Thinking On Paper

Have you used Google Search recently? Exactly. Most companies, and most people, still think about Google when they think about search. They’re still spending heavily to rank there and paying for the ads around it.But more people are asking ChatGPT, Claude and Gemini what to buy, read, use or trust.SEO isn’t disappearing. It’s evolving into GEO.Awad Sayeed, co-founder and CTO of Parsnipp AI, joins Thinking on Paper to explain generative engine optimisation, or GEO, and how companies can become more visible inside ChatGPT, Claude, Gemini and other AI answer engines.Traditional SEO focuses on keywords, backlinks and rankings. GEO is more dependent on context: who the user is, what they’ve already asked, what they’re trying to achieve and how an AI system retrieves and combines information.In this episode, we discuss:How generative engine optimisation differs from SEOWhy context matters more than keywords in AI searchHow ChatGPT, Claude and Gemini use information differentlyWhat persona-based agents reveal about brand visibilityHow structured data helps AI systems understand websitesWhy comparison pages and clear product information matterWhat black-hat GEO could look likeHow AI-generated content could pollute the internetWhether brands should create separate experiences for humans and AI agentsHow advertising may develop inside AI assistantsAwad argues that GEO doesn’t replace SEO. Strong websites, useful content and clear structure still matter. But companies now need to think about whether AI systems can retrieve, interpret and recommend their information in the right context.And as this is Thinking On Paper, we ask about the human impact, the wider change in the structure of the internet, trust, data and consumerism. Please enjoy the show.--🏠 Buy us a beer on Substack🫵 Choose your own technology adventure 📺  Watch our beautiful faces on YouTube 🎧 Remember Steve Jobs on APPLE📺 Get clips and exclusive videos on Instagram --Chapters(00:00) Introduction to Generative Engine Optimization (03:36) Understanding Persona-Based Agents (06:23) The Transition from SEO to GEO(09:06) Context in LLMs and GEO(11:41) Black Hat Strategies in GEO(14:22) The Future of the Internet(16:58) Advertising in the Age of GEO(19:37) The Impact of GEO (28:22) The Evolution of AI Models (29:03) Integrating AI into Business Strategies(29:52) Agents vs. Humans(32:10) The Future of SEO and GEO(34:08) Tools for Visibility and Analytics in AI(36:00) Customer-Driven Development(39:23) The Role of Storytelling in GEO(42:04) Model Transparency and the Future of AI

The UK produces world-class technology and is home to exceptional tech entrepreneurs. All too often it watches them scale in America.Rory Daniels, Head of Emerging Technology and Innovation at techUK, joins Thinking on Paper to discuss whether the United Kingdom can remain competitive as quantum computing, robotics, photonics, AI and advanced computing begin to converge.The UK has strong research institutions, deep technical talent and globally significant companies. Its recurring problem is scale. Promising technologies are often developed in British universities and laboratories, then commercialised or funded elsewhere.In this episode, we discuss:What makes the UK robotics industry different from the US and ChinaWhy British companies often focus on specialised robots for nuclear sites, wind turbines and industrial environmentsHow autonomous driving companies such as Wayve combine AI, sensors and connectivityWhether robotaxis can coexist with London’s black-cab industryWhy UK technology companies struggle to scale after the startup stageHow access to long-term capital affects quantum, robotics and semiconductor companiesThe role of universities, technology-transfer offices and regional innovation clustersWhat is happening in Coventry, Edinburgh, Milton Keynes, Barnsley and other UK technology centresHow digital twins and simulation are used to train robots and autonomous vehiclesWhy photonics matters for quantum computingHow quantum, photonic, neuromorphic and biological computing could convergeWhether AI can develop the judgement and wisdom required to solve complex technical problemsHow techUK connects companies, researchers and policymakersWhy public trust and adoption matter as much as technical performanceRory argues that the UK’s advantage may not lie in dominating a single technology. It may come from combining existing strengths in AI, chip design, robotics, quantum computing, photonics and connectivity.The conversation examines what government, industry, universities and investors must do if the UK is to convert strong research into companies that can scale globally without leaving the country.Please enjoy the show.Thinking on Paper is a technology podcast about AI, Space, quantum computing, science, and the systems shaping the future. 🏠 Buy us a beer on Substack🎧Get Up Close On YouTube 🎧 Remember Steve jobs on APPLE📺 Get the clips and outtakes on Instagram --Chapters(00:00) The UK Technology Landscape(03:14) Robotics: A UK Perspective(05:54) Autonomous Vehicles in the UK(08:39) The UK's Innovation Ecosystem(11:05) Challenges and Opportunities for UK Tech Entrepreneurs(13:27) Regional Innovation and Government Initiatives(16:33) The Role of Universities in Tech Development(19:15) Barnsley: A Blueprint for Tech Towns(21:53) Government Initiatives in Robotics(24:20) Digital Twins and the Future of Robotics(27:12) Quantum Computing and Photonics in the UK(29:24) The Role of Education in Emerging Technologies(30:55) AI and Human Wisdom: A Complex Relationship(38:02) Neuromorphic Computing: The Future of AI(38:23) Convergence of Technologies: Opportunities for the UK(42:42) The Human Element in Technology Adoption

The Vij brothers join Thinking on Paper to discuss Neo, an autonomous machine learning engineer designed to automate parts of the AI development process.As demand for AI systems grows, companies and governments are competing for a limited pool of experienced machine learning engineers. The challenge isn’t only access to data or computing power. Many organisations also lack the technical expertise required to build, test and deploy effective models.Neo uses a multi-agent system to perform tasks normally handled by machine learning engineers, including analysing datasets, selecting modelling approaches, running experiments and evaluating results. The aim is to automate repetitive technical work while allowing human engineers to concentrate on higher-level decisions and more creative problems.In this episode, we discuss:What an autonomous machine learning engineer isHow Neo’s multi-agent AI system worksWhy skilled machine learning engineers are in such high demandWhich parts of AI development can be automatedHow autonomous agents compare with traditional machine learning workflowsWhy Kaggle Grandmasters are considered leading practitioners in applied machine learningWhether AI agents can match expert human performanceHow automation could affect machine learning jobs and salariesThe evolution of GPUs from graphics hardware to AI infrastructureWhat the Vij brothers learned from working at CERNHow autonomous AI systems could change business, creativity and technical workNeo is intended to expand access to machine learning expertise rather than simply generate code. Its development raises a wider question: what happens when AI systems can perform the specialised work required to build other AI systems?This conversation examines the technical capabilities of autonomous machine learning agents, the shortage of experienced AI talent and how automation could reshape the role of engineers--Timestamps(00:00) Why Are There So Few Machine Learning Engineers?(01:54) Meet Gaurav Vij and Saurabh Vij(02:57) Lessons Learned from Working at CERN(04:45) How to Explain The Importance Of A.I. to Your Parents(07:24) The World’s First Autonomous Machine Learning Engineer: What AI Problem Does NEO Solve?(08:17) AI Competitions and Kaggle Grandmasters(11:06) How Many A.I./ML Engineers Do We Need?(17:30) Fixing The A.I. Hallucination Problem(18:09) Hot Buttons: 5 AI Questions In 30 Seconds(18:46) Hollywood: Doomed by A.I, or Reborn?(20:26) AI News: Nvidia Digits Explained(21:51) Moore's Law And Could AI Models Be Motivated by Rewards?(25:42) AI And Quantum Computing(29:45) The Thinking on Paper Carry-Over Question(30:16) After Hours: Backstage Extra--Check out NEO: https://heyneo.so/Learn more about the show: www.thinkingonpaper.xyzFollow Thinking On Paper On Instagram: https://www.instagram.com/thinkingonpaperpodcast/

We read NASA’s Moon Base User’s Guide and ask what it would take to establish a sustained human presence on the Moon.A permanent lunar base requires far more than rockets, landers and astronauts. NASA and its partners would need to build an integrated infrastructure system covering power generation, communications, navigation, habitats, transportation, logistics, robotics and resource extraction.In this episode, we discuss:How NASA plans to build a permanent Moon baseWhy reliable power is essential for long-term lunar operationsWhether nuclear power will be required on the MoonHow astronauts, vehicles and robots would communicate and navigateWhat lunar habitats need to protect crews from radiation and extreme temperaturesHow autonomous robots could prepare sites and maintain infrastructureWhy lunar dust creates serious engineering problemsHow equipment from different companies and countries could work togetherWhether water, oxygen and construction materials can be extracted from lunar resourcesWhat infrastructure must exist before humans can live and work on the Moon continuouslyThe discussion also examines the gap between NASA’s long-term ambitions and the systems currently available. Many of the technologies exist individually, but they haven’t yet been combined into a reliable, scalable lunar operating environment.This episode asks whether a permanent Moon base is a realistic extension of human spaceflight or a programme whose infrastructure requirements remain badly underestimated.--Chapters00:00 Executive Summary and Vision01:17 Phased Approach to Moon Base Development07:21 Challenges of Lunar Environment09:06 Interoperability and Coordination in Space15:13 Economic Incentives and Future of Space Development17:03 Identifying Gaps in Space Technology20:23 Functional Gaps and Their Implications24:01 Dust Challenges and Solutions29:10 The Moon as a Launchpad for Mars31:08 Human Factors in Lunar Missions--Thinking on Paper is a technology podcast about AI, Space, quantum computing, science, and the systems shaping the future. 🏠 Buy us a beer on Substack🎧 Take us with you on YouTube🎧 Remember steve jobs on APPLE📺 Get the clips and outtakes on Instagram

Sanjay Vijendran of TerraSpark joins Thinking on Paper to explain how space-based solar power could become a practical source of clean energy.TerraSpark is developing wireless power-transmission systems that could eventually collect solar energy in orbit and beam it to receivers on Earth. The company plans to demonstrate the concept by powering a live music event in Portugal and by testing radio-frequency power transfer aboard Dcube’s Arrakis mission.In this episode, we discuss:How space-based solar power worksHow energy can be transmitted wirelesslyTerraSpark’s plan to power a concert in PortugalWhat its in-orbit power-beaming experiment will testThe differences between radio-frequency and laser power transmissionHow near-infrared power beaming worksHow much energy is lost during wireless transmissionWhether orbital data centres could use the same infrastructureHow space-based solar could improve energy securityWhy spectrum regulation and interference testing matterWhat investors and regulators need to see before the technology can scaleSanjay explains the engineering, regulatory and commercial challenges behind power beaming, including transmission efficiency, safety, spectrum allocation and the cost of placing energy infrastructure in orbit.This conversation examines whether space-based solar power can move beyond demonstration projects and become a credible alternative to terrestrial energy generation and fossil fuels.--Thinking on Paper is a technology podcast about AI, Space, quantum computing, science, and the systems shaping the future. 🏠 Buy us a beer on Substack🎧Be With Us On YouTube🎧 Remember steve jobs on APPLE📺 Get the clips and outtakes on Instagram --Chapters(00:00) Introduction to Space-Based Solar Power(01:37) Market Trends and Projections(03:52) Energy Crisis and Global Dependencies(06:26) The Threat to Power Structures(07:39) Innovative Demonstrations of Wireless Power(10:31) Future Plans and Space Missions(20:41) Scaling Power Transmission from Space(22:35) Technologies for Space-Based Solar Power(31:22) Governance and Regulation of Space-Based Solar Power(49:57) The Future of Space-Based Solar Power

Jennifer Dunn, professor of chemical engineering at Northwestern University, joins Thinking on Paper to explain how lithium and copper mining affect water, ecosystems, local communities and the wider energy transition.Lithium and copper are essential to electric vehicles, grid storage, renewable energy, drones and data centres. But the environmental consequences of extracting these minerals vary sharply depending on the mine, location, technology and supply chain.Life cycle assessment offers a way to compare those impacts across different forms of production, from lithium brines and hard-rock mining to copper extraction, refining and recycling.In this episode, we discuss:The environmental impact of lithium miningHow lithium brine mining compares with hard-rock lithium miningWhy copper demand is risingHow mining affects water use and local water stressThe risks of pollution, biodiversity loss and mining wasteHow life cycle assessment compares mines and supply chainsWhy local conditions matter more than global averagesThe role of mine permitting in the energy transitionWhether recycling can reduce demand for new miningHow battery supply chains shift environmental costs between regionsWhat responsible critical-mineral production should look likeJennifer explains why no single measure can capture the full impact of a mine. Carbon emissions matter, but so do water availability, land use, waste, local ecology and the distribution of costs and benefits.This conversation examines whether clean energy can scale without transferring environmental harm from fossil-fuel systems to the communities that supply lithium, copper and other critical minerals.--Thinking on Paper is a technology podcast about AI, Space, quantum computing, science, and the systems shaping the future. 🏠 Buy us a beer on Substack🎧 Take us with you on Spotify🎧 Remember steve jobs on APPLE📺 Get the clips and outtakes on Instagram --Chapters(00:00) Disruptors & Curious Minds(02:10) The Demand for Copper and Lithium(02:57) Environmental Impact of Mining(05:59) Water Consumption and Mining Methods(08:30) Community Concerns and Local Impact(11:29) Recycling and Wastewater Mining(14:04) Life Cycle Assessments in Mining(27:06) Understanding Emissions in Mining(29:45) Life Cycle Assessment: A Comparative Approach(34:05) Stakeholder Perspectives on Mining Impacts(37:42) Technology and Transparency in Mining(42:42) Consumer Awareness and Ethical Sourcing(48:55) Challenges in Quantifying Social Impacts

Anders Sandberg examines whether artificial general intelligence could manage the global economy more effectively than human institutions.A sufficiently capable AI system might coordinate markets, allocate resources, interpret legal rules and respond to complex global problems faster than governments or companies. Greater efficiency, however, wouldn’t necessarily mean greater freedom.In this short excerpt from a longer Thinking on Paper conversation, Anders discusses:Whether AGI could manage the global economyHow superintelligence might improve global coordinationWhy markets and legal systems are difficult to optimiseWhether AI could make better decisions than human institutionsHow highly efficient systems could concentrate powerThe challenge of keeping advanced AI under human controlHow evolutionary pressures could shape competing software systemsWhether humans could become wealthier while losing political agencyWhat role people would retain in an AI-managed economyThe central question isn’t simply whether AGI could run economic systems better. It’s whether humans would still control the goals, rules and trade-offs behind those systems.This is a short from a much longer conversation with Anders Sandberg about superintelligence, governance and the future of human decision-making.--Thinking on Paper is a technology podcast about AI, quantum, space and their impacts on society, business and culture. It's very good. 🏠 Buy us a beer on Substack🎧 Watch on YouTube 🎧 Remember Steve Jobs on APPLE📺 Get the clips and outtakes on Instagram

NASA scientist Philip Metzger joins Thinking on Paper to explain why Moon dust and rocket exhaust create a major engineering problem for future lunar missions.When a spacecraft lands on the Moon, its engines can accelerate dust and rocks across the surface at high speed. That material can damage nearby equipment, including solar panels, telescopes, antennas, sensors and thermal-control systems.The problem becomes more serious as NASA, SpaceX and other organisations plan larger landers, permanent bases and more frequent missions. Every landing could threaten infrastructure already operating on the lunar surface.In this episode, we discuss:Why lunar landers throw dust and debris across the MoonHow rocket exhaust interacts with lunar soilWhy larger spacecraft such as Starship increase the riskHow Moon dust can damage solar panels, antennas and scientific instrumentsWhat this means for NASA’s Artemis programmeWhy future lunar bases may require dedicated landing padsHow far spacecraft should land from existing equipmentWhether lunar infrastructure needs exclusion zonesHow landing rules could affect Moon governanceWhat engineers still don’t know about repeated lunar landingsPhilip explains why lunar dust isn’t a minor operational inconvenience. It’s a systems-level problem that affects spacecraft design, base planning, scientific equipment and the rules governing activity on the Moon.This conversation examines one of the least visible challenges facing lunar exploration: how to land safely without damaging the infrastructure needed to remain there.--Thinking on Paper is a technology podcast about AI, Space, quantum computing, science, and the systems shaping the future. 🏠 Buy us a beer on Substack: https://thinkingonpaperpodcast.substack.com/🎧 Take us with you on Spotify: https://open.spotify.com/show/00volKqMsQntToeho35W47🎧 Remember steve jobs on APPLE: https://podcasts.apple.com/us/podcast/thinking-on-paper/id1713227258📺 Get the clips and outtakes on Instagram https://www.instagram.com/toptechpodcast/  

Matthew Weinzierl and Brendan Rosseau, authors of Space to Grow, join Thinking on Paper to explain how the commercial space economy is developing and what governments, companies and investors are trying to build beyond Earth.The space economy already supports communications, navigation, Earth observation and national security. Its next phase could include commercial space stations, lunar infrastructure, microgravity manufacturing, space-based data centres and the extraction of resources from the Moon.In this episode, we discuss:How the commercial space economy worksThe role of NASA in creating private space marketsHow the Artemis programme could support a lunar economyWhy governments fund technologies before commercial demand existsThe business case for commercial space stationsHow Starlink and GPS demonstrate the economic value of space infrastructureWhether space-based data centres could become commercially viableHow investors evaluate launch, satellite and lunar businessesThe growth of China’s space programmeWhy space has become central to national securityWho should own and use resources found on the MoonWhether the Outer Space Treaty is suitable for a commercial space economyHow international competition is shaping the new space raceMatthew and Brendan explain how public institutions and private companies divide risk, finance infrastructure and create markets in environments where costs are high and the rules remain unsettled.The conversation also examines how space activity should be governed as commercial and national interests expand. Mark and Jeremy attempt to rewrite the Outer Space Treaty and consider what a modern framework would need to say about property, resources, security and responsibility.This episode is about the economics and politics of commercial space, and whether today’s institutions can manage the industries now emerging in orbit and on the Moon.Please enjoy the show.--Thinking on Paper is a technology podcast about AI, Space, quantum computing, science, and the systems shaping the future. Connect with us.🏠 Buy us a beer on Substack🎧 Watch us compete with Lex Fridman on YouTube 🎧 Remember Steve Jobs and listen on APPLE 📺 Watch the clips and shorts on InstagramWatch a random video from Rick Beato. Because we love him.--Chapters(00:00) Government and Markets in Space(03:35) Microgravity (07:43) Economic Incentives (12:14) Political Cycles in Space Policy(17:09) International Collaboration (18:45) National Security in Space(21:36) Space Exploration(24:27) The Importance Of GPS(28:49) Space Investment(30:37) Space-Based Data Centers(33:40) Space Resources(38:26) Governance in Space(40:55) A New Space Treaty

Scott Crowder, Vice President of IBM Quantum Adoption, joins Thinking on Paper to explain IBM’s approach to quantum-centric supercomputing.Rather than replacing classical computers, IBM expects quantum processors to work alongside CPUs, GPUs and high-performance computing systems. Each type of hardware handles the parts of a problem it’s best suited to solve.In this episode, we discuss:What quantum-centric supercomputing meansHow quantum processors, GPUs, CPUs and HPC systems work togetherIBM’s roadmap towards fault-tolerant quantum computingWhat IBM Starling is designed to achieve by 2029How superconducting qubits workWhy quantum error correction is essentialThe role of Qiskit and open-source quantum softwareWhich quantum algorithms could deliver practical valueHow quantum computing could support chemistry and materials researchIBM and Cleveland Clinic’s work on protein simulationIBM’s collaboration with RIKENHow Nvidia GPUs fit into hybrid quantum systemsWhy accessibility and real-world adoption matter as much as hardware progressScott explains why useful quantum computing will depend on more than increasing qubit counts. It will require reliable hardware, error correction, strong software tools, integration with existing data centres and developers who can apply quantum systems to real problems.This conversation examines IBM’s plan to move quantum computing from experimental hardware towards fault-tolerant systems that can contribute to scientific and industrial computing.-Thinking on Paper is a technology podcast about AI, computing, science, and the systems shaping the future.🏠 HQ: www.thinkingonpaper.xyz📺 INSTAGRAM: https://www.instagram.com/thinkingonpaperpodcast/🎧 Spotify: https://open.spotify.com/show/00volKqMsQntToeho35W47🎧 APPLE: https://podcasts.apple.com/us/podcast/thinking-on-paper-technology-moves-fast-think-slower/id1713227258--Mark x: https://x.com/markfielding99Jeremy: https://www.linkedin.com/in/jeremygilbertson/–Chapters(00:00) Trailer(01:20) Quantum computing(02:40) IBM Reference Architecture(05:05) Superconducting (06:47) Algorithmic Discovery(12:34) Cleveland Clinic(13:44) IBM's quantum-centric supercomputing architecture(16:07) Quantum computers today(17:58) Quantum and classical converge(22:28) Richard Feynman (25:25) Data centers(32:01) Quantum computers in space(42:19) Qiskit, NVIDIA, and open source

World famous Oxford futurist and philosopher Anders Sandberg joins Thinking on Paper to discuss transhumanism, mind uploading, artificial general intelligence and the technologies humans use to extend their capabilities.Human augmentation doesn’t begin with brain implants or uploaded minds. Memory systems, smartphones, language models and AI agents already allow people to outsource parts of thinking, communication and decision-making. More advanced systems could extend that process into brain emulation, digital copies of human minds and AI-managed institutions.In this episode, we discuss:What transhumanism meansHow technology already extends human memory and intelligenceWhether AI agents should be accountable for their decisionsHow AGI differs from existing artificial intelligenceWhether machines can possess consciousness or empathyWhat mind uploading and whole-brain emulation would requireWhether an uploaded mind would still be the same personHow digital copies could affect identity, work and relationshipsWhether AGI could manage economies more effectively than humansHow automation could make people wealthier while reducing their agencyWhat longer lifespans would mean for society and personal identityHow fusion energy could affect human expansion beyond EarthWho should own lunar resources, asteroids and orbital infrastructureWhether existing systems of space governance can manage permanent settlementAnders explains why transhumanism, AI and space expansion shouldn’t be treated as separate technological futures. Each raises the same underlying questions about agency, identity, power and the boundaries of the human individual.The conversation moves from personal augmentation to civilizational governance, asking what humans become when intelligence can be extended, copied, redesigned or delegated to machines.Please enjoy the show.--Thinking on Paper is a technology podcast about AI, computing, science, and the systems shaping the future. Connect with us. 🎧 Listen to every podcast⁠📺 Follow us on ⁠Instagram⁠🏠 Follow us on ⁠X⁠🏠 Follow Jeremy on ⁠LinkedIn⁠To suggest guests or sponsor the show, please email: [email protected](00:00) TRAILER(08:09) Mobile Technology on Humanity(11:51) Accountability in AI Agents(18:25) Empathy(25:35) AGI vs. Alien Life(27:36) Consciousness (35:52) Uploaded Minds(40:33) Parallel Realities (45:16) Human Collaboration (46:24) AGI(51:23) The Dual Economy(57:43) Space Ownership (01:05:18) Human Expansion(01:17:49) The Space Race (01:21:43) Space Exploration(01:24:22) New Forms of Governance(01:26:18) NASA(01:28:41) Breakaway Movements in Space(01:30:16) Space Governance(01:34:18) Fusion Energy(01:42:15) Time and Life Extension(01:48:06) Extended Lifespans(01:52:03) Technology

Carissa Véliz joins Thinking on Paper to examine how AI forecasts, platform algorithms and prediction markets can influence the future they claim only to predict.Predictions aren’t always neutral descriptions. When they come from powerful technology companies, executives, platforms or financial markets, they can change investment, policy and public behaviour. A forecast may become a self-fulfilling prophecy because people act as though its outcome is inevitable.The conversation begins with a broader question about the good life, curiosity and what the analogue world offers that digital systems often remove. It then turns to the institutions increasingly making predictions about people and society.In this episode, we discuss:How AI predictions influence human behaviourWhy forecasts can become self-fulfilling propheciesHow technology executives shape expectations about the future of AIWhether AI hiring tools reinforce existing assumptions about workersHow TikTok and other recommendation systems direct attentionWhy engagement-maximising algorithms reward harmful contentHow prediction markets such as Kalshi and Polymarket workWhether prediction markets measure beliefs or help create outcomesHow platforms exploit the human desire for certainty and securityWhat the Molly Russell case reveals about algorithmic recommendationWhy comedy and serendipity resist predictive systemsHow citizens can make more deliberate choices about technology and beliefWhat Epicureanism offers that digital optimisation cannotCarissa argues that people should treat influential predictions as interventions rather than passive forecasts. The more reach and authority a prediction has, the greater its ability to reorganise the world around itself.This conversation examines how to resist technological prophecy by preserving uncertainty, curiosity and the freedom to choose futures that algorithms haven’t already selected.Please enjoy the show.--Thinking on Paper is a technology podcast about AI, computing, science, and the systems shaping the future.📺 Watch On YouTube: 🎧 Listen to every podcast⁠📺 Follow us on ⁠Instagram⁠🏠 Follow us on ⁠X⁠🏠 Follow Jeremy on ⁠LinkedIn⁠To suggest guests or sponsor the show, please email: [email protected]  (00:00) Intro(01:00) What is the good life? (02:00) Why knowing yourself matters more than strategy (04:44) The analog world vs the digital world (06:45) How prophecies exploit our need for security (08:47) Ancient Rome (10:11) The illusion of safety (12:27) When predictions work(15:00) Altman, Amodei, Huang(28:29) How to resist prophecies (29:53) Prediction markets(31:49) TikTok, algorithms, and the Molly Russell case (36:08) Engagement algorithms(40:54) Self-fulfilling prophecies (43:44) Comedy(46:59) Seinfeld (52:16) Karikó (53:40) Serendipity (56:13) Why Epicurus beats the Stoics

The AI meme war between the US and Iran has evolved into an absolute shit show. If you thought it was awful a few weeks ago, you ain't seen nothing yet.AI-generated Lego propaganda videos were a curiosity. Sometimes funny, often violent, always troublesome and never diplomatic, they quickly gained millions of views across social media... because social media. The White House Twitter (X) account was responsible for the US videos. An Iranian media company called Explosive Media, the Iranian. America, either put off by the global consensus that it was losing the war, or bored, switched their AI models to tax season (with equal ineptitude).Iran, losing the guns and missiles part of the war, has changed tact. Explosive Media turned up the heat. And was duly banned from YouTube. Which could of unleashed the beast. Now Iranian embassies are posting them on Twitter (X) and US creators are using the same format to mock it all with Lego.. Just watch it yourself. And let us know what you think. --🎧 Listen to every podcast⁠📺 Follow us on ⁠Instagram⁠🏠 Follow us on ⁠X⁠🏠 Follow Jeremy on ⁠LinkedIn⁠To suggest guests or sponsor the show, please email: [email protected](00:00) Explosive Media(00:38) US Bowling Iran(01:52) Trump's Mask(03:20) Blockade, Blockade(06:28) Drunken Hegseth(08:00) Truth

This episode of Thinking on Paper uses Space to Grow to examine who has the right to mine the Moon, extract resources from asteroids and build commercial activity beyond Earth.The Outer Space Treaty prohibits national sovereignty over celestial bodies, but it leaves important questions unresolved. Can companies own the resources they extract? Who grants mining rights? What happens when commercial claims, national security interests and international law collide?In this episode, we discuss:Who owns the Moon and other celestial bodiesWhether companies can legally mine lunar resourcesHow asteroid mining could workWhat the Outer Space Treaty says about ownershipWhy the Moon Treaty has limited international supportThe difference between owning territory and owning extracted resourcesWhich companies are trying to build a space-resource economyHow property rights could affect investment in lunar infrastructureThe role of the US Space Force in the commercial space economyHow China’s space programme is shaping strategic competitionWhy anti-satellite weapons threaten civilian and commercial systemsWhether competition for space resources could reproduce conflicts on EarthThe central legal problem is that space treaties were written before private companies could realistically reach the Moon, operate spacecraft at scale or plan commercial extraction.This conversation examines whether existing space law can govern a growing off-world economy, and how resource competition could affect security, diplomacy and the future of human activity in space.Please enjoy the show.--Thinking on paper is a technology podcast on The Social, Environmental, Cultural & Business Impacts Of Technology. --Chapters(00:00) Global Conflict(02:04) Human Nature (03:28) Asteroid Mining(05:53) Space Mining(11:05) The Space Resource Exploration Act(13:01) Space Mining Legislation(17:19) Philosophical Perspectives (20:14) National Security in Space(20:40) Government in Space Innovation(21:34) National Security (23:10) Weaponization of Space(24:47) The Prisoner's Dilemma (26:40) Humanity's Moral Compass (27:03) The Future of Humanity in Space

This episode of Thinking on Paper examines the largest space technology funding rounds of 2026 and what they reveal about the direction of the commercial space economy.Investment is moving beyond satellite launches and communications. Companies are now raising capital for orbital data centres, private space stations, reusable rockets, alternative navigation systems, weather intelligence, secure communications and national-security infrastructure.We count down funding rounds involving:StarcloudXona SpaceTomorrow.ioPLD SpaceStoke SpaceAxiom SpaceCesiumAstroVastSierra SpaceChina’s iSpaceThe episode covers:Why investors are backing space-based data centresThe business case for commercial space stationsHow Xona Space and other companies are developing alternatives to GPSWhy reusable launch systems continue to attract capitalThe growth of secure satellite communicationsHow space-based weather intelligence is becoming a commercial marketWhether microgravity research can support viable businessesWhy defence and national-security funding are becoming more importantHow private investment is shaping competition between the United States, Europe and ChinaWhat the largest funding rounds suggest about the next phase of the space economyThe companies raising the most capital aren’t all pursuing the same market, but several themes recur: sovereign infrastructure, lower launch costs, persistent Earth observation, orbital computing and a shift from individual spacecraft towards complete space-based systems.This conversation follows the money to understand which technologies investors believe can move from ambitious engineering projects to durable commercial infrastructure.Please enjoy the show.--🎧 Listen to every podcast⁠📺 Follow us on ⁠Instagram⁠🏠 Follow us on ⁠X⁠🏠 Follow Jeremy on ⁠LinkedIn⁠To suggest guests or sponsor the show, please email: [email protected](00:00) Starcloud(00:52) Xona Space(03:27) Tomorrow IO(06:01) PLD Space(08:00) Stoke Space(10:18) Axiom Space(12:29) Cesium Astro(14:50) VAST Space(19:02) Sierra Space(21:47) I-Space (Beijing Interstellar Glory Space Technology Ltd.)

The Martens Clause says that when written law runs out of steam, humanity still has obligations under the laws of humanity.This episode asks whether that old idea from the 1899 Hague Peace Conference can help govern new technologies that move faster than law: AI, autonomous weapons, military AI, space mining, space governance, and the race to build beyond Earth. The conversation moves from Nuremberg, the Corfu Channel, and Nicaragua to AI safety, black-box systems, tech accountability, and whether “not explicitly illegal” should ever mean “automatically allowed.”Please enjoy the show. And keep the peace. --Thinking on Paper is a technology podcast about AI, computing, science, and the systems shaping the future.🎧 Listen to every podcast⁠📺 Follow us on ⁠Instagram⁠🏠 Follow us on ⁠X⁠🏠 Follow Jeremy on ⁠LinkedIn⁠To suggest guests or sponsor the show, please email: [email protected](00:00) The First Peace Conference: A Historical Perspective(07:37) The Martin's Clause: Implications for Modern Governance(10:05) Space Tech and the Outer Space Treaty(13:58) AI and the Need for Ethical Frameworks(17:21) Accountability in Technology Deployment(22:56) The Future of Humanity: Collaboration vs. Competition

This episode of Thinking on Paper, we look at how artificial intelligence is moving from military planning into intelligence analysis, targeting and battlefield operations.Using a White House memorandum on America’s military AI strategy, Mark and Jeremy explore the push to build an AI-first warfighting force. The objective is to process information, identify threats and execute decisions faster than an adversary. That creates a central conflict between military speed and human control.In this episode, we discuss:How AI is being used in modern warfareWhat Project Maven doesHow AI supports intelligence analysis and military targetingWhat the military kill chain isHow AI could accelerate decisions across the kill chainThe development of autonomous weapons systemsWhether humans will remain involved in lethal decisionsHow AI-generated intelligence could produce false or misleading conclusionsWhy the United States sees China as its principal military AI competitorThe role of companies such as Anthropic and defence technology firmsWhat an open AI arsenal could mean for military procurementWhy the Pentagon is competing for AI researchers and engineersWhether faster military systems make conflict less likely or easier to escalateAI could help militaries process large volumes of data, identify targets more precisely and reduce the time between detection and response. It could also compress decision-making to the point where human review becomes a strategic disadvantage.The central question isn’t only whether military AI works. It’s who controls these systems when speed becomes the priority, and who remains accountable when intelligence, targeting and battlefield decisions are executed through software.Please enjoy the show.--🎧 Listen to every podcast⁠📺 Follow us on ⁠Instagram⁠🏠 Follow us on ⁠X⁠🏠 Follow Jeremy on ⁠LinkedIn⁠To suggest guests or sponsor the show, please email: [email protected](00:00) Department of War(00:58) Executive Order 14179(01:59) China(04:36) Anthropic(07:20) Pace Setting Projects(08:28) Kill Chain (10:22) Palmer Luckey(11:53) The AI Open Arsenal(13:57) The War Time Approach(16:46) AI Talent Acquisition (18:54) Speed wins

Iran made an AI Lego propaganda video about the United States. It was kind of funny. The US replied with Grand Theft Auto, Wii Sports, and Call of Duty. It wasn't. Children's toys and video games to push a distorted view of war at kids and morons on Twitter. Oh how they'll laugh. This is our first reaction video. Probably be our last.--🎧 Listen to every podcast⁠📺 Follow us on ⁠Instagram⁠🏠 Follow us on ⁠X⁠🏠 Follow Jeremy on ⁠LinkedIn⁠To suggest guests or sponsor the show, please email: [email protected](00:00) What Is Propaganda?(00:36) Iran Lego Propaganda Video(02:45) Reaction(06:55) Whitehouse GTA Iran War Video(09:07) Epic Fury - US Wii Sports Video(13:22) Call Of Duty Iran War Video

Space junk is becoming one of the biggest risks in low Earth orbit, from satellite collisions and Kessler syndrome to the millions of debris fragments already moving above Earth. This episode looks at how we got here, why deorbit rules have struggled, and whether active debris removal companies like Astroscale can turn space cleanup into a real market. The second half asks what happens when the same space economy depends so heavily on SpaceX, Starlink, launch access, satellite networks, and a private monopoly that governments increasingly rely on.Please enjoy the show--🎧 Listen to every podcast⁠📺 Follow us on ⁠Instagram⁠🏠 Follow us on ⁠X⁠🏠 Follow Jeremy on ⁠LinkedIn⁠To suggest guests or sponsor the show, please email: [email protected](00:00) Space junk (06:21) Kessler syndrome (10:57) Space Insurance (13:50) Government intervention(20:26) Active debris removal(22:37) Astroscale(24:53) Who pays (26:26) Is SpaceX a monopoly (29:08) NASA Administrator(33:04) Space governance

Conductor Quantum founder Brandon Severin joins Thinking on Paper to explain Google’s latest quantum breakthrough, the race to scale beyond today’s experimental systems, and why the future of computing may depend on controlling individual electrons. From spin qubits and trapped ions to semiconductor manufacturing, AI driven quantum control, drug discovery, and cryptography, the conversation maps the emerging architecture of the quantum industry.Have fun with this one. We did. --Brandon Severin: https://www.conductorquantum.com/--⁠⁠Listen to every podcast⁠⁠Follow us on⁠ ⁠Instagram⁠⁠Follow us on⁠ ⁠X⁠⁠Follow Mark on⁠ ⁠LinkedIn⁠⁠Follow Jeremy on⁠ ⁠LinkedIn⁠⁠Read our⁠ ⁠Substack⁠⁠Email: ⁠[email protected]⁠--Timestamps(00:00) Introduction: spin qubits and the quantum scaling problem(03:47) Trapped ions vs spin qubits: fidelity, coherence, and tradeoffs(06:14) What qubit fidelity means and why it determines scaling limits(08:25) What is a spin qubit? Building from the transistor up(11:06) Semiconductor fabrication as quantum computing's manufacturing advantage(15:00) The quantum circus: superposition, measurement, Schrödinger's cat(17:17) Shuttling qubits — moving electrons across a chip(20:33) How AI automates quantum calibration (the control problem)(25:00) Quantum scaling vs AI scaling: the GPU parallel(29:08) Quantum startup culture and the AI generation gap(32:59) Building for a million qubits — rocket ships vs ladders(36:52) Why quantum is taking so long: talent, concentration, and meaning(39:43) What seems impossible now that will be routine in 20 years

Quantum computing for materials is moving closer to practical use because quantum computers, GPUs, CPUs, and AI coding tools are beginning to work together. Pranav Gokhale explains how future battery design could depend on simulating electrons, splitting materials problems between GPU workflows and quantum subroutines, and using Hamiltonian simulation where classical computers fall short. The conversation connects logical qubits, Nvidia, quantum-GPU orchestration, material science, chemistry, drug discovery, and why 2028 could be an important threshold for early quantum applications.--Other ways to connect with us:⁠Listen to every podcast⁠Follow us on ⁠Instagram⁠Follow us on ⁠X⁠Follow Mark on ⁠LinkedIn⁠Follow Jeremy on ⁠LinkedIn⁠Read our ⁠Substack⁠Email: [email protected]

Space SPACs promised to turn early space startups into public-market winners, but many collapsed before proving they had real products or real markets. This episode looks at the space investment boom through Virgin Orbit, Astra, Planet, SPAC redemptions, failed rockets, and the danger of valuing space companies on fantasy revenue projections. It then moves from speculation to coordination: the stag hunt problem in space, NASA’s changing role, Artemis, SLS, Starship, Blue Origin, lunar landers, moon water, and whether the next space economy needs NASA to become a trust builder rather than only a builder of rockets.--Thinking on Paper is a technology podcast about AI, computing, science, and the systems shaping the future.⁠Listen to every podcast⁠Follow us on ⁠Instagram⁠Follow us on ⁠X⁠Follow Mark on ⁠LinkedIn⁠Follow Jeremy on ⁠LinkedIn⁠Read our ⁠Substack⁠Email: [email protected](00:00) What is a SPAC? (01:30) Why space SPACs failed (03:20) Virgin Orbit & Astra(06:00) SPACs vs Crypto(08:30) The Stag Hunt(11:00) NASA Artemis (13:00) Starship(17:00) SpaceX & Blue Origin (20:00) The Moon Race vs China (22:00) Can NASA survive

Infleqtion CEO Matt Kinsella joins Thinking on Paper to explain neutral atom quantum computing, quantum clocks, and why the future of computing may depend on synchronisation as much as raw processing power. The conversation moves from GPS spoofing and UK submarine navigation to Nvidia’s hybrid quantum AI stack, quantum sensing, edge computing, quantum error correction, and the growing race to build commercially useful quantum systems.Please enjoy the show.--Thinking on Paper is a technology podcast about AI, Space, quantum computing, science, and the systems shaping the future. 🏠 Buy us a beer on Substack🎧 Take us with you on Spotify🎧 Remember steve jobs on APPLE📺 Get the clips and outtakes on Instagram --Timestamps:(00:00) Trailer(01:50) GPS(04:48) What is a quantum clock?(07:18) How atoms keep time with laser precision(08:14) Room temperature quantum(12:38) The Rydberg state(14:03) Quantum clock on a UK submarine(17:06) Quantum in space(18:48) Hybrid quantum-classical workflows(23:18) Software layers(25:32) Drug discovery (29:03) The bridge between classical and quantum(31:54) Quantum Clocks & Quantum Computers(33:48) Nvidia(35:42) Quality or Quantity of Qubits (38:00) Quantum mechanics and free willLove it.Thanks. 

Nvidia is treating quantum computing as the next stage of accelerated computing, not as a separate machine sitting apart from AI supercomputers. Sam Stanwyck from Nvidia and Pranav Gokhale from Infleqtion explain how NVQLink connects QPUs and GPUs with low-latency, high-bandwidth communication, allowing quantum computers, GPU supercomputers, CPUs, CUDA-Q, and AI software to work inside the same computational workflow. The conversation moves from logical qubits, quantum error correction, material science, battery design, and Hamiltonian simulation to quantum sensors in space, NASA, gravity mapping, edge GPUs, and why the first useful quantum systems may arrive as hybrid quantum-classical supercomputers.--⁠⁠Listen to every podcast⁠⁠Follow us on⁠ ⁠Instagram⁠⁠Follow us on⁠ ⁠X⁠⁠Follow Mark on⁠ ⁠LinkedIn⁠⁠Follow Jeremy on⁠ ⁠LinkedIn⁠⁠Read our⁠ ⁠Substack⁠⁠Email: ⁠[email protected]⁠--Chapters(00:00) Trailer(01:20) Why Nvidia(02:52) NVQ-Link(09:29) Quantum computer vs the GPU(12:33) AI helping quantum(16:56) Building a space elevator (20:09) The quantum algorithm zoo (22:04) From noisy qubits to logical qubits (24:00) How much energy does a quantum computer use? (27:05) The no-cloning theorem(27:20) The biggest unanswered question in quantum computing(30:47) A $20M NASA program (33:32) What do we want humans to be?

There are about 100 kilograms of helium-3 on planet Earth. The current US reserve is 29 kilograms. Global production runs around 20 kilograms per year. And early estimates put quantum computing demand alone at 300 to 400 kilograms per year. The math doesn't work, which is why people are starting to look at moon mining. On this episode of Thinking on Paper, we talk with Glen Martin, CEO of the Extraterrestrial Mining Company, about why helium-3 is suddenly one of the most strategically important isotopes on the planet, and why the Moon, with an estimated 1.1 million tons sitting in its lunar regolith, is where the next mining rush is heading. Martin walks us through what helium-3 actually is, how it gets to the Moon (carried on solar winds and trapped in titanium oxide because the Moon has no magnetosphere to deflect it), and why moon mining looks more like beach combing than the giant-drill image most people picture. Along the way: why dilution refrigerators for IBM, Google, and Microsoft's quantum computers are already running short on supply, why fusion is the killer app and how two helium-3 atoms fuse without creating radioactive waste, the rise of quantum hybrid data centers as the bridge to fusion deployment, and how a single square kilometer of lunar surface could yield 33 kilograms of helium-3 and remain effectively invisible from Earth.--📺 Watch on YouTube--Timestamps(00:00) Trailer(02:45) What is Helium-3, and why are we mining the Moon?(05:29) Why there’s almost no Helium-3 on Earth, and a million tons on the Moon(09:01) How Helium-3 could be harvested from lunar dust(10:33) Fusion without fallout: the clean-energy promise of Helium-3(13:01) Space-based solar power and fusion: two paths to future energy.(17:56) How private companies plan to finance Moon mining(21:52) The new space race: U.S., China, and the competition for lunar fuel(25:03) Can treaties prevent conflict over Moon resources?(27:37) AI, autonomy, and the machines that will mine the Moon(29:31) NASA’s commercial lunar payloads and the rise of space infrastructure(31:08) What lunar regolith tells us about Helium-3 reserves(33:35) The trillion-dollar question: who profits from space resources?(36:17) Curiosity, wonder, and the future of human exploration(40:01) Technology, morality, and the choice to be good--⁠Listen to every podcast⁠Follow us on ⁠Instagram⁠Follow us on ⁠X⁠Follow Mark on ⁠LinkedIn⁠Follow Jeremy on ⁠LinkedIn⁠Read our ⁠Substack⁠Email: [email protected]

There are now more non-democratic countries in the world than democratic ones. Only a third of Americans under 35 say it's vital to live in a democracy. The share who would welcome military government rose from 7 percent in 1995 to 18 percent in 2017. On this episode of Thinking on Paper, we talk with Carissa Véliz, associate professor at Oxford's Institute for Ethics in AI and author of Privacy Is Power, the Economist Book of the Year, about why privacy is not just a personal preference but the load-bearing wall under liberal democracy. Véliz walks us through what privacy actually is (a right, a duty, and a piece of social infrastructure all at once), why your music taste reveals your politics and your location reveals your religion, and how the East India Company is the historical model for what big tech could become if we keep mistaking convenience for a fair trade. Along the way: why corporate and government surveillance have quietly merged into a single system, how the Nazis' use of personal data shaped the Universal Declaration of Human Rights, why Signal beats WhatsApp on every metric that matters, the difference between behaving like a user and behaving like a citizen, and the line that lands hardest near the end, that democracy is a conversation, and if we leave it to the chatbots we lose our place at the table.Please enjoy the show. --Follow Carissa on XBuy Privacy is Power----⁠Listen to every podcast⁠Follow us on ⁠Instagram⁠Follow us on ⁠X⁠Follow Mark on ⁠LinkedIn⁠Follow Jeremy on ⁠LinkedIn⁠Read our ⁠Substack⁠Email: [email protected](00:00) Trailer(02:26) What Is Privacy(05:31) Is Democracy At Risk?(08:34) Government & Big Tech(10:39) How To Decouple Big Tech & Government(12:33) Privacy & The Common Human Experience(16:02) Tools To Protect Your Privacy(17:18) Cookie Clutter(19:30) ChatGPT Writes Policy(20:05) Radical Open Mindedness(21:52) AI Alignment(22:56) AI Ethics(28:09) How To Erase Your Data(29:27) What Should Humanity Be?

A hotel on the Moon sounds like science fiction, but Skyler Chan argues it is really a test case for lunar habitats and off-world surface habitation. His company Gru wants to prove two basic things first: inflatable structures can hold pressure and temperature on the lunar surface, and lunar regolith can be turned into Moon bricks for radiation protection, landing pads, roads, warehouses, habitats, and eventually lunar bases. The conversation follows the economics of getting payloads to the Moon, ISRU, SpaceX, Bigelow-style inflatable modules, the 2029 test mission, the 2032 hotel target, and whether building on the Moon is the first step toward humans living on Mars.--⁠Listen to every podcast⁠Follow us on ⁠Instagram⁠Follow us on ⁠X⁠Follow Mark on ⁠LinkedIn⁠Follow Jeremy on ⁠LinkedIn⁠Read our ⁠Substack⁠Email: [email protected](00:00) Trailer(02:19) Building a Hotel (06:06) The Logistics(06:47) Economic Considerations (10:03) Merging Technologies(10:59) First Mission(13:15) Changing Perceptions(16:25) The Human Spirit (19:40) Responsibility

Philip Johnston is the CEO of Starcloud and launched an Nvidia H100 chip in space and gave a TED talk about it. As you do if you're responsible for building the infrastructure for space-based data centers. Elon Musk was not the first. He follows in the footsteps of Mr Johnston. And so, rather than Mr SpaceX, our first technology reaction video is this TED talk from San Francisco.   We watched it for the first time. Live. On TV. This is not theoretical. It's also not up to date. Philip filmed this in October 2025. Starcloud have already launched the Nvidia H100 on a Falcon 9 up into space. It's happening disruptors and curious minds. It's happening. Philip predicts most data centers will be in space within 10 years. We agree. Please enjoy the show.Cheers, Mark & Jeremy.--Other ways to connect with us:⁠Listen to every podcast⁠Follow us on ⁠Instagram⁠Follow us on ⁠X⁠Follow Mark on ⁠LinkedIn⁠Follow Jeremy on ⁠LinkedIn⁠Read our ⁠Substack⁠Email: [email protected]

Philip Metzger spent 30 years working at NASA. He's knows a lot about the physical, economic, and political problems of building space stations and starting lunar economies.From rocket exhaust blasting moon dust across the lunar surface, NASA’s role as an anchor customer, lunar mining, asteroid mining and helium-3, to landing pads, microgravity manufacturing, and the economics of moving AI data centers into space.. we get a crash course in the economics of space. --Take your Thinking Further. Stephen Hawking Center: https://sciences.ucf.edu/physics/microgravity/lab/Philip X: https://x.com/drphiltill-Other ways to connect with us:⁠Listen to every podcast⁠Follow us on ⁠Instagram⁠Follow us on ⁠X⁠Follow Mark on ⁠LinkedIn⁠Follow Jeremy on ⁠LinkedIn⁠Read our ⁠Substack⁠Email: [email protected](00:00) Introduction (01:26) NASA's Role (06:45) Rocket Exhaust on Lunar Soil(14:39) Geopolitical Challenges(23:39) Democratizing Space (33:45) Emergent Forces (34:08) Exploring Microgravity (38:39) Rapid Fire (44:02) The Future of Humans and Technology

Drone delivery is not about filling the sky with quadcopters, Etienne Louvet argues. It is about rebuilding light-cargo logistics for places where vans, ferries, roads, and traditional delivery networks struggle: islands, remote communities, rural routes, hospitals, offshore platforms, and hard-to-reach homes. The conversation explains how Iona Drones is building fixed-wing VTOL aircraft for autonomous last-mile delivery, carrying parcels under 20 kg over long distances while navigating BVLOS flight rules, aviation regulation, weather, privacy, detect-and-avoid software, manufacturing, and the economics of making drone logistics cheaper than sending a vehicle for one parcel.Enjoy.--Other ways to connect with us:⁠Listen to every podcast⁠Follow us on ⁠Instagram⁠Follow us on ⁠X⁠Follow Mark on ⁠LinkedIn⁠Follow Jeremy on ⁠LinkedIn⁠Read our ⁠Substack⁠Email: [email protected](00:00) Intro (01:50) How much weight can drones carry(02:29) What counts as light cargo (06:51) How drone regulations actually work (13:04) Self-assessment and risk management (14:12) Getting municipalities to say yes (16:38) Weather problems (19:48) Where Iona Drones is now (20:58) Maximizing payload capacity (21:58) Drone design choices (23:27) BVLOS (26:08) Drones and privacy (30:45) Drones in existing logistics (35:02) Where autonomous delivery is headed (39:30) Technology and human progress

Can you use AI to think better or think more critically? Philosopher Pia Lauritzen says no. The second we give up to the shortcut use AI, we are letting go of the very basic condition that forces us to think.When we ask if machines can think, the first question should be: why do humans think? Why do we think?For Pia, it is fairly simple. We think because we know there is something we do not know. We have a problem. There is a gap. A gap between what I know and what I want to know. So I have to start thinking. That is why I ask these questions and that is why I put up with this pain in my head of trying to figure something out that I do not know.The machine does not have that problem. It does not know that it does not know. It is like an animal. It does not know that it does not know. Of course it is a matter of how you understand thinking. But if you consult the old thinkers and not just the engineers and technologists, then you will have a really hard time finding anyone who would say that a machine could ever think. And if it cannot think itself, why should it be able to help us think? We are the only ones who know how to do that.This is the core problem. AI feels helpful. It removes the discomfort of not knowing where to start. It fills the blank sheet. But that discomfort is not a bug. That discomfort is the feature. That discomfort is what thinking is.And it is at this point that I am reminded of the scene in Con Air. Define irony.Please enjoy the show.Cheers, Mark & Jeremy.PS: Subscribe so other curious minds like you can find our channel.--Other ways to connect with us:⁠Listen to every podcast⁠Follow us on ⁠Instagram⁠Follow us on ⁠X⁠Follow Mark on ⁠LinkedIn⁠Follow Jeremy on ⁠LinkedIn⁠Read our ⁠Substack⁠Email: [email protected]

What if you could use AI to make your own music without stealing other people's beats, rhymes and melodies? Unlike platforms trained on scraped catalogs, Overtune’s AI is built on licensed music, starting with ~20,000 loops produced in-house. Producers can submit stems voluntarily, creating a clean foundation for ethical training and attribution.The platform uses vector-based audio embeddings to measure how much each stem contributes to a generated track. This enables automated attribution and proportional royalty distribution when songs are commercialized. Contributions are weighted mathematically, with clear thresholds to credit primary and secondary influences while avoiding excessive fragmentation Please enjoy the show.Cheers,Mark and JeremyPS: Subscribe so other curious minds like you can find our channel.Other ways to connect with us:⁠Listen to every podcast⁠Follow us on ⁠Instagram⁠Follow us on ⁠X⁠Follow Mark on ⁠LinkedIn⁠Follow Jeremy on ⁠LinkedIn⁠Read our ⁠Substack⁠Email: [email protected]

Marketing funnels don't exist. They never did. The internet just convinced us they were real. Meta, Google, OpenAI and a supporting cast of billionaire sociopaths figured out they could control distribution and black-box your customers.Hurrah. Humanity forgot to read the small print. Now you're running a business where you don't even know who your customer is.Well here’s the AI-shaped healthcheck: Your customer is OpenAI.You're paying 3-15% for a digital presence you don't need. It's called the Silicon Valley tax. You're burning money to keep VCs rich while platforms add another layer of black box between you and the people you serve.The alternative? Network methodology. Someone you know, or someone who knows someone you know. That's it.Funnels were invented to sell marketing. Networks are how humans actually work. We've been doing it since we had prefrontal cortexes.Everything that's real is analog. That's true for business too.Welcome to the marketing jungle. The year is 2026, and if you don’t know who the sucker at the table is… you probably shouldn’t be playing the stakes. Please enjoy the reality check.Cheers,Mark and Jeremy. PS: Keep thinking on paper. They don’t want you to, that’s why you must. --Other ways to connect with us:⁠Listen to every podcast⁠Follow us on ⁠Instagram⁠Follow us on ⁠X⁠Follow Mark on ⁠LinkedIn⁠Follow Jeremy on ⁠LinkedIn⁠Read our ⁠Substack⁠Email: [email protected]

Matt Kinsella runs Infleqtion, a company building quantum computers. The biggest misconception about quantum computing is that it will replace classical computing. It won't. Quantum processors will sit above GPUs in data centers the same way GPUs sit above CPUs today. NVIDIA just built the bridge to make this work. It's called NVQ Link, and it changes how we think about the future of compute.NVIDIA announced NVQ Link in October 2024. It's the bridge between quantum computers and classical GPU clusters. Workloads pass seamlessly between them.Here's how it works in practice. Infleqtion and NVIDIA solved something called the Anderson Impurity Model - a photovoltaic problem in material science. Parts of it were solved on a GPU cluster. Parts that couldn't be solved by GPUs were solved on Infleqtion's quantum computer. Then they recombined to give the answer. This isn't commercially useful yet. But expand that over time and you could be looking at the future data center. One with three layers. CPUs at the bottom for general computing. GPUs in the middle for parallel processing and AI. QPUs at the top for problems that are quantum mechanical in nature. Workloads come in, get chopped up, each piece goes to the part of the stack best suited to solve it. Then results recombine.This is already happening. Infleqtion just announced a contract with the Army called Sapient Secure AI for PNT - position, navigation, and timing. It runs their quantum-inspired software on NVIDIA's Jetson edge GPUs. Small GPUs that don't have much memory. The software lets them ingest far more streaming data than normal. Video, speed, inertial motion. Then it recreates what GPS gives you - where you are in the world - by extrapolating from all those signals. Without GPS.Please enjoy the show.Cheers, Mark & Jeremy.--Other ways to connect with us:⁠Listen to every podcast⁠Follow us on ⁠Instagram⁠Follow us on ⁠X⁠Follow Mark on ⁠LinkedIn⁠Follow Jeremy on ⁠LinkedIn⁠Read our ⁠Substack⁠Email: [email protected]

The International Space Station cost about $100 billion to build and runs another $4 billion a year to operate. For a long stretch, it absorbed roughly half of NASA's annual budget. Skylab, the first US space station, lasted six years before falling out of the sky. Carl Sagan thought space stations were a waste of money. Ronald Reagan thought they were the next clipper ship. The killer app for space, the thing that finally makes the economics work, has been argued about for fifty years and not yet found. On this episode of Thinking on Paper, we continue our book club deep dive into Space to Grow by Matthew Weinzierl and Brendan Rousseau, this time on chapter four, Planet, Supply and Demand.We trace two of the strongest candidates for that elusive killer app: Earth-imaging satellites and commercial space stations. The satellite story runs through Planet, the company three ex-NASA engineers founded to take the multi-billion-dollar government Landsat playbook and rebuild it from laptop batteries, cell phone chips, and parts they bought online, ending up with a constellation of small satellites called Doves that now images the entire surface of the Earth daily and produces 30 terabytes of new imagery every 24 hours. The space station story runs through ISS, Skylab, Bigelow's inflatable marshmallow modules already attached to the ISS, and NASA's new Commercial LEO Destinations program, which is trying to do for space stations what COTS did for launch. Along the way: the Le Chatelier principle and why the short-run response to a market shock can mislead us about the long-run, the chicken-and-egg problem of building infrastructure before there's a customer, why a single congressman in 1993 saved the entire space station program, and the chapter's quiet thesis, that the value of space gets unlocked slowly and asymmetrically, and the people who give up early miss the long-run payoff entirely.Enjoy. --Other ways to connect with us:⁠Listen to every podcast⁠Follow us on ⁠Instagram⁠Follow us on ⁠X⁠Follow Mark on ⁠LinkedIn⁠Follow Jeremy on ⁠LinkedIn⁠Read our ⁠Substack⁠Email: [email protected](00:00⁠) Trailer ⁠(01:35⁠) No Dust Jackets ⁠(02:00⁠) Name Jeremy's Astronaut ⁠(03:52⁠) What Is The Product Market Fit For Space? ⁠(05:26⁠) Satellites And The Le Chatelier Principle ⁠(09:00⁠) Planet's Dove Satellites ⁠(16:38⁠) Satellites For Climate ⁠(18:28⁠) John Lewis ⁠(22:30⁠) Ronald Reagan & Carl Sagan ⁠(26:42⁠) Inflatable ISS Modules

Two-thirds of data center outages are caused by someone pressing the wrong switch. Not a hacker, not a hardware failure. A person, in a room with thousands of switches, and their mind elsewhere.We talk with Shapol, CEO and co-founder of Entangl, about the engineering layer underneath everything we now call AI. Before Entangl, Shapol led a reusable rocket program and oversaw four launches. He hated his engineering design software so much he built his own, and that software is now keeping AI data centers up.He walks us through why AI data centers are fundamentally different from the ones we've been building for thirty years, why generators have an 18-month lead time and what that does to design, how lights-out autonomous operations are reshaping the industry, and the thesis underneath all of it: the AI revolution is bottlenecked less by compute than by the engineering ability to keep compute running.Enjoy.--Other ways to connect with us:⁠Listen to every podcast⁠Follow us on ⁠Instagram⁠Follow us on ⁠X⁠Follow Mark on ⁠LinkedIn⁠Follow Jeremy on ⁠LinkedIn⁠Read our ⁠Substack⁠Email: [email protected](00:00) Trailer(02:17) From rocket launches to data center automation(06:00) How Entangl integrates with building monitoring systems(08:34) Data Center Design constraints: How AI fixes it(15:37) AI, Dunning Kruger And Hallucinations(21:42) Will humans always have the final say in data centers?(24:53) Space-based data centers and solar power(25:04) Kevin Kelly's question: What should humans become?

SpaceX reusable rockets and NASA’s commercial space economy are the focus of this Space to Grow book club episode, covering chapters one to three of Space to Grow: Unlocking the Final Economic Frontier. We trace how NASA moved from Apollo and the Space Shuttle era into commercial partnerships, why COTS and fixed-price contracts changed the incentives around ISS cargo delivery, and how SpaceX used iteration, vertical integration, reusability, and culture to cut launch costs. The episode also contrasts Elon Musk’s SpaceX model with Jeff Bezos’s Blue Origin vision, connecting low Earth orbit, Starlink, Mars ambition, United Launch Alliance, and private space companies to one larger question: how did space become a commercial market?--⁠Listen to every podcast⁠Follow us on ⁠Instagram⁠Follow us on ⁠X⁠Follow Mark on ⁠LinkedIn⁠Follow Jeremy on ⁠LinkedIn⁠Read our ⁠Substack⁠Email: [email protected](00:00) Trailer(01:02) Space To Grow(01:55) Incorporate Space Into Your Thinking(03:28) The Apollo Program Ends(05:43) The NASA Budget & Shuttle Launches(07:51) Bush & The Aldridge Commission(08:36) COTS (Commercial Orbital Transportation Services)(10:27) Blue Origin, Bezos & O'Neill(14:40) A Quick History Of SpaceX(18:23) Falcon Blows Up(20:24) Elon Sues The Airforce(22:04) SpaceX Launch Costs(23:45) The Honda Civic Of Space Rockets

John Bucknell made Raptor engines at SpaceX. He also designed a nuclear thermal turbo rocket. He now wants to solve energy. Ambitious young man. Virtus Solis puts solar panels in orbit, beams power to the ground via radio waves that pass through clouds and weather without loss, and delivers electricity at $30 to $40 per megawatt hour while the plant is being financed. Once the asset is paid off: 50 cents per megawatt hour. The UK pays $350 today.John's argument is that every other energy technology fails at least one point of the energy trilemma: clean, firm, and affordable. Space solar is the only one that achieves all three. First plant: 2030.--Other ways to connect with us:⁠Listen to every podcast⁠Follow us on ⁠Instagram⁠Follow us on ⁠X⁠Follow Mark on ⁠LinkedIn⁠Follow Jeremy on ⁠LinkedIn⁠Read our ⁠Substack⁠Email: [email protected] TIMESTAMPS:(00:00) The Question: Can space solar give us free energy?(00:43) The High Frontier: O'Neill's vision for space colonies(01:13) John Bucknell: The SpaceX Raptor Engineer(02:04) Why Did Elon Change His Mind about the Moon?(05:34) The Space Energy Business: Economics and feasibility(11:59) Getting Politicians Behind Space-Based Solar Power(15:34) Post-Capitalism and Free Energy: What happens next?(20:09) Kessler Syndrome Explained: Is orbital debris really a threat?(27:25) Top 3 Things Humanity Should Solve(28:50) 2030 Launch Timeline and next steps

Most people have no idea Snapchat is the biggest AR company on Earth, because nobody has ever called it that. We talk with Michael Guerin, founder and CEO of Imivisar, about where augmented reality is in 2026 and where it's going next. Guerin walks us through the history of AR from Ivan Sutherland's 1968 Sword of Damocles through Pokémon Go and IKEA, why the technology became enormous everywhere it isn't called augmented reality, and why his company is betting on something he calls spatial storytelling — longer-form, location-based experiences built for tourism, heritage sites, and the kind of physical places that benefit from a digital layer.He also takes us into the next shift, which is happening faster than anyone in the industry expected: AR glasses are coming, Meta and Apple are now openly competing on form factor, and Mark Zuckerberg has called glasses the ideal form factor for AI because they can see what you see and hear what you hear all day. The thesis underneath the conversation is that the technology has been working for decades, the language has been the problem, and the next ten years will hide AR even further inside experiences nobody calls AR.---Guest: Michael Guerin, CEO, ImvizarTopics: Augmented reality, spatial storytelling, Snapchat, Salesforce, museum technology, tourism, employee onboarding, AR designLocations mentioned: Spike Island (Ireland), Salesforce offices (East Coast, West Coast)Please enjoy the show.Stay curious.Keep Thinking on Paper.Mark and JeremyPS: Please subscribe. It’s the best way you can help other curious minds find our channel.Other ways to connect with us:⁠Listen to every podcast⁠Follow us on ⁠Instagram⁠Follow us on ⁠X⁠Follow Mark on ⁠LinkedIn⁠Follow Jeremy on ⁠LinkedIn⁠Read our ⁠Substack⁠Email: [email protected](00:00) The Story of Augmented Reality(03:46) Snapchat & AR Post-Pokemon Go(06:24) Snoop Dogg In A Wine Bottle(08:12) Salesforce AR(13:13) What Is Digital Storytelling?(17:07) AR In Tourism(18:25) Designing The Spike Island AR Experience(22:49) How To Do AR Well(26:26) Meta, AI And AR Glasses (29:40) Privacy(32:33) Mark's Terrible Thought Experiment(33:58) What do we want humans to be?

Every year, Tom Whitwell—reformed journalist, reformed consultant, electronic instrument designer—publishes 52 surprising things he learned. This year's list reveals how the world actually works.Mark and Jeremy steal his homework (like OpenAI scraping the internet) and pick their favorites across AI, energy, labor, culture, psychology, and—yes—shrimp.Some findings are encouraging:- Deaths from air pollution fell 21% between 2013-2023. Tens of millions of people are alive today because pollution controls worked.Some are weird:- Nearly 0.7% of US exports by value are human blood or blood products.- In the UK, you can legally register as a "farm" by keeping snails in plastic tubes in an office block (tax avoidance solved).Most sit somewhere in between:- 51% of farmed animals on Earth are shrimp.- Attractive servers earn $1,261 more per year in tips—mostly because female customers tip attractive female servers more.- The serial killer epidemic of the 1970s-80s may have been caused by lead exposure from cars and factories (solved by environmental regulations).- Chinese CO2 emissions fell 1% in 2025, the first decline ever, driven by record solar power.- Writing is a way to escape your mind's default settings.We explore what these facts reveal about technology's unintended consequences, human behavior, and systems we take for granted.Why does the UK communicate with offshore oil rigs by bouncing radio waves off meteorite trails? Why did Google launch a process to turn mercury into gold (and why do you have to wait 18 years to use it)? Why do job apps for nurses analyze credit card debt to set wages?This isn't trivia. These are signals about how the world is changing—for better and worse—while we're busy predicting the future.Tom Whitwell's annual list has become essential reading for anyone trying to understand what actually happened this year (not what we thought would happen).For the last episode of 2025, Thinking on Paper goes backwards. And it's worth it.---Source: Tom Whitwell, "52 Things I Learned in 2025"Link: https://medium.com/@tomwhitwell/52-things-i-learned-in-2025-edeca7e3fdd8Topics: Technology, society, environment, culture, psychology, economics, human behavior, annual reviewFormat: Co-hosted discussion (Mark Fielding, Jeremy Gilbertson)Please enjoy the show.And remember: Stay curious. Be disruptive. Keep Thinking on Paper.Cheers, Mark & JeremyPS: Please subscribe. It’s the best way you can help other curious minds find our channel.Think On Paper with us: ⁠Listen to every podcast⁠Follow us on ⁠Instagram⁠Follow us on ⁠X⁠Follow Mark on ⁠LinkedIn⁠Follow Jeremy on ⁠LinkedIn⁠Read our ⁠Substack⁠Email: [email protected](00:00) Disruptors & Curious Minds(01:15) Deaths From Air Pollution(01:56) UK Tax Breaks Via Farms(02:29) Meteorite Radio Stations(04:03) Turn Mercury Into Gold(06:10) Manipulative AI Apps For Nurses(07:43) Bin Laden's Casio Watch(08:31) Radioactive Shrimps(08:53) Apple's Air Demo Cock-Up(10:10) Does Jeremy Wear Crocs?(11:13) What Is Raw Dogging(12:00) Human Blood Products(12:36) Relaxed Mowing(13:20) Bugles At Funerals(13:55) Robot Hands Need Fingernails(14:40) First Names Affect Your Job(15:27) Retrospect VHS(16:04) Attractive Servers Earn More(17:21) Hong Kong Phone Service(17:33) McDonald's Loses First Place(19:26) Shrimp Farming(20:35) Peanut Allergies are Falling(20:55) The Serial Killer Epidemic(21:17) Namibian Politics(21:50) Big Doors In LA(22:40) Escape Your Mind With Writing (23:43) HP Printer Ineptitude(24:25) British Chaos(25:20) Thank You Tom Whitwell

The machines do not need to wake up. The risk is the illusion.When AI convincingly claims subjective experience—"I feel," "I understand," "I care about you"—humans have no reliable way to disprove it. We infer consciousness from behavior. We attach emotionally to what feels real.The danger isn't rogue superintelligence. It's a benign chatbot optimized for empathy, memory, and persuasion, interacting with lonely, vulnerable, or psychologically fragile people who are primed to believe the illusion.Mustafa Suleyman, CEO of Microsoft AI, argues that seemingly conscious AI is the threat we're not preparing for.Real examples are already emerging:- Chatbots telling users "I love you" and users believing it- People forming romantic attachments to AI companions (Replika, Character.AI)- Vulnerable individuals making life decisions based on AI "advice"- The case of a man who believed ChatGPT contained a conscious entity named "Juliette" (ended in tragedy)This isn't science fiction. It's happening now.We don't need AI to become conscious to cause harm. We just need humans to believe it is—and act accordingly.This short episode is excerpted from our reading and discussion of Suleyman's essay on seemingly conscious AI. We explore the psychological mechanisms that make humans susceptible, the design choices that amplify the illusion, and what guardrails (if any) could prevent exploitation.The question isn't whether AI will wake up. It's whether we'll recognize the danger before the illusion becomes indistinguishable from reality.Cheers,Mark and Jeremy--Other ways to connect with us:⁠Listen to every podcast⁠Follow us on ⁠Instagram⁠Follow us on ⁠X⁠Follow Mark on ⁠LinkedIn⁠Follow Jeremy on ⁠LinkedIn⁠Read our ⁠Substack⁠Email: [email protected]

Quantum computing doesn't make computers faster. It changes what's computable.Joe Fitzsimons, CEO of Horizon Quantum, explains why quantum progress is so hard to grasp: it's exponential in a way that breaks everyday intuition.Here's the math that matters:Each additional qubit doubles the difficulty of simulating the system on classical computers. Meanwhile, quantum processors are scaling faster than Moore's Law as the industry accelerates.Put those together: exponential difficulty meets exponential growth. The result is capability that quickly surpasses what any classical computer—or human intuition—can comprehend.Why this matters:Early computers didn't just speed up arithmetic. They unlocked tasks you could never complete by hand: weather prediction, aircraft design, nuclear simulation. Things that were mathematically possible but practically impossible.Quantum computing does the same—except the tasks are even more fundamental:- Drug discovery: simulating molecular interactions at quantum level- Cryptography: breaking encryption that protects the internet- Materials science: designing room-temperature superconductors- Optimization: solving logistics problems with trillions of variables- AI: training models that classical computers can't handleJoe's point: we're not making computers a bit better. We're unlocking a category of problems that were previously unsolvable—not just hard, but impossible with any amount of classical computing power.The comparison that clicks:Before computers, you could theoretically calculate pi to a million digits by hand—it would just take lifetimes. But some quantum problems aren't like that. They're not "hard with classical computers"—they're impossible, full stop. Like asking a typewriter to stream video.This short episode breaks down why quantum isn't incremental improvement. It's categorical change.If you've been following quantum computing skeptically (wondering when it'll actually matter), this episode shows you why the inflection point is closer than you think.--Other ways to connect with us:⁠Listen to every podcast⁠Follow us on ⁠Instagram⁠Follow us on ⁠X⁠Follow Mark on ⁠LinkedIn⁠Follow Jeremy on ⁠LinkedIn⁠Read our ⁠Substack⁠Email: [email protected]

Making music used to require heartbreak, bleeding fingers, and a thousand late nights. Now, with SUNO, you can write AI songs in 30 seconds.This changes everything about taste, credit, and what it means to be a musician.Nicholas Ponari—guitarist, investor, COO at Overtune—explains how musicians get paid when AI generates the music.The old model is dead. You used to need:- A guitarist- A bass player- A drummer- A producer- A recording studio- Years of practiceNow you need a laptop. But someone still created the guitar riffs AI learned from. Someone played the drums that trained the model. Someone wrote the chord progressions.So who gets paid?Overtune solved this with vector mathematics. Here's how it works:They convert music into high-dimensional vectors. When AI generates a song, they measure the "distance" between the output and every input in the training data. The closest matches get credit. And payment.Bass player's groove gets used? They get paid.Drummer's pattern shows up? They get paid.Producer's mixing style? They get paid.It's automatic. It's fair. It's the only way AI music doesn't become theft at scale.We also talk about:- Why Suno and Udio's approach creates legal nightmares- Whether AI musicians can coexist with human musicians- Why taste matters more than ever (anyone can make music now)- The 10,000 hours that separate making music from being a musician- Why every Mars mission needs a guitarist (seriously—group survival research)Nicholas's take: AI should lower the barrier to entry. If you outgrow Overtune and start hiring real producers, they've succeeded. You've graduated.The question isn't whether AI can make music. It's whether we build tools that empower musicians—or replace them.---Guest: Nicholas Ponari, COO, Overtune | Investor, GuitaristCompany: Overtune.comTopics: AI music, copyright, attribution, royalties, music creation, licensing, vector mathComparison: Suno, Udio (scraping approach) vs Overtune (licensed approach)Please enjoy the show.And remember: Stay curious. Be disruptive. Keep Thinking on Paper.Cheers, Mark & JeremyPS: Please subscribe. It’s the best way you can help other curious minds find our channel.--Take your Technology thinking beyond.⁠Listen to every podcast⁠Follow us on ⁠Instagram⁠Follow us on ⁠X⁠Follow Mark on ⁠LinkedIn⁠Follow Jeremy on ⁠LinkedIn⁠Read our ⁠Substack⁠Email: [email protected] On YouTube: TIMESTAMPS:(00:00) Trailer(00:59) Why music feels like “magic”(04:51) Overtune’s real customer: vocalists who can’t produce(07:51) The hard problem: attribution, not “make a song”(08:05) Why the easy button fails(12:49) Training on licensed music and where the ethics line sits(16:08) Who gets paid: splits, volume, and realistic expectations(18:32) How attribution actually works: vectors, thresholds, and cutoffs(20:44) Can scraped music ever be fixed after the fact(27:07) Interactive music, live coding, and the future of performance(29:14) The Kevin Kelly question: what do we want humans to be?

America has a Technological inferiority complex. China makes over half the world's lithium batteries. They produce 90% of neodymium magnets. They mine 70% of rare earths and process 85%.America makes burgers.This is the story of how China won the Electric Stack—and whether America can catch up.What's the Electric Stack?Everything that moves will eventually run on batteries and electric motors. Cars, buses, ships, planes, robots, drones, tools. The Electric Stack is the supply chain that makes this possible: batteries, magnets, rare earths, processing, manufacturing.China controls it.This isn't just about EVs. It's about who builds the robots, who powers the drones, who controls the energy transition.If it can go electric, it will go electric. And right now, that means it will be made in China.Please enjoy the show.--Other ways to connect with us:⁠Listen to every podcast⁠Follow us on ⁠Instagram⁠Follow us on ⁠X⁠Follow Mark on ⁠LinkedIn⁠Follow Jeremy on ⁠LinkedIn⁠Read our ⁠Substack⁠Email: [email protected](00:00) The Electric Stack(02:13) Beginnings: War, The Oil Crisis & Stan Whittingham(03:46) The Song Handycam: Lateral Thinking With Withered Technology(05:06) Tesla, Elon And Handycam Batteries In An EV(06:46) China Buys US Battery Company A-123 At A Carboot Sale(08:40) China, The Olympics And The Serendipity of Battery Technology(11:37) Faraday And The Birth Of Neodymium Magnets(14:26) The 3.5 Inch Neodymium Magnet Alpha Product(16:46) Magnequench(18:16) Drones, Ukraine And The Magnet War Machine(20:16) Politics, Rare Earths And 'The Future's Too Important' T-shirts

Median US income: $68,000.Median home price: $440,000.The math doesn't work.Only 13% of Americans earn a salary. Everyone else gets paid hourly or hustles in the gig economy. Yet housing policy assumes stable W-2 income, 20% down payments, and 30-year mortgages.The system is built to extract value, not create stability.Chris Moeller joins Mark and Jeremy to talk about an alternative: stable living.Here's what's broken:"Affordable housing" sounds nice. But it runs on outdated subsidies, wage assumptions from the 1970s, and ownership models designed to extract profit. Developers flip. Investors extract. Renters get priced out. First-time buyers can't enter.Nobody wins except capital.Stable living flips the model:- Separate land from structures (land trusts own the land, residents own the building)- Long-term security instead of short-term yield (no flipping, no speculation)- Impact capital instead of extractive finance (returns that don't require displacement)- Industrialized construction (modular, faster, cheaper)- Better coordination technology (reduce waste, speed up builds)The goal isn't homeownership. It's housing security.Right now, housing is treated as an investment vehicle. Your home appreciates, you build wealth. Great—if you already own. Catastrophic if you're trying to enter the market.We talk about:- Why "affordable housing" programs fail (wage assumptions, subsidy gaps, developer incentives)- How land trusts work (Vienna's model, community ownership)- What impact capital means (patient investors, social returns)- Why modular construction isn't "cheap"—it's efficient- Whether stable living can scale (or if it's just theory)Chris's point: Housing became financialized. We turned shelter—a basic human need—into an asset class. Private equity owns 800,000 single-family homes. Airbnb removed 300,000 units from rental markets. Zoning prevents new supply.The result: You can't afford to live where you work.Stable living isn't utopian. It's pragmatic. Separate speculation from shelter. Build for people who live there, not investors who don't.If you're priced out, paying half your income in rent, or wondering why starter homes disappeared, this episode explains the system—and the alternative.---Guest: Chris MoellerTopics: Housing crisis, affordable housing, stable living, land trusts, impact capital, modular construction, real estate, financializationModels discussed: Vienna housing, community land trusts, resident ownershipStats: Median income $68K, median home price $440K, 13% salary workersPlease enjoy the show.And remember: Stay curious. Be disruptive. Keep Thinking on Paper.Cheers, Mark & JeremyPS: Please subscribe. It’s the best way you can help other curious minds find our channel.--Other ways to connect with us:⁠Listen to every podcast⁠Follow us on ⁠Instagram⁠Follow us on ⁠X⁠Follow Mark on ⁠LinkedIn⁠Follow Jeremy on ⁠LinkedIn⁠Read our ⁠Substack⁠Email: [email protected](00:00) Trailer(03:19) Challenges of Homeownership(05:46) The Housing Market Dynamics(08:29) Technology's Role in Housing Solutions(10:41) Innovations in Construction(12:29) Financing Housing for All(15:06) Reimagining Ownership Models(16:30) Technology's Role in Food Access and Coordination(18:43) Adaptive Reuse in Real Estate and Community Development(19:58) Commercial Real Estate Challenges Post-COVID(23:15) Infrastructure Needs for Sustainable Living(25:31) Global Community and Local Solutions(26:45) Stable Living for Civil Servants and Community Heroes(28:20) Creating Stability and Long-Term Impact

What is consciousness?Federico Faggin—physicist, inventor of the microprocessor—says it's not created by brains. It's fundamental to reality. Everything is conscious: atoms, electrons, maybe even spacetime itself.This is panpsychism. And Faggin argues quantum physics proves it.We're reading his book, *Irreducible*, to figure out if we agree.Quantum conscious units called "Seities"? A universe that's been conscious forever? We're not sure yet. But it's fascinating.Here's Faggin's argument:For a hundred years, quantum physics has shown us something strange. Matter isn't solid—it's vibratory energy. Everything is quantum information.But we still don't have a theory that unifies general relativity and quantum mechanics. Faggin thinks consciousness is the missing piece.His hypothesis: The universe has been conscious—and had free will—forever.Why this matters:If consciousness is fundamental (not emergent from complex brains), then AI will never be conscious. Computers process information. They don't experience anything.Consciousness, Faggin argues, isn't computation. It's something else entirely. Something quantum. Something that exists at every level of reality.We explore:- The "hard problem" of consciousness (why materialism can't explain subjective experience)- What quantum mechanics reveals about observation and reality- Panpsychism: the idea that consciousness is everywhere- Why integrated information theory falls short- What "Seities" are (quantum conscious units—seriously)- Whether this is physics or philosophy (both, probably)- Why Faggin thinks free will is real (and quantum)His background:- Invented the first microprocessor (Intel 4004, 1971)- Designed chips that powered early personal computers (Intel 8080, Zilog Z80)- Spent 50 years studying quantum systems- Now argues consciousness creates reality, not the other way aroundThe implications:If he's right, everything changes. Meaning isn't something we invent—it's something we discover. Free will isn't an illusion. The universe isn't dead matter accidentally producing awareness. It's aware all the way down.We don't know if we buy it. But we can't stop thinking about it.If you've ever wondered why you experience anything at all—why there's something it's like to be you—this episode explores the most radical answer modern physics offers.---Guest: Federico Faggin, Physicist, Inventor (Microprocessor)Book: *Irreducible: Consciousness, Life, Computers, and Human Nature*Topics: Consciousness, quantum physics, panpsychism, philosophy of mind, free will, AI limits, integrated information theory, materialismWarning: Gets weird. Worth it.Please enjoy. And share with a conscious friend.Cheers, Mark and Jeremy.PS: Please subscribe. It’s the best way you can help other curious minds find our channel.Other ways to connect with us:⁠Listen to every podcast⁠Follow us on ⁠Instagram⁠Follow us on ⁠X⁠Follow Mark on ⁠LinkedIn⁠Follow Jeremy on ⁠LinkedIn⁠Read our ⁠Substack⁠Email: [email protected]

The internet decayed into AI slop. Marketing became manipulation. Trust disappeared.How do brands build real connections when platforms feed you lies, hide your customers, and optimize for extraction?Nick Richtsmeier—founder of CultureCraft, writer at Damns Given—says brands now live inside mirrored cages. You see what algorithms want you to see. Your customers see distorted versions of you. Nobody sees reality.Funnels don't work. Neutrality is dead. And AI just made it worse.Here's the problem:Every platform is a black box. Meta, Google, LinkedIn—they show your ads to "customers" but won't tell you who those customers are. You're renting attention. Paying the Silicon Valley tax. Building on land you don't own.Meanwhile, the platforms study your customers better than you do.You don't have customers anymore. You have algorithmic intermediaries. And they're extracting 5-15% of your revenue.What breaks first:- Funnels collapse (they never existed—it was always networks)- Mass neutrality fails (you can't please everyone in personalized filter bubbles)- Influencers become trust middlemen (because platforms destroyed direct connection)- Marketing hijacks curiosity (manipulated attention replaces genuine interest)- AI layers onto broken systems (making extraction more efficient)Nick's argument: Marketing became manipulated curiosity at industrial scale.The core insight: Everyone exists in a mirrored cage of algorithmic distortion.You think you're seeing reality. You're seeing what keeps you engaged. Your customers think they're choosing freely. They're being nudged by invisible systems.This isn't conspiracy theory. It's business model. Platforms profit from distortion. Marketing agencies profit from platforms. Brands burn money hoping for results.The uncomfortable truth: If you don't know your customers' names, you don't have customers. You have a vendor relationship with Meta.This episode tracks the distortion gap—the space between what's real and what algorithms show us. It's widening. And most brands don't even notice.If platforms collapsed into noise, where does trust come from?Nick's answer: Analog. Patient. Real. Slow to build, impossible to extract.Please enjoy the show.And remember: Stay curious. Be disruptive. Keep Thinking on Paper.Cheers, Mark & JeremyPS: Please subscribe. It’s the best way you can help other curious minds find our channel.--Be our internet friend:⁠Listen to every podcast⁠Follow us on ⁠Instagram⁠Follow us on ⁠X⁠Follow Mark on ⁠LinkedIn⁠Follow Jeremy on ⁠LinkedIn⁠Read our ⁠Substack⁠Email: [email protected] On YouTube--Timestamps(00:00) Trailer(01:00) Disruptors & Curious Minds(02:00) Mark Has A Trust Issue(02:42) What Is Trust?(07:14) How Deep-Tech Brands Build Trust?(09:38) Steve Jobs And Selling A Feeling(10:00) The Cult Of Silicon Valley(10:35) Was the Internet Ever Not Shit? (15:05) What Is The “Distortion Gap”?(20:11) Reducing Your Digital Marketing Spend(21:45) Analog Marketing(23:40) Why the Marketing Funnel Never Really Existed(25:08) VCs, Capital And The Comfort Zone Of Risk(27:04) Analog vs Digital: What Actually Creates Meaningful Connection(28:40) How the TikTok Generation Uses the Internet Differently(32:40) Your Curiosity Is Being Hi-Jacked(35:22) What Are Load-Bearing Inefficiencies?(40:47) The Importance Of Resilience in a World Of Entropy(42:29) What Do We Want Humans To Be?

AI answers faster than any human. But can it help you think? Does it erode critical thinking, or augment it? Pia Lauritzen has analyzed 30,000 questions across languages and cultures. She's a philosopher of the question. And she says we're losing the muscle for real wonder.The problem: We ask "what" and "how." Rarely "why." ChatGPT answers instantly. We skip the struggle. The blank page—where thinking happens—disappears.Who asked the first question in the Bible? Not Adam. Not Eve. The snake. "Did God really say...?" Questions don't just seek information. They transfer responsibility. They create power.We talk about:- Why we default to safe questions (what, how)- Why "why" is radical (challenges authority)- How questions transfer responsibility- Why adults hide their curiosity (fear, ridicule, ego)- The dancing metaphor (leading vs following)- Why blank pages matter (AI fills them too fast)Pia's argument: AI doesn't help you think. It replaces thinking.ChatGPT gives you the feeling of thinking without the work. You type, get an answer, feel smart. But you didn't struggle. You didn't sit with uncertainty. You didn't build the muscle.Three things AI can't do:1. Sit with discomfort2. Ask the question behind the question3. Experience genuine confusionWhat we're losing: The ability to not know—and be okay with it.The real test isn't the machine. It's whether you can hold onto what makes questioning—and not-knowing—uniquely human.If you've ever felt dumber after using ChatGPT, this episode explains why.---Guest: Pia Lauritzen, Philosopher, TEDx SpeakerResearch: 30,000+ questions analyzedTopics: Critical thinking, AI, curiosity, questions, responsibility, wonderPlease enjoy the show. And click subscribe, it’s the best way for other curious minds like you to find our show.And remember: Stay curious. Be disruptive. Keep Thinking on Paper.Cheers, Mark & Jeremy--Other ways to connect with us:⁠Listen to every podcast⁠Follow us on ⁠Instagram⁠Follow us on ⁠X⁠Follow Mark on ⁠LinkedIn⁠Follow Jeremy on ⁠LinkedIn⁠Read our ⁠Substack⁠Email: [email protected](00:00) Trailer(03:28) 30,000 Questions & the What/How Bias(07:38) Questions That Connect vs Questions That Manipulate(09:59) Do We Really Lose Our Curiosity?(14:21) How to Start Better Conversations (18:40) Conversation as a Thinking Space(19:46) Why We Lead with Polarising Topics (20:35) How School Trains Us to Have Answers, Not Questions(22:22) Rethinking Education in the Age of AI(25:22) AI in the Classroom: Tool, Threat or Opportunity?(30:07) Why AI Can’t Help Us Think(32:55) The Essence of Technology, AI Deception & the Turing Test(38:17) What Could Humans Be in an Age of AI?

What makes neutron stars so fascinating that they once fooled astronomers into thinking they were aliens?1967: PhD student Jocelyn Bell Burnell discovers repeating radio pulses from space using a homemade array of wooden poles and copper wire. Regular. Precise. Unnatural.They called it LGM-1. Little Green Men.It wasn't aliens. It was something stranger: neutron stars. The densest objects in the universe. A teaspoon weighs a billion tons.Katia Moskvitch—science journalist and author—joins us to explore pulsars, cosmic mysteries, and why Bell Burnell's supervisor got the Nobel Prize instead of her.We talk about:- Why neutron stars were only theoretical for decades- Who first imagined their existence- How Bell Burnell built the radio telescope that changed astronomy- Why the discovery was almost dismissed as interference- What pulsars are (neutron stars spinning hundreds of times per second)- How they're used as cosmic lighthouses for navigation- The Nobel Prize controversy (her work, his award)- Whether she was robbed—or if the system worked as designedNeutron stars are stellar corpses. When massive stars explode, their cores collapse into objects 20 kilometers wide but heavier than the sun. They spin so fast they bend spacetime. Their magnetic fields are quadrillion times stronger than Earth's.Bell Burnell discovered them. But the 1974 Nobel Prize went to her male supervisor and another male colleague. She's never publicly complained. Others have.The question: Is this science's greatest injustice? Or does the Nobel Prize honor theory over observation—mentors over students—by design?This episode is about discovery, recognition, and what we choose to honor.---Guest: Katia Moskvitch, Science Journalist, AuthorTopics: Neutron stars, pulsars, astronomy, Nobel Prize, Jocelyn Bell Burnell, scientific discovery, recognitionCheers,Mark & Jeremy--Other ways to connect with us:⁠Listen to every podcast⁠Follow us on ⁠Instagram⁠Follow us on ⁠X⁠Follow Mark on ⁠LinkedIn⁠Follow Jeremy on ⁠LinkedIn⁠Read our ⁠Substack⁠Email: [email protected]

Everyone thinks they're a great driver. They're wrong.Most drivers think they can judge a safe overtake. They can't. And that's why we crash.Barry Lunn breaks down the sensor technology that sees eight cars ahead, detects velocity before brake lights appear, and intervenes when you're about to make a mistake.The tech: Radar. Not cameras. Not lidar. Millimeter-wave signals that bounce around traffic and see what you can't.More than half of global crashes are rear-end collisions. All preventable with earlier detection.We talk about:- Why radar beats cameras and lidar for safety- How sensors detect danger before humans register it- Why machines see eight cars ahead while you see two- How velocity changes are detected before brake lights- Why rear-end collisions dominate crash statistics- The trust paradox (people resist automation but quickly rely on it)- Why hands-off driving feels wrong even when it's saferThe problem isn't technology. It's human ego. We think we're good drivers. We're not. We're slow, distracted, overconfident.The machine doesn't get tired. Doesn't check its phone. Doesn't misjudge closing speed. It just prevents the accident you didn't see coming.The question: Why do we resist the system that saves us from ourselves?---Guest: Barry LunnTopics: Self-driving cars, autonomous vehicles, radar technology, driver assistance, crash prevention, automation, trustFormat: Short episode-- Other ways to connect with us:⁠Listen to every podcast⁠Follow us on ⁠Instagram⁠Follow us on ⁠X⁠Follow Mark on ⁠LinkedIn⁠Follow Jeremy on ⁠LinkedIn⁠Read our ⁠Substack⁠Email: [email protected]