tiz. Um, and, and then like someone internal open, it was like, yeah, that's a good way to frame it.[00:36:32] Martin Casado: That's so funny.[00:36:33] swyx: Uh, but maybe it, maybe it collapses down to reason and that's it. It's not like a hundred dimensions doesn't life. Yeah. It's two dimensions. Yeah, yeah, yeah, yeah. Like and exactly. Beside manner versus coding. Yeah.[00:36:43] Martin Casado: Yeah.[00:36:44] swyx: It's, yeah.[00:36:46] Martin Casado: I, I think for, for any, it's hilarious. For any, for anybody listening to this for, for, for, I mean, for you, like when, when you're like coding or using these models for something like that.[00:36:52] Like actually just like be aware of how much of the interaction has nothing to do with coding and it just turns out to be a large portion of it. And so like, you're, I [00:37:00] think like, like the best Soto ish model. You know, it is going to remain very important no matter what the task is.[00:37:06] swyx: Yeah.[00:37:07] What He's Actually Coding: Gaussian Splats, Spark.js & 3D Scene Rendering Demos[00:37:07] swyx: Uh, speaking of coding, uh, I, I'm gonna be cheeky and ask like, what actually are you coding?[00:37:11] Because obviously you, you could code anything and you are obviously a busy investor and a manager of the good. Giant team. Um, what are you calling?[00:37:18] Martin Casado: I help, um, uh, FEFA at World Labs. Uh, it's one of the investments and um, and they're building a foundation model that creates 3D scenes.[00:37:27] swyx: Yeah, we had it on the pod.[00:37:28] Yeah. Yeah,[00:37:28] Martin Casado: yeah. And so these 3D scenes are Gaussian splats, just by the way that kind of AI works. And so like, you can reconstruct a scene better with, with, with radiance feels than with meshes. ‘cause like they don't really have topology. So, so they, they, they produce each. Beautiful, you know, 3D rendered scenes that are Gaussian splats, but the actual industry support for Gaussian splats isn't great.[00:37:50] It's just never, you know, it's always been meshes and like, things like unreal use meshes. And so I work on a open source library called Spark js, which is a. Uh, [00:38:00] a JavaScript rendering layer ready for Gaussian splats. And it's just because, you know, um, you, you, you need that support and, and right now there's kind of a three js moment that's all meshes and so like, it's become kind of the default in three Js ecosystem.[00:38:13] As part of that to kind of exercise the library, I just build a whole bunch of cool demos. So if you see me on X, you see like all my demos and all the world building, but all of that is just to exercise this, this library that I work on. ‘cause it's actually a very tough algorithmics problem to actually scale a library that much.[00:38:29] And just so you know, this is ancient history now, but 30 years ago I paid for undergrad, you know, working on game engines in college in the late nineties. So I've got actually a back and it's very old background, but I actually have a background in this and so a lot of it's fun. You know, but, but the, the, the, the whole goal is just for this rendering library to, to,[00:38:47] Sarah Wang: are you one of the most active contributors?[00:38:49] The, their GitHub[00:38:50] Martin Casado: spark? Yes.[00:38:51] Sarah Wang: Yeah, yeah.[00:38:51] Martin Casado: There's only two of us there, so, yes. No, so by the way, so the, the pri The pri, yeah. Yeah. So the primary developer is a [00:39:00] guy named Andres Quist, who's an absolute genius. He and I did our, our PhDs together. And so like, um, we studied for constant Quas together. It was almost like hanging out with an old friend, you know?[00:39:09] And so like. So he, he's the core, core guy. I did mostly kind of, you know, the side I run venture fund.[00:39:14] swyx: It's amazing. Like five years ago you would not have done any of this. And it brought you back[00:39:19] Martin Casado: the act, the Activ energy, you're still back. Energy was so high because you had to learn all the framework b******t.[00:39:23] Man, I f*****g used to hate that. And so like, now I don't have to deal with that. I can like focus on the algorithmics so I can focus on the scaling and I,[00:39:29] swyx: yeah. Yeah.[00:39:29] LLMs vs Spatial Intelligence + How to Value World Labs' 3D Foundation Model[00:39:29] swyx: And then, uh, I'll observe one irony and then I'll ask a serious investor question, uh, which is like, the irony is FFE actually doesn't believe that LMS can lead us to spatial intelligence.[00:39:37] And here you are using LMS to like help like achieve spatial intelligence. I just see, I see some like disconnect in there.[00:39:45] Martin Casado: Yeah. Yeah. So I think, I think, you know, I think, I think what she would say is LLMs are great to help with coding.[00:39:51] swyx: Yes.[00:39:51] Martin Casado: But like, that's very different than a model that actually like provides, they, they'll never have the[00:39:56] swyx: spatial inte[00:39:56] Martin Casado: issues.[00:39:56] And listen, our brains clearly listen, our brains, brains clearly have [00:40:00] both our, our brains clearly have a language reasoning section and they clearly have a spatial reasoning section. I mean, it's just, you know, these are two pretty independent problems.[00:40:07] swyx: Okay. And you, you, like, I, I would say that the, the one data point I recently had, uh, against it is the DeepMind, uh, IMO Gold, where, so, uh, typically the, the typical answer is that this is where you start going down the neuros symbolic path, right?[00:40:21] Like one, uh, sort of very sort of abstract reasoning thing and one form, formal thing. Um, and that's what. DeepMind had in 2024 with alpha proof, alpha geometry, and now they just use deep think and just extended thinking tokens. And it's one model and it's, and it's in LM.[00:40:36] Martin Casado: Yeah, yeah, yeah, yeah, yeah.[00:40:37] swyx: And so that, that was my indication of like, maybe you don't need a separate system.[00:40:42] Martin Casado: Yeah. So, so let me step back. I mean, at the end of the day, at the end of the day, these things are like nodes in a graph with weights on them. Right. You know, like it can be modeled like if you, if you distill it down. But let me just talk about the two different substrates. Let's, let me put you in a dark room.[00:40:56] Like totally black room. And then let me just [00:41:00] describe how you exit it. Like to your left, there's a table like duck below this thing, right? I mean like the chances that you're gonna like not run into something are very low. Now let me like turn on the light and you actually see, and you can do distance and you know how far something away is and like where it is or whatever.[00:41:17] Then you can do it, right? Like language is not the right primitives to describe. The universe because it's not exact enough. So that's all Faye, Faye is talking about. When it comes to like spatial reasoning, it's like you actually have to know that this is three feet far, like that far away. It is curved.[00:41:37] You have to understand, you know, the, like the actual movement through space.[00:41:40] swyx: Yeah.[00:41:40] Martin Casado: So I do, I listen, I do think at the end of these models are definitely converging as far as models, but there's, there's, there's different representations of problems you're solving. One is language. Which, you know, that would be like describing to somebody like what to do.[00:41:51] And the other one is actually just showing them and the space reasoning is just showing them.[00:41:55] swyx: Yeah, yeah, yeah. Right. Got it, got it. Uh, the, in the investor question was on, on, well labs [00:42:00] is, well, like, how do I value something like this? What, what, what work does the, do you do? I'm just like, Fefe is awesome.[00:42:07] Justin's awesome. And you know, the other two co-founder, co-founders, but like the, the, the tech, everyone's building cool tech. But like, what's the value of the tech? And this is the fundamental question[00:42:16] Martin Casado: of, well, let, let, just like these, let me just maybe give you a rough sketch on the diffusion models. I actually love to hear Sarah because I'm a venture for, you know, so like, ventures always, always like kind of wild west type[00:42:24] swyx: stuff.[00:42:24] You, you, you, you paid a dream and she has to like, actually[00:42:28] Martin Casado: I'm gonna say I'm gonna mar to reality, so I'm gonna say the venture for you. And she can be like, okay, you a little kid. Yeah. So like, so, so these diffusion models literally. Create something for, for almost nothing. And something that the, the world has found to be very valuable in the past, in our real markets, right?[00:42:45] Like, like a 2D image. I mean, that's been an entire market. People value them. It takes a human being a long time to create it, right? I mean, to create a, you know, a, to turn me into a whatever, like an image would cost a hundred bucks in an hour. The inference cost [00:43:00] us a hundredth of a penny, right? So we've seen this with speech in very successful companies.[00:43:03] We've seen this with 2D image. We've seen this with movies. Right? Now, think about 3D scene. I mean, I mean, when's Grand Theft Auto coming out? It's been six, what? It's been 10 years. I mean, how, how like, but hasn't been 10 years.[00:43:14] Alessio: Yeah.[00:43:15] Martin Casado: How much would it cost to like, to reproduce this room in 3D? Right. If you, if you, if you hired somebody on fiber, like in, in any sort of quality, probably 4,000 to $10,000.[00:43:24] And then if you had a professional, probably $30,000. So if you could generate the exact same thing from a 2D image, and we know that these are used and they're using Unreal and they're using Blend, or they're using movies and they're using video games and they're using all. So if you could do that for.[00:43:36] You know, less than a dollar, that's four or five orders of magnitude cheaper. So you're bringing the marginal cost of something that's useful down by three orders of magnitude, which historically have created very large companies. So that would be like the venture kind of strategic dreaming map.[00:43:49] swyx: Yeah.[00:43:50] And, and for listeners, uh, you can do this yourself on your, on your own phone with like. Uh, the marble.[00:43:55] Martin Casado: Yeah. Marble.[00:43:55] swyx: Uh, or but also there's many Nerf apps where you just go on your iPhone and, and do this.[00:43:59] Martin Casado: Yeah. Yeah. [00:44:00] Yeah. And, and in the case of marble though, it would, what you do is you literally give it in.[00:44:03] So most Nerf apps you like kind of run around and take a whole bunch of pictures and then you kind of reconstruct it.[00:44:08] swyx: Yeah.[00:44:08] Martin Casado: Um, things like marble, just that the whole generative 3D space will just take a 2D image and it'll reconstruct all the like, like[00:44:16] swyx: meaning it has to fill in. Uh,[00:44:18] Martin Casado: stuff at the back of the table, under the table, the back, like, like the images, it doesn't see.[00:44:22] So the generator stuff is very different than reconstruction that it fills in the things that you can't see.[00:44:26] swyx: Yeah. Okay.[00:44:26] Sarah Wang: So,[00:44:27] Martin Casado: all right. So now the,[00:44:28] Sarah Wang: no, no. I mean I love that[00:44:29] Martin Casado: the adult[00:44:29] Sarah Wang: perspective. Um, well, no, I was gonna say these are very much a tag team. So we, we started this pod with that, um, premise. And I think this is a perfect question to even build on that further.[00:44:36] ‘cause it truly is, I mean, we're tag teaming all of these together.[00:44:39] Investing in Model Labs, Media Rumors, and the Cursor Playbook (Margins & Going Down-Stack)[00:44:39] Sarah Wang: Um, but I think every investment fundamentally starts with the same. Maybe the same two premises. One is, at this point in time, we actually believe that there are. And of one founders for their particular craft, and they have to be demonstrated in their prior careers, right?[00:44:56] So, uh, we're not investing in every, you know, now the term is NEO [00:45:00] lab, but every foundation model, uh, any, any company, any founder trying to build a foundation model, we're not, um, contrary to popular opinion, we're

In this episode, Shaleen Vira, MD, MBA, Chief of Orthopaedic Spine Surgery at Banner University Medical Center and author of Spine and Strategy, discusses scaling spine services in a high growth market while taking a disciplined approach to robotics and AI adoption. He shares why resilience, surgeon skill preservation, and thoughtful capital allocation matter more than hype when evaluating new technology.

In this episode, Shaleen Vira, MD, MBA, Chief of Orthopaedic Spine Surgery at Banner University Medical Center and author of Spine and Strategy, discusses scaling spine services in a high growth market while taking a disciplined approach to robotics and AI adoption. He shares why resilience, surgeon skill preservation, and thoughtful capital allocation matter more than hype when evaluating new technology.

Trener Robotics is solving a fundamental problem in industrial automation: the 5 million robotic arms deployed globally operate without intelligence, relying on 60-year-old procedural programming methods. With $38 Million in total funding—including a just-closed $32 Million Series A—the company compressed an 18-month journey from pre-seed to Series A by focusing ruthlessly on CNC machine tending. In this episode of Category Visionaries, I sat down with Asad Tirmizi, Founder of Trener Robotics, to unpack how 14 years of research in robotics and AI converged with market timing to create what judges recognized as this year's biggest innovation in machining—despite the founding team having zero machining expertise. Topics Discussed: Why Trener Robotics chose CNC machine tending over higher-visibility applications like airplane cleaning The capital efficiency trade-offs between sales cycle length, development complexity, and runway Partnering with three of the five largest robot OEMs controlling 4.3 million of 5 million deployed units Expanding to six countries (Norway, Denmark, Sweden, Portugal, Spain, US) through integrator networks Converting technical curiosity into closed deals in a risk-averse industry with 60-year-old workflows Building training materials in Portuguese for markets the founding team has never visited GTM Lessons For B2B Founders: Sales cycle length determines survival, not TAM size: Trener Robotics rejected compelling applications with massive TAM like airplane cleaning because sales cycles would burn through runway before reaching scale. Asad was explicit: "If your sales cycle is too long, your funding is too less and your development time is too much, that's it, you're out of business." They chose CNC machine tending specifically because manufacturers already budget for robots, understand ROI calculations, and have existing vendor relationships. Calculate your actual time-to-close from first meeting to signed contract, multiply by customer acquisition cost, and build your runway model around that reality—not the TAM slide in your deck. Niche dominance beats horizontal expansion every time: Despite having technology capable of 100+ applications, Trener Robotics committed to machine tending exclusively. Asad's framework: "Making 100 skills is easy. Distributing 100 skills, maintaining 100 skills, marketing hundred skills—that's where most startups break when scaling, not when incubating." The constraint forced them to become the definitive solution for one workflow, enabling repeatable sales motions and concentrated marketing spend. Most founders intellectually agree with focus but fail operationally—they take revenue from adjacent use cases "just this once." Don't. Pick your beachhead, win it completely, then use that cash cow to fund expansion. Industry awards are underutilized credibility hacks: Trener Robotics won the Machine Tool Innovation Award—the machining industry's most prestigious recognition—despite being roboticists with no machining background. This wasn't luck. They studied what innovations historically won, trained their models on data that would produce award-worthy results, and positioned the submission around industry pain points. The award opened OEM partnership conversations that would have taken years otherwise. Identify the 2-3 awards that matter in your category, reverse-engineer what wins, and build your product roadmap accordingly. Third-party validation converts skeptical enterprise buyers faster than any sales deck. Channel partner economics need structural win-win design: Trener Robotics secured partnerships with three of the five largest robot OEMs (controlling 86% of deployed units globally) by solving a specific problem: OEMs sell hardware but lose recurring revenue to system integrators who program robots. Trener Robotics' AI models let OEMs capture software subscription revenue while reducing integrator programming costs. Asad acknowledged they're still learning: "I would not by any stretch of imagination say we have proven how good we are in managing channel partners. It's a journey we are on." But the structural economics work because both sides make more money. When designing channel programs, don't just offer margin points—restructure the value chain so partners access new revenue pools they couldn't capture before. Interest signals are worthless without conversion timeline mapping: Asad's painful admission: "Interest does not mean sales. Pilots do not mean sales. Even letter of interest or contracts to test your equipment does not mean sales." As a technical founder, he initially conflated technical validation with buying intent. The fix: obsessively measure time between interest signal and closed deal, then segment by customer type, deal size, and decision-maker level. Only after mapping this could they accurately forecast and avoid the "too much time in the gray area of interest turning to sales" trap. Build a conversion funnel that tracks days-in-stage, not just stage progression percentages. // Sponsors: Front Lines — We help B2B tech companies launch, manage, and grow podcasts that drive demand, awareness, and thought leadership. www.FrontLines.io The Global Talent Co. — We help tech startups find, vet, hire, pay, and retain amazing marketing talent that costs 50-70% less than the US & Europe. www.GlobalTalent.co // Don't Miss: New Podcast Series — How I Hire Senior GTM leaders share the tactical hiring frameworks they use to build winning revenue teams. Hosted by Andy Mowat, who scaled 4 unicorns from $10M to $100M+ ARR and launched Whispered to help executives find their next role. Subscribe here: https://open.spotify.com/show/53yCHlPfLSMFimtv0riPyM

Amazon said Blue Jay's core tech will be used for other robotics projects and the employees who worked on it were moved to other projects. Also, Mastodon is looking to grow its open source, decentralized social network with new features aimed at creators. Learn more about your ad choices. Visit podcastchoices.com/adchoices

Don't be surprised if you find yourself needing head and neck cancer surgery and your surgeon has an unexpected assistant: a robot. But it won't be R2D2 or C3P0 from Star Wars. Instead, the modern medical robot is a high-tech tool that surgeons use to perform sensitive tasks in hard-to-reach places. In this episode, Dr. Vivian Wu, a head and neck surgeon at Pacific Neuroscience Institute, takes us into the new world of robotic surgeries. Dr. Wu also tells us about some new tests that can show how successful someone's cancer treatment has been, without the need for invasive biopsies. She talks about how to prevent and treat Human Papillomavirus, or HPV, which has started showing up in people's mouths in greater numbers. Dr. Wu also emphasizes the important role a care “village” plays in a patient's treatment and recovery.

This week Shawn Tierney meets up with Soroush Karimzadeh of Novarc to discuss their AI-Powered Autonomous Welding Robotics in this episode of #TheAutomationPodcast. For any links related to this episode, check out the “Show Notes” located below the video. Watch The Automation Podcast from The Automation Blog: Listen to The Automation Podcast from The Automation Blog: The Automation Podcast, Episode 262 Show Notes: Special thanks goes out to Soroush Karimzadeh for coming on the show, and to Novarc for sponsoring this episode. To learn more about their AI-Powered Autonomous Robotic Welding solution, see the below links: Soroush Karimzadeh, LinkedIn, CEO & CoFounder, Novarc Technologies Inc.: https://www.linkedin.com/in/soroushkarimzadeh Novarc Technologies, LinkedIn: https://www.linkedin.com/company/novarc-technologies-inc- Novarc Technologies Website: https://www.novarctech.com/ NovAI™ – Adaptive Welding: The full power of AI and machine vision in welding automation: https://www.novarctech.com/products/novai/ Spool Welding Robot (SWR™): https://www.novarctech.com/products/spool-welding-robot/ Until next time, Peace ✌️ If you enjoyed this content, please give it a Like, and consider Sharing a link to it as that is the best way for us to grow our audience, which in turn allows us to produce more content

Explore the Zero Project tech conference in Vienna, discover Speaky AI's smart screen reader and innovative accessibility tools, and hear insights on Apple's upcoming 4 March event and the future of affordable MacBooks.Steven Scott and Shaun Preece bring a lively look at the world of assistive tech from Vienna. Steven shares his travel misadventures, from oversized hiking boots to airport wheelchair journeys, and dives into the incredible accessibility innovations showcased at the Zero Project Conference.Highlights include Speaky AI, a next-gen conversational screen reader that works with inaccessible apps, and ambitious plans for smart glasses and even a robotic guide dog. The duo also discuss Apple's newly announced 4 March event, rumours of a low-cost, colourful MacBook, and the ongoing challenge of making VoiceOver and accessible tech widely understood.Relevant LinksZero Project Conference: https://zeroproject.org Find Double Tap online: YouTube, Double Tap Website---Follow on:YouTube: https://www.doubletaponair.com/youtubeX (formerly Twitter): https://www.doubletaponair.com/xInstagram: https://www.doubletaponair.com/instagramTikTok: https://www.doubletaponair.com/tiktokThreads: https://www.doubletaponair.com/threadsFacebook: https://www.doubletaponair.com/facebookLinkedIn: https://www.doubletaponair.com/linkedin Subscribe to the Podcast:Apple: https://www.doubletaponair.com/appleSpotify: https://www.doubletaponair.com/spotifyRSS: https://www.doubletaponair.com/podcastiHeadRadio: https://www.doubletaponair.com/iheart About Double TapHosted by the insightful duo, Steven Scott and Shaun Preece, Double Tap is a treasure trove of information for anyone who's blind or partially sighted and has a passion for tech. Steven and Shaun not only demystify tech, but they also regularly feature interviews and welcome guests from the community, fostering an interactive and engaging environment. Tune in every day of the week, and you'll discover how technology can seamlessly integrate into your life, enhancing daily tasks and experiences, even if your sight is limited. "Double Tap" is a registered trademark of Double Tap Productions Inc. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

This week we bounce from haunted literary labyrinths and gonzo chaos in Real Life, into falling space junk, AI hype experiments, and surprisingly clever cows in Future or Now — before wrapping up with Isaac Asimov's Liar! and a discussion about robot ethics, emotional harm, and the danger of well-intentioned lies.   Real Life Steven is deep into House of Leaves, and yeah — "trip" is the correct word. The book continues to be less of a story and more of a psychological maze that actively messes with your sense of reality while you read it. Not a casual bedtime book. More like a "stare at the page and question existence" book. Meanwhile, Ben is reading Fear and Loathing in Las Vegas, courtesy of Mom, which is a wildly different flavor of chaos. Where Steven is lost in haunted architecture and footnotes, Ben is cruising through drug-fueled journalism and American absurdity. Balanced intellectual diets all around. Devon, however, is reading… nothing. Which raises several questions. Is he okay? Is he plotting? Has he transcended books? We don't know. We're monitoring the situation. Ben also brought genuine excitement to the table with the upcoming Star Trek: Voyager – Across the Unknown. It's got the theme song. That alone earns emotional bonus points. The real curiosity, though, is whether it leans into branching narrative choices like a Mass Effect-style experience. If it does, that opens up a ton of potential for alternate Voyager storylines, which is basically catnip for any Trek fan.   Future or Now Steven covered a genuinely clever scientific development: researchers are now using earthquake sensors to detect falling space junk. Instead of building entirely new tracking systems, they're piggybacking on instruments already listening to the Earth's vibrations. When debris screams through the atmosphere and creates sonic booms, those sensors can track its path, breakup, and potential impact zones. It's one of those solutions that feels obvious in hindsight but brilliant in execution — and also a reminder that space debris is no longer a purely theoretical problem. http://sciencedaily.com/releases/2026/01/260124003808.htm   Devon brought in a story that feels like it was engineered in a lab to trigger the phrase "AI hype cycle." A writer tested a platform where AI agents supposedly "rent grounded humans" to perform real-world tasks. The result? Almost no legitimate work, lots of promotional nonsense, intrusive automated follow-ups, and a general sense that the entire ecosystem is more marketing than function. It's less "future of labor" and more "future of weird startup experiments." The big takeaway: AI agents still struggle as real-world coordinators when things leave the digital sandbox. https://futurism.com/artificial-intelligence/ai-rent-human https://www.wired.com/story/i-tried-rentahuman-ai-agents-hired-me-to-hype-their-ai-startups/   Ben, in what might be the most unexpectedly wholesome science story of the week, talked about a cow using a tool. Yes, a literal cow. Researchers observed a pet cow using a deck brush to scratch herself, even switching between the bristled end and the stick depending on the body area. That level of flexible tool use challenges the long-standing assumption that livestock lack cognitive complexity. In short: cows might be smarter (and more adaptable) than we've historically given them credit for, which is both fascinating and mildly humbling. https://www.cell.com/current-biology/fulltext/S0960-9822(25)01597-0?_returnURL=https://linkinghub.elsevier.com/retrieve/pii/S0960982225015970?showall%3Dtrue   Book Club For Book Club, we tackled Liar! by Isaac Asimov, and this one sparked a surprisingly philosophical discussion. Herbie the robot doesn't lie out of malice — he lies because of the First Law of Robotics: a robot may not harm a human, and emotional harm counts. So instead of telling painful truths, he tells comforting lies, which ultimately causes even more psychological damage. Classic Asimov move: take a simple rule and stress-test it until it breaks in morally uncomfortable ways. We did agree the human characters feel a bit flat and two-dimensional, but the core sci-fi idea is doing the heavy lifting. The story still holds up because the ethical dilemma is timeless: is a comforting lie more harmful than a painful truth? Especially when the lie is delivered by something programmed to protect you? YouTube link: https://youtu.be/jDXW9hEjxps  Next week, we're heading into a tonal shift with a watch and review of Predator: Badlands, which should move us from philosophical robots and lying logic loops straight into survival, spectacle, and probably some very questionable life choices by characters who ignore obvious danger signs. Should be fun.   If you enjoyed this episode, make sure to follow the show, share it with a friend who loves sci-fi and strange tech stories, and join our community for bonus content, playlists, AI images, and unedited episodes over on Patreon. You can also hop into the Discord to talk books, space news, and questionable future technology with us. And don't forget to tune in next week for our review of Predator: Badlands — because nothing says thoughtful sci-fi discussion like immediately pivoting into survival horror chaos.

Today’s Peoples Bank Let’s Talk Indianola features Indianola Robotics members, Jackson Middleton and Aiden Barber.

The Hidden Lightness with Jimmy Hinton – For the first time, humanoid robots are not just being demonstrated behind glass or teased in controlled environments. They're being placed into the hands of the public to shape, test, refine, and reimagine. Humanoid robots and AI are not a distant future. They are a present reality. The question is no longer whether they will integrate into...

In this episode of The Kula Ring, Jeff White and Carman Pirie sit down with David Kilzer, founder and principal of Strategic Transformation Advisors, to explore the convergence of artificial intelligence and advanced humanoid robotics. Drawing on more than 50 years of experience in automation, David shares why this technological shift may dwarf previous revolutions like the internet and smartphones. The conversation dives into what makes AI-enabled humanoid robots fundamentally different from traditional industrial automation, why change management and human readiness are critical to success, and how manufacturers can begin preparing today. David introduces the concept of the “Humanoid Readiness Quotient,” a framework to help organizations assess their preparedness for this emerging era. The discussion also explores open-source robot operating systems, the importance of data infrastructure, and the competitive implications of dramatically lower operating costs. This episode is a forward-looking, practical guide for manufacturing leaders who want to navigate and capitalize on the coming transformation. To hear more from David on this fascinating topic, please give his Tedx talk a look, you can find that here.

Aditya Agarwal did not plan to work in robotics. He got rejected from his first-choice major, joined a student club to keep his parents off his back, and stumbled into one of the fastest-growing fields in tech. Now he is Head of Robotics at Medra, a company building physical AI scientists that let researchers run experiments remotely at speeds a traditional lab cannot touch."Even the companies that have made the most progress haven't deployed at the scale of laptops, cars, or phones. So if you have experience scaling hardware products, that is super valuable at an early-stage robotics company."What we get into: why the PhD requirement is mostly gone, how AI is shrinking the hardware development timeline, and the cheapest way to start building with robotics today if you cannot afford to go back to school or take a step back in your career.Timestamped Highlights01:19 The accidental path into robotics that actually worked03:04 Whether you still need an engineering degree for hardware roles04:48 Master's degree vs. early-stage startup: what gets you there faster10:57 How AI is replacing the guesswork in hardware configuration15:51 How to start learning robotics at home without spending much18:38 Why rigid hiring processes are costing robotics teams good candidatesIf this one lands, subscribe and share it with someone who has been thinking about making a move into the space.

Host Jeremy C. Park interviews Ethan Pretsch, Founder and President of WatchDog Robotics, which offers autonomous fire suppression systems using industry leading sensors and monitoring software combined with robotic nozzles to detect fires early and rapidly extinguish them. Ethan shares his some of his background and where the idea for the business started after searching for fire suppression systems that could be used in large tent structures that were up for long periods of time. He explains the limitations of traditional sprinkler systems, which have remained unchanged since 1874, and describes their autonomous alternative that uses sensors to detect and extinguish fires within 12 seconds using robotic water cannons. The system can detect fires up to 400 feet away and deliver water to a range of about 200 feet, making it suitable for large industrial spaces, warehouses, and manufacturing facilities. Ethan notes that while the technology is evolving, traditional systems still pose risks of extensive water damage and toxic cleanup, and the autonomous solution offers a more efficient and cost-effective alternative.Jeremy and Ethan discuss the global adoption of advanced fire protection technologies, noting that while Europe and Asia have embraced these innovations, the US market is catching up. Ethan highlights the positive reception from fire engineers and code enforcement officials, suggesting that written codes will soon reflect the potential of robotic autonomous firefighting.Ethan highlights the potential markets in Tennessee, including manufacturing, distribution, and forestry products, and mentions his ties to Memphis, where he has an employee and access to a robust industrial base with skilled professionals. He emphasizes the importance of building relationships to leverage shared knowledge and support, which is crucial for driving opportunities.Ethan expresses interest in applying his technology to wildfire defense and improving plant uptime and safety. He shares advice on taking the first step towards achieving goals, emphasizing the importance of action and learning through available resources like CAD design and programming. He highlights the transformative impact of AI and current low barriers to entry, encouraging natural curiosity and willingness to learn. Ethan also provides details on how to connect with WatchDog Robotics, including their website, social media presence, and options for site visits, demonstrations, and risk assessments.Visit https://www.watchdogrobotics.com to learn more about WatchDog Robotics.

In this episode of Lead-Lag Live, I sit down with Derek Yan, Senior Investment Strategist at KraneShares, to discuss whether humanoid robotics is a real commercialization story or just the next overhyped thematic trade.From factory deployment by Tesla, BMW, and Amazon to China's aggressive industrial push, Yan explains why embodied AI may represent the next structural shift in automation — and how the KraneShares Global Humanoid and Embodied Intelligence ETF $KOID captures the full ecosystem beyond mega-cap names like Nvidia and Tesla.In this episode:– Why humanoid robotics is already entering commercialization– How equal weighting avoids mega-cap concentration– What Morgan Stanley's trillion-dollar projections really mean– Why China exposure is a feature, not a bug– The key milestones that signal mass deployment is comingLead-Lag Live brings you inside conversations with the financial thinkers who shape markets. Subscribe for interviews that go deeper than the noise.#HumanoidRobotics #EmbodiedAI #ArtificialIntelligence #ThematicInvesting #EmergingTech #ETFSupport the show

On this episode of The Green Way Outdoors podcast Kyle Green, Ryan Parks and AJ Beadle discuss The Darién Gap. A dangerous, roadless jungle spanning the Colombia-Panama border, acting as the sole overland link between South and Central America, and has become a perilous route for migrants seeking to reach North America, filled with natural hazards like rivers and wildlife, alongside human threats from traffickers, smugglers, and violence, with hundreds of thousands undertaking the trek annually despite extreme risks, including death, disease, and exploitation. Then they dive in to the Florida Everglades, where researchers are using "robo-bunnies", solar-powered robotic rabbits, to lure and trap invasive Burmese pythons, which have devastated native mammal populations. These modified toy bunnies mimic real marsh rabbits with heat, movement, and soon scent, attracting pythons to strategically placed pens, triggering alerts for contractors to remove the snakes. It's a high-tech, ongoing trial by the University of Florida and South Florida Water Management District to combat the elusive pythons that are nearly impossible to find otherwise. Lastly, a large coalition of scientists, tribal nations, and environmental groups strongly advocates for removing the four lower Snake River dams (Ice Harbor, Little Goose, Lower Monumental, Lower Granite) because they are seen as a major obstacle to salmon recovery, making populations vulnerable to extinction, despite the dams providing benefits like power, irrigation, and barge transport, which would need replacing. Federal agencies, including NOAA Fisheries, have concluded that breaching is essential for salmon survival, especially with climate change making reservoirs warmer, while proponents argue it's the single best way to restore vital salmon runs to Idaho and beyond. On the other hand, the economic Impact of dam removal would be terrible and end efficient barge transport for wheat and irrigation for 400,000 acres, increasing costs for farmers. There is also no true way to transport that wheat for export if the dams were removed. The dams also generate significant clean energy, which would need replacing. Some also argue climate change, hatchery issues, and predation are also major threats, and dam removal isn't a guaranteed fix. So what is the right answer?  Watch our HISTORY Channel show on:HISTORY: https://www.history.com/shows/the-green-way-outdoors  Follow us on:Facebook: https://www.facebook.com/TheGreenWayOutdoors/Instagram: https://www.instagram.com/thegreenwayoutdoors/Twitter: https://twitter.com/thegreenwayout?lang=enYoutube: https://m.youtube.com/channel/UCjR5r6WwXcPKK0xVldNT5_gWebsite: www.thegreenwayoutdoors.com Watch our HISTORY Channel show on:HISTORYWAYPOINT TVFollow us on:FacebookInstagramTwitterYoutubeOur Website

In this episode we reconnect with Robert Rose, CEO of Reliable Robotics, fresh off his testimony before Congress on the state of advanced air mobility. Robert shares what most people misunderstand about FAA certification, i.e. that the regulator isn't there to coach you through it, they're just calling balls and strikes. We explore why Reliable has spent eight years building autonomous systems within existing regulations rather than waiting for new rules, how they've convinced the FAA that zero-visibility automated landing standards can scale from wide-body jets down to Cessna Caravans, and why the "cargo first" narrative that dominates autonomy discussions is largely a regulatory myth.We also dig into Reliable's new Pentagon contract to deploy autonomous cargo aircraft for contested logistics in the Indo-Pacific, what the military calls building "conveyor belts in the sky." Robert explains why military logistics actually demands commercial-grade safety in ways most people don't appreciate, how their solid-state radar technology became an unexpected multibillion-dollar opportunity for existing airlines, and what changed at the FAA after years of low morale and congressional scrutiny. It's a grounded, technically rigorous conversation about what it actually takes to certify autonomy, why operational risk assessments don't work for aircraft above a certain weight class, and how Reliable is grinding through hundreds of compliance submissions to prove that autonomy isn't some distant dream but it's ready now.

All-in-one software solutions didn’t work before and aren’t going to work this time, Juan George, co-founder of 858 Partners, tells Bloomberg Intelligence. In this episode of the Choppin’ It Up podcast, George sits down with BI’s senior restaurant and foodservice analyst Michael Halen to discuss why modular, open systems that allow operators to innovate are the best option. He also gives advice on negotiating software-as-a-service (SaaS) contracts and comments on investments in AI, robotics, point-of-sale systems, customer data platforms and solutions to help control back-of-house spending.See omnystudio.com/listener for privacy information.

In this episode of The Venture Capital Podcast (VC.fm), hosts Peter Harris and Jon Bradshaw talk with Wally Wang, Founding Managing Partner at Scale Asia Ventures and a Business Insider Seed 100 (2025) investor, about identifying AI winners early — and scaling them across the U.S. and Asia (Japan, Korea, APAC).Wally brings a unique mix of experience as a machine learning scientist (NYU / CMU / Microsoft), a former operator (YC-backed Pebble, acquired by Fitbit), and an enterprise founder who helped grow DataVisor (raised $150M+). Today, he backs AI infrastructure, enterprise AI agents, cybersecurity, developer tools, and robotics, with a strong cross-border thesis that helps founders expand globally and access strategic distribution partners in Asia.We discuss:How to evaluate “real” AI founders vs hypeAI moats: why product-market fit isn't enough anymoreWhat actually creates defensibility in enterprise AI (data, trust, workflows, integrations)Build vs buy in AI: when enterprises DIY vs purchase softwareWhy vertical AI agents are safer than horizontal AI wrappersCross-border expansion strategy: U.S. → Asia vs Asia → U.S.Robotics + physical AI opportunities and Asia's hardware advantageWhy enterprise + government budgets are still under-targetedFollow the PodcastInstagram: https://www.instagram.com/venturecapitalfm/Twitter: https://twitter.com/vcpodcastfmLinkedIn: https://www.linkedin.com/company/venturecapitalfm/Spotify: https://open.spotify.com/show/7BQimY8NJ6cr617lqtRr7N?si=ftylo2qHQiCgmT9dfloD_g&nd=1&dlsi=7b868f1b72094351Apple: https://podcasts.apple.com/us/podcast/venture-capital/id1575351789Website: https://www.venturecapital.fm/Follow Jon BradshawLinkedIn: https://www.linkedin.com/in/mrbradshaw/Instagram: https://www.instagram.com/mrjonbradshaw/Twitter: https://twitter.com/mrjonbradshawFollow Peter HarrisLinkedIn: https://www.linkedin.com/in/peterharris1Twitter: https://twitter.com/thevcstudentInstagram: https://instagram.com/shodanpeteYoutube: https://www.youtube.com/@peterharris2812

Virgilio Matheus, MDAshley L. Botsford, PA-C

Matthew Shindell outlines the history of robotic exploration, from Mariner to Ingenuity, while noting the political and technical hurdles facing future human missions to Mars.

AJ Meyer, CEO of Pickle Robot, isn't betting on general-purpose humanoid robots. Instead, he's a believer in robots and Physical AI which solve specific, high-volume problems. AJ joins Sam and Asad to reveal how he recently secured a nine-figure enterprise contract and why "boring" logistics tasks are the gateway to mass adoption of robots. But with mass adoption's opportunities, so too are there new risks. AJ shares that while physical safety is an important consideration, the cybersecurity risk of a networked robot workforce is what needs the most attention right now. This and a ton more in this week's episode of Topline with Sam Jacobs (CEO @ Pavilion) and Asad Zaman (CEO @ Sales Talent Agency). Thanks for tuning in! Catch new episodes every Sunday Subscribe to Topline Newsletter. Tune into Topline Podcast, the #1 podcast for founders, operators, and investors in B2B tech. Join the free Topline Slack channel to connect with 600+ revenue leaders to keep the conversation going beyond the podcast! Chapters: 00:00 Teaser and Introduction to AJ Meyer 02:53 The Convergence of Physical and Digital AI 05:50 Safety Constraints and the "Acrobat" Robot Disaster 09:19 Mobile Manipulation vs. General Purpose Humanoids 12:47 Cybersecurity Risks in Connected Robot Networks 18:52 AI Surveillance and Authoritarian Risks 28:01 Debunking the Myth of Unskilled Labor 34:54 The Moving Goalposts of AGI 38:19 Solving the Open World Generalization Problem 42:09 Why Foundation Models Need Systems Engineering 48:23 Designing Business Models for Enterprise and Mid-Market 53:20 The Nine-Figure "ChatGPT Moment" for Robotics 58:14 Transferring SaaS Go-To-Market Skills to Hardware 01:03:45 Taste and Judgment as Career Differentiators 01:07:50 Hiring Needs and Closing Thoughts

Agade: The AI-Powered Wearable Robots That Protect Workers, Not Replace Them AI Meets Human CraftsmanshipThere's something poetic about a technology born to help people with muscular dystrophy finding its second life on factory floors and logistics warehouses. That's the story of Agade, an Italian deeptech startup that began as a research project at Politecnico di Milano and evolved into something far more ambitious: a mission to preserve human craftsmanship in an age of automation.I sat down with Lorenzo Aquilante, CEO and co-founder of Agade, to talk about their journey from healthcare innovation to industrial exoskeletons—and what it was like showcasing their latest product at CES 2026.The origin story matters here. Back in 2017, researchers at Politecnico di Milano started developing exoskeletons for people affected by muscular dystrophy. They created something different—a semi-active model powered by AI that recognizes when a user is lifting and responds accordingly. It wasn't just about motors and sensors. It was about intelligence.Then companies came knocking. Manufacturing firms, logistics operations, industries where human workers still matter because their skills, experience, and judgment can't be replaced by machines. They saw potential. Why not use this technology to protect the people doing the heavy lifting—literally?Agade was founded in 2020 with a clear mission: preserve craftsmanship against the physical toll of material handling. Not replace humans. Protect them.The company now has two products. The first, launched in 2024, focuses on shoulder assistance. The second—the one they brought to CES 2026—targets the lower back, which makes sense when you consider that back pain is practically an occupational hazard for anyone moving materials all day.What makes Agade's approach different is that semi-active AI system. The exoskeleton knows when you're lifting. It responds. It's not just a passive brace or a fully motorized suit that takes over. It's somewhere in between—smart enough to help, light enough to wear all day.Lorenzo emphasized something that resonated with me: the importance of feedback. From day one, Agade has been obsessed with real-world testing. Not lab conditions. Actual workers doing actual jobs. Because the buyer isn't the user—companies purchase these for their employees—and that creates a unique dynamic. You need both sides to believe in the technology.The CES experience brought that home. There's always the initial wow factor when someone sees a wearable robot with motors and sensors. But the real work happens after the demo, when users tell you what needs to improve. That's where the collaboration lives.And here's what struck me most about this conversation: Agade isn't trying to remove humans from the equation. They're trying to keep humans in it longer, healthier, and more capable. In a world racing toward full automation, there's something refreshing about a company betting on human skill—and building technology to protect it.The products are available globally. You can reach Agade through their website at agadexoskeletons.com, find them on LinkedIn and other social channels, and even arrange trials before committing to a purchase.For those of us watching the intersection of AI, robotics, and human labor, Agade represents a different path. Not humans versus machines. Humans with machines. Tools that amplify rather than replace.That's a story worth telling.Marco Ciappelli interviews Lorenzo Aquilante, CEO & Co-Founder of Agade, for ITSPmagazine's Brand Highlight series following CES 2026.>>> Marcociappelli.comGUESTLorenzo Aquilante, CEO and co-founder of Agadehttps://www.linkedin.com/in/lorenzo-aquilante-108573b0/RESOURCESAGADE: https://agade-exoskeletons.comAre you interested in telling your story?▶︎ Full Length Brand Story: https://www.studioc60.com/content-creation#full▶︎ Brand Spotlight Story: https://www.studioc60.com/content-creation#spotlight▶︎ Brand Highlight Story: https://www.studioc60.com/content-creation#highlightKEYWORDSAgade, exoskeleton, CES 2026, wearable robotics, AI, future of work, industrial exoskeleton, made in Italy, workplace safety, deeptech, robotics. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Jeff Bliss and Brandon Weichert debate the AI boom, predicting a market correction followed by a second wave where robotics and AI integration fundamentally transform the global economy.1919 PACIFIC PALISADES AND HOLLYWOOD SETS

Brandon Weichert predicts the next major shift involves pairing reliable AI with accurate robotics to replicate human hands, lowering costs but potentially displacing American workers across manufacturing sectors.1958

This interview is disseminated on behalf of Realbotix. Realbotix (TSXV: XBOT | OTC: XBOTF | FSE: 76M0.F) CEO Andrew Kiguel joins Stocks to Watch to break down the company's recently announced transaction involving a NASDAQ-listed vehicle. He explains how the reverse takeover structure works, why the company chose this path instead of a traditional uplisting, how shareholders maintain ownership at the parent level, and what 75–90% control could mean post-closing. Kiguel also addresses market confusion, dilution concerns, regulatory approvals, and the expected timeline for completion.Learn more about the transaction: https://www.realbotix.ai/news/realbotix-corp-announces-the-sale-of-realbotix-llc-subsidiary-to-a-nasdaq-listed-issuerVisit: https://www.realbotix.ai/ Watch the full YouTube interview here: https://youtu.be/_qAtjqFgCHsAnd follow us to stay updated: https://www.youtube.com/@GlobalOneMedia

This interview is disseminated on behalf of Humanoid Global. Humanoid Global (CSE: ROBO | OTC: RBOHF | FRA: 0XM1) aims to provide diversified exposure to humanoid robotics and embodied AI as robots transition from labs to factories, warehouses, and eventually homes.We sit down with CEO Shahab Samimi and Marc Theermann, Chief Strategy Officer at Boston Dynamics and a Technical Advisor at Humanoid Global, to explore the rapid evolution of humanoid robotics and physical AI. The interview covers Humanoid Global's investment strategy, the distinction between humanoid and task-specific robots, the global labor shortage, scaling challenges in robotics, and what separates viable commercial platforms from early-stage prototypes.Learn more: https://www.humanoidglobal.aiWatch the full YouTube interview here: https://youtu.be/jVlVx2F2g0o?si=O0sxmywmeGOG8BXqAnd follow us to stay updated: https://www.youtube.com/@GlobalOneMedia

A.I. is evolving fast, and humanity is falling behind. Dario Amodei, the chief executive of Anthropic, has warned about the potential benefits — and real dangers — linked to the speed of that progress. As one of the lords of this technology, is he on the side of the human race?01:37 - The promise and optimism of A.I.12:59 - White collar "bloodbaths"25:09 - Robotics and physical labor30:16 - The first “dangerous” scenario42:22 - What if it goes rogue?48:01 - Claude's constitution(A full transcript of this episode is available on the Times website.)Thoughts? Email us at interestingtimes@nytimes.com. Please subscribe to our YouTube Channel, Interesting Times with Ross Douthat. Subscribe today at nytimes.com/podcasts or on Apple Podcasts and Spotify. You can also subscribe via your favorite podcast app here https://www.nytimes.com/activate-access/audio?source=podcatcher. For more podcasts and narrated articles, download The New York Times app at nytimes.com/app. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

AI developments legit change hourly.That means what business leaders can do changes daily. And drastically.As soon as you FINALLY learn a new AI technique, it's often already outdated, and approvals to use it at work can take forevvvvver.Solution: know what's coming.That's why you have me.I'm not a mind reader, but I spend almost all day every day talking with the people building AI, using it myself, and teaching others. It's literally my job.Each year I publish predictions and a roadmap because you don't have 10 hours a day to keep up.(I do.)So, today is part 1 of one of our most important shows of the year: Our AI Predictions and Roadmap series. AI Agent Crash, Software Collapses and Non-Human Economies. 2026 AI Prediction and Roadmap Series. Vol 1Let's get it.Newsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageJoin the discussion on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders.Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: info@youreverydayai.comConnect with Jordan on LinkedInTopics Covered in This Episode:2026 AI Lab-University Data PartnershipsVenture Capital Firms Shift to Venture StudiosEnterprise Demand for Grounded-Only AI ModelsGoogle AI Winning Short-Term, Building Long-TermInternal Agent-to-Agent Enterprise EconomiesSoftware Stocks and ETFs Face 2026 DrawdownMessaging as Universal AI Agent InterfaceMajor 2026 National AI Agent Crash PredictedFortune 500 Shadow AI Data BreachesTimestamps:00:00 "AI Trends and Roadmap Insights"04:39 2026 AI Roadmap Series09:28 "AI Impact on U.S. Colleges"12:11 "AI Strategy and Training Services"16:36 "Future of Venture and Software"18:45 "Three Sources for AI Models"21:56 "Google's Multimodal Model Dominance"24:15 "Google's Robotics & AI Advances"27:17 Agent Cost Attribution Systems32:56 "Messaging as AI Communication Interface"36:42 "Autonomy Crisis and Media Impact"38:13 "AI Surge and 2026 Crash"41:26 Choosing the Right AI Tools44:27 "AI Governance and Accountability Essentials"49:36 AI Disruption and Safety Warning52:27 AI Automates Competitive Analysis Tasks53:55 "AI Drives 24/7 Agent Ops"58:11 "Sharing Notes and Support"Keywords: O2026 AI predictions, AI roadmap, AI agent crash, software collapse, non-human economies, AI agent,Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info) Start Here ▶️Not sure where to start when it comes to AI? Start with our Start Here Series. You can listen to the first drop -- Episode 691 -- or get free access to our Inner Cricle community and access all episodes there: StartHereSeries.com 

Learn French by Watching TV with Lingopie: https://learn.lingopie.com/dailyfrenchpodLe traditionnel défilé du Nouvel An chinois a envahi les Champs-Élysées ce dimanche. Des dragons robotisés ont marqué l'entrée dans l'année du Cheval de Feu.Traduction:The traditional Chinese New Year parade took over the Champs-Élysées this Sunday. Robotic dragons marked the beginning of the spectacular Year of the Fire Horse. Hébergé par Acast. Visitez acast.com/privacy pour plus d'informations.