The People's AI: The Decentralized AI Podcast

What if the future of AI in healthcare depends less on better models and more on whether patients can actually access their own data?In this episode of The People’s AI, presented by the Vana Foundation, we explore why health data portability is not just a bureaucratic headache, but a foundational issue for better care, better research, and better AI. We begin with the story of Liz Salmi, who discovered just how difficult it was to access and move her own medical records after years of treatment for brain cancer. That experience became the starting point for a bigger conversation about patient rights, siloed health systems, and the real-world consequences of inaccessible data.From there, we examine how better access to health records can help patients catch errors, ask better questions, and become more active participants in their own care. We also look at the larger implications for medicine itself: how fragmented data limits research, weakens AI models, and slows the development of more personalized treatments.We then dig into the idea of digital twins in healthcare, with insights from Jim St.Clair, Reinhard C. Laubenbacher, Ph.D., and Dr. Matthew DeCamp. Together, they help explain how digital models of the body could eventually support more precise diagnostics, treatment planning, and preventive care, but only if the underlying data is portable, usable, and governed in ways that respect privacy and patient ownership.It is a conversation about medical records, interoperability, digital twins, precision medicine, and the broader question of who controls health data in an AI-driven future.Topics covered:Liz Salmi’s story of navigating brain cancer and inaccessible medical recordsWhy patient access to records can improve care and reduce errorsThe role of data portability in healthcare innovationHow siloed data weakens AI models and medical researchWhat digital twins in medicine actually are, and how they could workWhy personalized medicine depends on better, more connected data systemsThe tension between privacy, access, and patient ownership of dataThe People’s AI is presented by the Vana Foundation, supporting a new internet rooted in data sovereignty and user ownership, where individuals, not corporations, govern their own data and share the value it creates. Learn more at Vana.org.

What happens when the biggest questions about AI stop being theoretical and start shaping jobs, education, truth, power, and even what it means to be human.In this episode of The People’s AI, presented by the Vana Foundation, we explore ten of the biggest questions on the future of AI. We examine whether AI will create abundance or accelerate job displacement, whether it will improve education or weaken critical thinking, and how societies should think about AI safety, misinformation, deepfakes, human relationships, power dynamics, AGI, and creativity. Rather than offering one simple answer, this conversation maps the major tensions that will define the next phase of AI.Key moments:[00:00:00] Steve Brown frames AI as a transition into a possible post-work era of service and exploration [00:02:17] Question 1: what AI could mean for jobs, labor, and the economy [00:05:25] Kevin Surace argues AI is driving the cost of content creation and knowledge work toward zero [00:10:24] Derek Rydall on why both optimism and disruption may be true, depending on timing [00:12:15] Question 2: is AI on an exponential path or approaching a limit [00:14:09] Question 3: how AI could reshape education, homework, testing, and personalized learning [00:17:18] Why higher education may need to rethink curriculum, pedagogy, and AI use in the classroom [00:20:25] Derek Rydall’s warning about cognitive atrophy and using AI as a crutch [00:22:58] Question 4: how to think about AI safety, guardrails, and real-world risks [00:25:30] James Bellingham on AI, cybersecurity, economic threats, and why misuse matters more than sci-fi scenarios [00:30:11] Question 5: how AI companions, assistants, and home robots may affect human relationships [00:32:01] Question 6: AI power dynamics, inequality, sovereignty, and who benefits most [00:34:11] The geopolitical race for AI power and why AI capability may concentrate in a few countries and companies [00:37:29] Derek Rydall on AI as both a force for concentration and a tool for individual leverage [00:40:00] Question 7: what happens if AI reaches AGI or superintelligence [00:43:19] Question 8: misinformation, deepfakes, and navigating a world where synthetic media gets harder to detect [00:45:42] Question 9: how AI may change human creativity, cognition, and identity [00:51:17] Question 10: the unknown unknowns, and why everyone needs to help shape the future we wantGuests:Steve Brown — AI FuturistKevin Surace — AI FuturistDerek Rydall — Author, A Whole New HumanJames Bellingham — Executive Director, IAA at Johns HopkinsThe People’s AI is presented by the Vana Foundation, supporting a new internet rooted in data sovereignty and user ownership, where individuals, not corporations, govern their own data and share the value it creates. Learn more at Vana.org.

What if the next life-saving medical breakthrough isn’t a brand-new drug, but an old generic hiding in plain sight, waiting to be matched to the right disease?In this episode of The People’s AI, presented by the Vana Foundation, we explore the upside of AI and data when used to solve consequential problems, from AI drug discovery and drug repurposing to ambient AI in clinical workflows -- to climate change science and preventing wild fires -- and to the often-overlooked importance of data portability and health data interoperability.Key moments[00:00:00] A rare-disease crisis becomes a roadmap for a new model of discovery with Dr. David Fajgenbaum[00:02:00] Why this episode focuses on the promise of AI and richer, more granular data[00:06:00] The incentives problem: why there’s little profit in finding new uses for generic drugs[00:10:00] Every Cure’s approach: scanning the world’s knowledge to score drug–disease matches at scale[00:11:00] Dr. İlkay Altıntaş on turning data at scale into scientific insights, faster[00:13:00] Wearables and digital biomarkers: what Oura-style data revealed during COVID-era research[00:17:00] Personalized medicine, dosage, and the return of tailored treatment through AI assistance[00:18:00] Wildfire AI and disaster resilience: integrating fragmented data to predict risk and act earlier[00:26:00] Dr. Marschall Runge on the healthcare talent crunch and what AI changes in practice[00:27:00] Ambient AI / AI medical scribe: why clinicians embrace it and what it frees up[00:30:00] Interoperability: why health records still don’t talk, and what AI can and can’t fix[00:33:00] Data portability, explained with Art Abal: why “your data should follow you” is still rare[00:35:00] The most “locked” data today: health trackers and social platforms, and why it matters[00:38:00] Competition, innovation, and antitrust: how data silos shape who gets to build[00:42:00] Surprising matches: examples like Botox for depression and lidocaine around tumors[00:45:00] A provocative future: early diagnosis at home, continuous signals, and faster interventionGuestsDr. David Fajgenbaum — Co-founder and President, Every CureDr. İlkay Altıntaş — Chief Data Science Officer, San Diego Supercomputer Center (SDSC)Dr. Marschall Runge — Author, The Great Healthcare DisruptionArt Abal — Co-founder, VanaThe People’s AI is presented by the Vana Foundation, supporting a new internet rooted in data sovereignty and user ownership, where individuals, not corporations, govern their own data and share the value it creates. Learn more at Vana.org.

What happens when robots stop looking like industrial machines—and start looking (and even feeling) human? And if “replicants” become plausible within our lifetimes, what would it take to get there… and what might it break along the way?In this episode of The People’s AI, presented by the Vana Foundation, we explore the robot revolution from three angles: what robots can actually do today (quietly, at scale), what’s likely in the near-term (especially in warehouses, logistics, healthcare, and elder care), and what the more radical futures imply—humanoids, “fleshbots,” and the thorny question of rights and personhood. A through-line across every conversation: the hidden constraint isn’t just hardware or dexterity—it’s data. Robotics doesn’t have an LLM-sized training corpus, and that gap shapes everything from progress timelines to privacy concerns and labor dynamics. We also dig into an under-discussed limiter: power consumption, and why energy efficiency may quietly govern how ubiquitous robots can become.GuestsThomas Frey — Futurist (former IBM engineer)Dr. Aniket Bera — Director of the IDEAS Lab at Purdue UniversityJeff Mahler — Co-founder & CTO, Ambi RoboticsWhat we coverWhy most impactful robots won’t look humanoid (at least at first)Specialized machines—crane-like systems, warehouse sorters, mobile carts—are already delivering value because they can be engineered for reliability in constrained environments.The robots already among us (even if we don’t notice them)Warehousing and supply chain, recycling and waste sorting, mobile delivery systems, and surgical robotics are all expanding—often out of public view.Humanoid robots: where they might actually make senseHomes, hospitals, assisted living, and caregiving settings—places where human spaces and human expectations matter—may be the earliest “real” markets.Robots in science and medicine: the bullish caseLab automation, drug discovery loops, high-throughput testing, and more precise (and potentially remote) surgical procedures could be some of the most meaningful gains.The true bottleneck: the robot data gapLLMs feast on web-scale text. Robots need massive volumes of real-world interaction data—vision, touch, force, motion, and the consequences of actions.How robot companies may collect data (and what that implies)Motion-capture / imitation learning (wearables that mirror human movement), teleoperation (“humans in the loop” controlling robots remotely), simulation, and deployment flywheels that generate production data.Privacy + labor: the coming debateIf robots learn from human environments and human demonstrations, who owns that data—and who gets paid for producing it?A final irony: why humanoids might win more share than we expectWe have endless data of humans doing tasks—videos, demonstrations, routines—so humanoid form factors may benefit from transfer learning advantages, even if they’re not mechanically optimal.About VanaThe People’s AI is presented by the Vana Foundation, supporting a new internet rooted in data sovereignty and user ownership—where individuals, not corporations, govern their own data and share the value it creates.Learn more at Vana.org.

What happens when AI gets smarter by quietly consuming the work of writers, artists, and publishers—without asking, crediting, or paying? And if the “original sin” is already baked into today’s models, what does a fair future look like for human creativity?In this episode, we examine the fast-moving collision between generative AI and copyright: the lived experience of authors who feel violated, the legal logic behind “fair use,” and the emerging battle over whether the real infringement is training—or the outputs that can mimic (or reproduce) protected work.What we coverA writer’s gut-level reaction to AI training on her books—and why it feels personal, not merely financial. (00:00:00–00:02:00)Pirate sites as the prequel to the AI era: how “free library” scams evolved into training data pipelines. (00:04:00–00:08:00)The market-destruction fear: if models can spin up endless “sequels,” what happens to the livelihood—and identity—of authors? (00:10:00–00:12:30)The legal landscape: why some courts are treating training as fair use, and how that compares to the Google Books precedent. (00:13:00–00:16:30)Two buckets of lawsuits: (1) training as infringement vs. fair use, and (2) outputs that may be too close to copyrighted works (lyrics, Darth Vader-style images, etc.). (00:17:00–00:20:30)Consent vs. compensation: why permission-based regimes might make AI worse (and messy to administer), and why “everyone gets paid” may be mathematically underwhelming for individual creators. (00:21:00–00:25:00)The “archery” thought experiment: should machines be allowed to “learn from books” the way humans do—and where the analogy breaks. (00:26:00–00:29:30)The licensing paradox: if training is fair use, why are AI companies signing licensing deals—and could this be a strategy to “pull up the ladder” against future competitors? (00:30:00–00:33:30)Medium’s blunt framework: the 3 C’s—consent, credit, compensation—and why the fight may be about leverage and power as much as law. (00:34:00–00:43:00)A bigger, scarier question: if AI becomes genuinely great at novels and storytelling, how do we preserve the human spark—and do we risk normalizing a “kleptocracy” of culture? (00:49:00–00:53:00)GuestsRachel Vail — Book author (children’s + YA)Mark Lemley — Director, Stanford Program in Law, Science and TechnologyTony Stubblebine — CEO, MediumPresented by Vana Foundation.Vana supports a new internet rooted in data sovereignty and user ownership—so individuals (not corporations) can govern their data and share in the value it creates. Learn more at vana.org.If this one sparked a reaction—share it with a writer friend, a founder building in AI, or anyone who thinks “fair use” is a settled question.

What happens when a “kid-friendly” AI bedtime story turns racy—inside your own car?In this episode of The People’s AI (presented by the Vana Foundation), we explore “Generation Generative”: how kids are already using AI, what the biggest risks really are (from inappropriate content to emotional manipulation), and what practical parenting looks like when the tech is everywhere—from smart speakers to AI companions.We hear from Dr. Mhairi Aitken (The Alan Turing Institute) on why children’s voices are largely missing from AI governance, Dr. Sonia Tiwari on smart toys and early-childhood AI characters, and Dr. Michael Robb (Common Sense Media) on what his research is finding about teens and AI companions—plus a grounded, parent-focused conversation with journalist (and parent) Kate Morgan.TakeawaysKids often understand AI faster—and more ethically—than adults assume (especially around fairness and bias).The “AI companion” category is different from general chatbots: it’s designed to feel personal, and that can be emotionally sticky (and potentially manipulative).Guardrails are inconsistent, age assurance is weak, and “safe by default” still isn’t a safe assumption.The long game isn’t just content risk—it’s intimacy + data: systems that learn a child’s inner life over years may shape identity, relationships, and worldview.Parents don’t need perfection—but they do need ongoing, low-drama conversations and some shared rules.Guests Dr. Michael Robb — Head of Research, Common Sensehttps://www.commonsensemedia.org/bio/michael-robbDr. Sonia Tiwari — Children’s Media Researcherhttps://www.linkedin.com/in/soniastic/Dr. Mhairi Aitken — Senior Ethics Fellow, The Alan Turing Institutehttps://www.turing.ac.uk/people/research-fellows/mhairi-aitkenKate Morgan — JournalistPresented by the Vana FoundationVana supports a new internet rooted in data sovereignty and user ownership—so individuals (not corporations) can govern their data and share in the value it creates. Learn more at vana.org.

Can AI help us grieve, or does it blur the line between comfort and delusion in ways we’re not ready for?In this episode of The People’s AI, we explore the rise of grief tech: “griefbots,” AI avatars, and “digital ghosts” designed to simulate conversations with deceased loved ones. We start with Justin Harrison, founder of You, Only Virtual, whose near-fatal motorcycle accident and his mother’s terminal cancer diagnosis led him to build a “Versona,” a virtual version of a person’s persona. We dig into how these systems are trained from real-world data, why “goosebump moments” matter more than perfect realism, and what it means when AI inevitably glitches or hallucinates.Then we zoom out with Jed Brubaker, director of The Identity Lab at CU Boulder, to look at digital legacy and the design principles that should govern grief tech, including avoiding push notifications, building “sunsets,” and confronting the risk of a “second loss” if a platform fails.Finally, we speak with Dr. Elaine Kasket, cyberpsychologist and counselling psychologist, about the psychological reality that grief is idiosyncratic and not scalable, the dangers of grief policing, and the deeper question beneath it all: who controls our data, identity, and access to memories after death.In this episodeJustin Harrison’s origin story and the creation of a “Versona”What griefbots are, how they’re trained, and why fidelity is hardThe ethics: dependence, delusion risk, and “second loss”Consent, rights, and the economics of data after deathCultural attitudes toward death and why Western discomfort shapes the debateA provocative question: if relationships persist digitally, what does “dead” even mean?Presented by the Vana Foundation. Learn more at vana.org.The People’s AI is presented by Vana, which is supporting the creation of a new internet rooted in data sovereignty and user ownership. Vana’s mission is to build a decentralized data ecosystem where individuals—not corporations—govern their own data and share in the value it creates. Learn more at vana.org.

Who are the invisible human data-workers behind the “magic” of AI, and what does their work really look like?In this episode of THE PEOPLE'S AI, presented by Vana, We pull back the curtain on AI data labeling, ghost work, and content moderation with former data worker and organizer Krystal Kauffman and AI researcher Graham Morehead. We hear how low-paid workers around the world train large language models, power RLHF safety systems, and scrub the worst content off the internet so the rest of us never see it.We trace the journey from early data labeling projects and Amazon Mechanical Turk to today’s global workforce of AI data workers in the US, Latin America, Kenya, India, and beyond. We talk about trauma, below-minimum-wage pay, and the ethical gray zones of labeling surveillance imagery and moderating violence. We also explore how workers are organizing through projects like the Data Workers Inquiry at the Distributed AI Research Institute (DAIR), and why data sovereignty and user-owned data are part of the long-term solution.Along the way, we ask a simple question with complicated answers: if AI depends on human labor, what do those humans deserve?Timestamps:0:02 – Krystal’s life as an AI data worker and the “10 cents a minute” rule2:40 – What is data labeling, and why AI can’t exist without it6:20 – RLHF, safety, and the hidden workforce grading AI outputs9:53 – Amazon Mechanical Turk and building Alexa, image datasets, and more14:42 – Labeling border crossings and the ethics of unknowable end uses25:00 – Kenyan content moderators, trauma, and extreme exploitation32:09 – Turker organizing, Turker-run ratings, and early resistance33:12 – DAIR, the Data Workers Inquiry, and workers investigating their own workplaces36:43 – Unionization, political pressure, and reasons for hope41:05 – Why humans will keep “labeling” AI in everyday life for years to comeThe People’s AI is presented by Vana, which is supporting the creation of a new internet rooted in data sovereignty and user ownership. Vana’s mission is to build a decentralized data ecosystem where individuals—not corporations—govern their own data and share in the value it creates. Learn more at vana.org.

What if your robot vacuum accidentally leaked naked photos of you onto Facebook—and that was just the tip of the iceberg for how your data trains AI?In this episode of The People’s AI, presented by Vana, we kick off Season 3 with a deep-dive primer on the real stakes of AI and data: in our homes, in our work, and across society. We start with a jaw-dropping story from MIT Technology Review senior reporter Eileen Guo, who uncovered how images from “smart” robot vacuums—including a woman on a toilet—ended up in a Facebook group for overseas gig workers labeling training data.From there, we zoom out: what did this investigation reveal about how AI systems are actually trained, who’s doing the invisible labor of data labeling, and how consent quietly gets stretched (or broken) along the way? We hear from Professor Alan Rubel about how seemingly mundane data—from smart devices to license-plate readers—feeds powerful surveillance infrastructures and tests the limits of long-standing privacy protections.Then we move into the workplace. Partners Jennifer Maisel and Steven Lieberman of Rothwell Figg walk us through the New York Times’ landmark lawsuit against OpenAI and Microsoft, and why they see it as a fight over whether copyrighted work—and the broader creative economy—can simply be ingested as free raw material for AI. We explore what this means not just for journalists, but for anyone whose job involves producing text, images, music, or other digital output.Finally, we widen the lens with Michael Casey, chairman of the Advanced AI Society, who argues that control of our data is now inseparable from individual agency itself. If a small number of AI companies own the data that defines us, what does that mean for democracy, power, and the risk of a “digital feudalism”?We cover:How a robot vacuum’s “beta testing” led to intimate photos being shared with gig workers abroadWhy data labeling and annotation work—often done by low-paid workers in crisis-hit regions—is a critical but opaque part of the AI supply chainHow consent language like “product improvement” quietly expands to include AI trainingThe New York Times’ legal theory against OpenAI and Microsoft, and what’s at stake for copyright, fair use, and the creative classHow AI-generated “slop” can flood the internet, dilute original work, and undercut creators’ livelihoodsWhy everyday workplace output—emails, docs, Slack messages, meeting transcripts—may become fuel for corporate AI systemsThe emerging risks of pervasive data capture, from license-plate tracking to always-on devices, and the pressure this puts on Fourth Amendment protectionsMichael Casey’s argument that data control is a fundamental human right in the digital age—and what a more decentralized, user-owned future might look likeGuestsEileen Guo – Senior Reporter, MIT Technology ReviewProfessor Alan Rubel – Director, Information School, University of WisconsinJennifer Maisel – Partner, Rothwell Figg, counsel to The New York TimesSteven Lieberman – Partner, Rothwell Figg, lead counsel in the NYT v. OpenAI/Microsoft caseMichael Casey – Chairman, Advanced AI SocietyThe People’s AI is presented by Vana, which is supporting the creation of a new internet rooted in data sovereignty and user ownership. Vana’s mission is to build a decentralized data ecosystem where individuals—not corporations—govern their own data and share in the value it creates. Learn more at vana.org.

How do we protect privacy in an AI-powered world?As AI systems become increasingly powerful, they’re also becoming increasingly invasive. The stakes are no longer theoretical — they’re immediate and personal. From hospitals and law firms to small construction firms, businesses across industries are facing a pressing dilemma: how can we unlock the benefits of AI without compromising sensitive data?In this episode of The People’s AI, presented by Gensyn, we explore two leading approaches to privacy-preserving AI. First, we speak with Marta Belcher, President of the Filecoin Foundation and a longtime advocate for civil liberties in technology. She breaks down how centralized AI systems threaten privacy and how decentralized, open-source models — like Filecoin — can provide a better alternative. We also dig into why overzealous regulation could backfire and how the stakes go far beyond crypto and into mainstream business.Then, we shift to a more technical conversation with Jiahao Sun, CEO of Flock, a startup pioneering federated learning and blockchain-based governance. He walks us through how decentralized training models are already being used in hospitals in the UK and Korea — and what it will take to make private, local, user-controlled AI the norm.We cover:How centralized AI supercharges surveillance riskWhy federated learning and encryption may hold the keyThe case for decentralized AI in healthcare and beyondWhy tokenomics, staking, and governance matter for AI trustWhat a privacy-first future of agents and personal models could look likeThis isn’t just a crypto or Web3 issue — it’s a business imperative.Flock:https://www.flock.ioFilecoin:https://filecoin.ioAbout Gensyn:Gensyn is a protocol for machine learning computation. It provides a standardised way to execute machine learning tasks over any device in the world. This aggregates the world's computing supply into a single network, which can support AI systems at far greater scale than is possible today. It is fully open source and permissionless, meaning anyone can contribute to the network or use it.Gensyn - LinkedIn - Twitter - Discord

What happens when AI runs out of energy? As models grow exponentially, the world’s compute and energy needs are skyrocketing—and our current infrastructure may not keep up.On today's episode of THE PEOPLE'S AI, presented by Gensyn, we speak with Greg Osuri, founder and CEO of Akash Network, to dive into the future of decentralized AI and why distributed compute could be the key to solving AI’s looming energy crisis. Greg explains the real-world constraints facing AI data centers, why GPU shortages are only the beginning, and how asynchronous AI training and swarm learning could fundamentally change how models are trained.We explore:[2:39] The core problem decentralized compute is solving[7:17] AI’s insatiable energy demand and the role of hyperscalers[9:33] Why energy supply is the real AI bottleneck[12:29] Asynchronous and distributed AI training explained[20:44] How mainstream AI is beginning to embrace decentralized models[24:57] Moving AI compute to the power source: solar, wind, and home devices[41:38] The White House AI plan and the future of open-source AIThis episode connects AI infrastructure, energy sustainability, and decentralization, offering a first-principles look at how we can build a more resilient, sovereign future for machine intelligence.If you’re curious about AI compute, open-source AI, and the intersection of energy and technology, this conversation will expand the way you think about the future of AI.Akash Network:https://akash.network/About Gensyn:Gensyn is a protocol for machine learning computation. It provides a standardised way to execute machine learning tasks over any device in the world. This aggregates the world's computing supply into a single network, which can support AI systems at far greater scale than is possible today. It is fully open source and permissionless, meaning anyone can contribute to the network or use it.Gensyn - LinkedIn - Twitter - Discord

What does it mean for AI to be creative? Can a machine surprise us—or even move us?This week, we explore the frontier of AI-generated art, emotional AI, and decentralized creativity through two very different lenses. In this episode of The People’s AI, presented by Gensyn, we speak with Mario Klingemann, creator of the autonomous artist Botto, and Shavonne Wong, the mind behind the interactive AI companion Eva.We look at how Botto uses generative AI to create tens of thousands of artworks per week, then lets a DAO community vote on which get minted as NFTs—some of which have sold at Sotheby’s. Shavonne walks us through Eva, a “listening machine” designed to be emotionally available, raising questions about grief tech, AI intimacy, and what it means to be heard.Topics include:(03:48) How Botto works: generation, voting, and DAO-based curation(09:15) The role of taste modeling and semantic drift in AI art(16:02) AI companions, grief tech, and emotional projection(24:30) Will AI cause cultural atrophy—or unlock new creative paradigms?(28:44) The tension between AI as tool vs. AI as collaboratorWe close with a reflection on how human meaning gets projected onto machines—and what that might mean for the future of art, identity, and emotional connection in an AI-shaped world.BottoMeet Eva HereAbout Gensyn:Gensyn is a protocol for machine learning computation. It provides a standardised way to execute machine learning tasks over any device in the world. This aggregates the world's computing supply into a single network, which can support AI systems at far greater scale than is possible today. It is fully open source and permissionless, meaning anyone can contribute to the network or use it.Gensyn - LinkedIn - Twitter - Discord

How will AI Agents transform the world? And why do they need to be Decentralized?This episode explores the frontier of AI agents—their power, their risks, and their role in shaping our future, on THE PEOPLE'S AI, presented by Gensyn. Host Jeff Wilser talks with Shaw Walters (founder of Eliza Labs) and Harry Grieve (co-founder of Gensyn) about what happens when AI agents become autonomous, self-coding, and capable of running their own workflows or even companies.Shaw explains how Eliza Labs is building an operating system for AI agents that can write plugins, make decisions, and operate independently. Harry walks through how Gensyn is creating a decentralized infrastructure for machine learning verification, allowing trust to be cryptographically enforced.Together, they discuss:Why “agent swarms” may soon outnumber human teamsHow cryptographic trust can secure AI systemsWhether AI agents will replace white-collar jobsWhat a decentralized, AI-native internet might look likeWe also dig into philosophical questions: Who governs these agents? What does it mean to build trust in autonomous systems? And what happens to society when the agents are working… for themselves?About Gensyn:Gensyn is a protocol for machine learning computation. It provides a standardised way to execute machine learning tasks over any device in the world. This aggregates the world's computing supply into a single network, which can support AI systems at far greater scale than is possible today. It is fully open source and permissionless, meaning anyone can contribute to the network or use it.Gensyn - LinkedIn - Twitter - DiscordEliza Labs:https://www.elizaos.ai/

Should AI be regulated by governments, left to the courts, or guided by open markets and open source? That question is at the heart of this thoughtful, civil debate between two leaders shaping the future of AI policy.In this episode of The People’s AI, we’re joined by Justin Hendrix (Tech Policy Press) and Jeff Amico (Gensyn) for a wide-ranging conversation on how — and by whom — artificial intelligence should be governed. We explore the competing tensions between innovation and regulation, centralization and decentralization, open models and closed ones.We cover:The case for federal vs. state-level AI legislationWhether a moratorium on state AI laws could backfireAI’s environmental footprint and the hidden cost of data centersNational security, China, and the myth of technological containmentThe nuanced risks (and rewards) of open-source AI modelsThis isn’t a food fight — it’s a conversation grounded in substance, disagreement, and common ground.Timestamps:(2:03) What is Gensyn? What is Tech Policy Press?(4:16) Defining the guests’ north stars for AI policy(6:37) Who should set the rules—Congress, states, courts, or global bodies?(12:31) The federal bill that may override state AI laws(17:22) What exactly should we regulate? Models, data, or applications?(24:17) Geopolitics, China, and national security implications(30:45) The open-source debate: freedom vs. risk(39:08) What keeps them up at night: from monopolies to environmental collapse(46:55) Notes of optimism — and what gives them hopeIf you’re curious about the future of AI regulation, this is the debate to hear.Tech Policy Press:https://www.techpolicy.press/About Gensyn:Gensyn is a protocol for machine learning computation. It provides a standardised way to execute machine learning tasks over any device in the world. This aggregates the world's computing supply into a single network, which can support AI systems at far greater scale than is possible today. It is fully open source and permissionless, meaning anyone can contribute to the network or use it.Gensyn - LinkedIn - Twitter - Discord

What if we could trust AI results the way we trust cryptographic signatures? That’s the radical promise behind Gensyn’s work—building verifiable, decentralized AI infrastructure from the ground up.We kick off Season 2 of The People’s AI with Ben Fielding and Harry Grieve, the co-founders of Gensyn. We hear how their origin story began in a London warehouse—right before COVID lockdown—and how their mission has evolved from federated learning for tier-one banks to a sweeping new protocol for decentralized machine learning computation.In this episode, we dig deep into:Why decentralized AI is technically hard to pull off — and why Gensyn is focused on solving itThe limitations of vertical scaling, and how “horizontal scaling” might change everythingWhat determinism really means in ML, and why it’s essential for verification and trustThe nuance behind hallucinations in GenAI — and why they’re not always a bugHow agentic systems and a “machine economy” might transform our future interactionsWe also explore how their work connects to AI arbitration, smart contracts, and the growing demand for trustless execution across compute environments.This is one of those conversations where infrastructure, philosophy, and future vision converge. We’re excited to share it with you.About Gensyn, presenting partner of The People's AI Season 2:Gensyn is a protocol for machine learning computation. It provides a standardised way to execute machine learning tasks over any device in the world. This aggregates the world's computing supply into a single network, which can support AI systems at far greater scale than is possible today. It is fully open source and permissionless, meaning anyone can contribute to the network or use it.Gensyn - LinkedIn - Twitter - Discord

Why can ChatGPT write your emails, but robots still can’t fold your laundry? The answer isn’t hardware—it’s data.On today's episode of THE PEOPLE'S AI, presented by Vana, we speak with Bayley Wang, co-founder of PrismaX, a startup building the “base layer” for real-world robotics. PrismaX connects user-generated video data—like clips of people folding sheets or restocking shelves—with robotics companies that are desperate for this kind of input. Think of it as the data DAO for training robots.We explore the real reasons robotics has lagged behind generative AI, why teleoperation is suddenly a $50/hour job, and how decentralization could be the missing ingredient in bringing useful robots into everyday life.Topics include:– Why software, not hardware, is holding robotics back (0:45)– The failure of rule-based systems in real-world AI (3:56)– How videos of everyday tasks are used to train robots (19:15)– Incentive models: from passive video uploads to paid robot control (17:18–27:06)– Web3 infrastructure: tokens, DAOs, and decentralized data marketplaces (28:52)– The robot-in-every-home future (36:23)PrismaX recently came out of stealth at the A16Z Demo Day, and in this conversation, we unpack what that launch means and what’s next for robotics powered by real-world data and open protocols.Want to get involved? Learn more at PrismaX.aiAnd a big thanks to our partner, Vana, whose mission is to enable user-owned AI through user-owned data -- starting with an ecosystem of Data DAOs and decentralized data marketplaces.About Vana:https://linktr.ee/vanahqVana on Twitter/X:https://x.com/vanaVana ecosystem: vana.comSubscribe to The People's AI on YouTube:https://www.youtube.com/channel/UCnLiYlJulQIcmvCjnVRYotwJeff Wilser on Twitter/X:https://x.com/jeffwilser

What if your personal data wasn’t  being harvested — but instead valued, tokenized, and returned to you?That’s the radical shift proposed by Vana, a platform building decentralized data marketplaces that aim to give users economic power over their own data.In this episode of "The People's AI," we speak with Art Abal, Vana’s co-founder, to unpack how today’s data economy works behind the scenes — and why it’s fundamentally broken. In this episode, we explore how data is currently bought and sold by tech giants, what it would take to redesign that infrastructure around user ownership, and how Vana’s VRC-20 token could be the first step in treating data as a liquid, tradable asset.We go deep on:Why centralized data brokers still dominate AI training pipelinesHow “data market makers” could unlock liquidity in user-owned datasetsThe mechanics and philosophy behind the VRC-20 tokenReal-world case studies, like data DAOs for Reddit and electric vehicle telemetryThe path toward Universal Data Income (UDI) — and how it might reshape AI’s futureWhether you’re a builder, investor, or simply data-curious, this episode offers a look into a future where AI and Web3 integration doesn’t just protect your privacy — it pays you back.Please subscribe, share, and join the conversation.About Vana:Vana's mission is to enable user-owned AI through user-owned data. Vana recently announced a collaboration with Flower Labs to build the world’s first user-owned foundation model. About Vana's collaboration with Flower:https://www.vana.org/posts/vana-flower-labs-partnershipMore on Vana:https://linktr.ee/vanahqVana on Twitter/X:https://x.com/vanaSubscribe to The People's AI on YouTube:https://www.youtube.com/channel/UCnLiYlJulQIcmvCjnVRYotwJeff Wilser on Twitter/X:https://x.com/jeffwilser

What’s real in Crypto + AI—and what’s just noise? In this episode of THE PEOPLE'S AI podcast, presented by Vana, we sat down with two top investors to unpack the actual state of decentralized AI. These are the people who see all the decks, hear all the pitches, and are funding what they believe is the future of the space. So what’s legit, what’s frothy, and where’s it all headed?First, we talk with Alex Odagiu, Investment Director at YZI Labs. Alex gives a clear-eyed view of the current landscape, sharing insights on the wave of projects flooding the space, the use cases that excite him (think data markets, DeFi agents, and composable infrastructure), and the challenges of signal vs. noise. He also breaks down how he uses AI tools in his own VC workflow—practical, actionable insights for anyone trying to level up their research game.Then we shift to a wide-ranging, deeply thoughtful conversation with Daniel Barabander, GC and Investment Partner at Variant. We get into the big picture of how crypto and AI fit together, especially around agent composability, economic ownership, and verification layers. He walks us through why AI agents might need to spend cryptocurrency, where that thesis holds up, and where it doesn’t. We also explore what’s still broken in the space—and what’s needed for real breakthroughs.Topics include:[00:04:00] Why 75% of pitches Alex sees are Crypto + AI[00:10:00] Why blockchain incentives are well-suited for labeling scarce data[00:14:00] What’s broken in open-source model training—and how Web3 could fix it[00:18:00] Agents in DeFi: useful or meme coin mania?[00:21:00] How VCs vet AI x Crypto teams—and why founder ‘pivot mindset’ matters[00:25:00] Practical ways VCs use LLMs for diligence and synthesis[00:32:00] Daniel’s 3 pillars: Aggregation, Verification, and Self-Custody[00:36:00] Do AI agents really need to spend cryptocurrency?[00:44:00] Why economic ownership is crucial to decentralized AI[00:48:00] Composability as a path to superintelligence[00:55:00] What’s still broken: the “fuzzy verification” problem[01:00:00] The underrated promise of modular data layersWe close with a fun look at the modular vs. monolithic debate—Daniel makes the case for why the open, decentralized, and composable internet still has a shot.If you’re at Consensus and still in Toronto, swing by the AI Summit and say hello.Daniel Barabander:https://x.com/dbarabanderAlex Odagiu:https://x.com/odagiusAnd a big thanks to our partner, Vana, whose mission is to enable user-owned AI through user-owned data. Vana recently announced a collaboration with Flower Labs to build the world’s first user-owned foundation model. About Vana's collaboration with Flower:https://www.vana.org/posts/vana-flower-labs-partnershipAbout Vana:https://linktr.ee/vanahqVana on Twitter/X:https://x.com/vanaVana ecosystem: vana.comSubscribe to The People's AI on YouTube:https://www.youtube.com/channel/UCnLiYlJulQIcmvCjnVRYotwJeff Wilser on Twitter/X:https://x.com/jeffwilser

In this episode of The People’s AI, presented by Vana, we explore one of the most promising frontiers in artificial intelligence: federated learning.We speak with Nic Lane, co-founder and Chief Scientific Officer of Flower Labs and a professor at the University of Cambridge, to unpack why decentralized AI may not only be more ethical—but more effective—than centralized systems.We dive into:•Why centralized AI may soon hit a wall due to limited data access•How federated learning enables richer, more private, and more diverse datasets•The mechanics behind training large language models across global networks•Flower’s partnership with Vana to build Collective One, a new model powered by user-owned data•Real-world use cases: from healthcare to weather forecasting to robotics•Why decentralization isn’t just a principle—it might be a technical necessityWhether you’re a developer, investor, or just curious about where AI is headed, this episode offers a lucid, technical-yet-accessible look at the future of open source AI, data DAOs, and user-owned AI models.Subscribe on Apple, Spotify, or YouTube—and if you’re into crypto and AI, this is the conversation you don’t want to miss.For more: https://flower.ai/Presented by Vana, the open protocol for user-owned AI through user-owned data.  Vana's vision is for user-owned AI through user owned-data. Its mission is to be the world's first open protocol for data sovereignty. https://linktr.ee/vanahqVana on Twitter/X:https://x.com/vanaVana ecosystem: vana.comVana at MIT Decentralized AI Summit, Builders WorkshopSubscribe to The People's AI on YouTube:https://www.youtube.com/channel/UCnLiYlJulQIcmvCjnVRYotwJeff Wilser on Twitter/X:https://x.com/jeffwilser

In this episode of The People’s AI, presented by Vana, we dive into one of the most compelling intersections in tech today: AI agents and crypto infrastructure. Our guest is Bryan Pellegrino, co-founder and CEO of LayerZero Labs. His journey is anything but ordinary—former top-ranked professional poker player, model builder for MLB teams alongside Billy Beane, and now a key architect of decentralized AI infrastructure.We explore the deep mechanics of decentralized agents: How will they live on-chain? Will they need wallets? How will reputation systems work for non-human actors? And most critically—what does it mean for blockchains when agents, not humans, drive the majority of transactions?Bryan unpacks why micro-payments are broken in legacy systems and how crypto rails solve that. He discusses why LayerZero should be thought of as the TCP/IP of the decentralized internet—abstracting cross-chain operations for AI agents and making assets truly fungible across blockchains.We also tackle big-picture topics like:[06:10] The problem with overhyping LLMs and the limits of scaling compute[14:45] What LayerZero actually does and why it matters[17:40] AI agents as blockchain-native actors—and why payments come first[27:30] The challenge of AI hallucination vs. blockchain immutability[31:20] Wallets, security, and reputation for AI agents[37:10] Use cases for decentralized AI people are sleeping on[44:45] How to build infrastructure that can outlast rapid AI evolution[46:00] Bryan’s grounded, long-view predictions for AI, AGI, and agent proliferationBryan brings rare insight—and skepticism—to the table, showing where hype diverges from real engineering, and where massive opportunity still lies. If you care about decentralized AI, agentic systems, or the future of crypto infrastructure, this one’s not to be missed.For more: Layer Zero LabsPresented by Vana, the open protocol for user-owned AI through user-owned data.  Vana's vision is for user-owned AI through user owned-data. Its mission is to be the world's first open protocol for data sovereignty. https://linktr.ee/vanahqVana on Twitter/X:https://x.com/vanaVana ecosystem: vana.comVana at MIT Decentralized AI Summit, Builders WorkshopSubscribe to The People's AI on YouTube:https://www.youtube.com/channel/UCnLiYlJulQIcmvCjnVRYotwJeff Wilser on Twitter/X:https://x.com/jeffwilser

In this episode of The People’s AI, presented by Vana, we sit down with Jason Zhao, co-founder of Story Protocol, one of the most ambitious projects at the intersection of AI, blockchain, and intellectual property.We explore how Story Protocol aims to reinvent the $61 trillion global IP economy by transforming IP into a liquid, programmable, and creator-friendly asset class. From Hollywood screenplays to DNA datasets, from fashion brands to decentralized fanfiction, Story is building the infrastructure to track, license, and monetize all forms of IP—seamlessly and transparently.We dig into major use cases:• [04:00] How IP inefficiencies punish creators—and how Story fixes that• [07:40] Case studies in nontraditional IP: gene data (GenoBank) and fashion brands (Crocs, Balmain, Ablo)• [08:10] Why Batman Begins writer David Goyer is launching a new sci-fi universe, Emergence, using Story Protocol• [13:55] The roadmap for fan-owned franchises and decentralized content worlds• [20:00] What happens when AI agents remix copyrighted music—and how revenue flows back to IP holders• [25:00] The complexity of attribution in AI training vs. fine-tuning vs. output• [30:00] The Studio Ghibli dilemma: viral GenAI trends vs. creator compensation• [35:00] How Story Protocol could offer legal protection even when AI models ignore IP rules• [38:30] Inside IP Portal: a GitHub-like hub where creators can register and license IP• [41:24] The two biggest hurdles to mainstream adoption: legitimacy and scale• [45:00] Why AI agents may become major users—and producers—of IP on Story Protocol• [47:28] Predictions for 2025: AI models that share revenue with creators, and the rise of “licensed deepfakes”We also discuss how legacy IP giants might integrate with decentralized systems, and what it means when your likeness—human or AI—is composable and monetizable across the internet.If you’re curious about the future of AI, creators’ rights, and programmable culture, this is a conversation you won’t want to miss.Story Protocol:https://linktr.ee/storyprotocolAbout Vana:Vana's vision is for user-owned AI through user owned-data. Its mission is to be the world's first open protocol for data sovereignty. https://linktr.ee/vanahqVana on Twitter/X:https://x.com/vana• Vana ecosystem: vana.comVana introduces VRC 20 TokenSubscribe to The People's AI on YouTube:https://www.youtube.com/channel/UCnLiYlJulQIcmvCjnVRYotwJeff Wilser on Twitter/X:https://x.com/jeffwilser

In this episode of The People’s AI, presented by Vana, we dive deep into the evolution of decentralized AI with Ramesh Raskar, Associate Professor at the MIT Media Lab and advisor to Vana.We explore why decentralized AI isn’t a rival to centralized AI—but a natural next step. Ramesh outlines a five-phase framework for the evolution of intelligent systems, moving from the “mainframe era” to the emerging “web of AI agents.” Along the way, we discuss how decentralized systems can address problems of data ownership, incentive design, verifiability, and user experience.Whether you’re interested in AI agents, blockchain, data sovereignty, or the ethics of AI infrastructure, this episode provides a high-level vision with real-world applications... from a credible AI researcher who's far from a kool-aid drinking crypto-bro. Timestamps:4:46 – Mainframe vs PC: A mental model for AI evolution6:01 – From intranet to internet to the “web of AI agents”9:13 – Limits of centralized AI12:08 – Market forces, decentralization, and the economy of AI agents13:21 – The four pillars of decentralized AI16:41 – Proof of inference, verifiability, and trust in AI18:14 – Rethinking UX in an agentic future21:21 – Do we need standards? Or will AI agents just adapt?25:27 – A case study of how Vana addresses the four pillars28:21 – A look at the future: agent schools, decentralized labor, and AI socialization32:58 – How Web3 could accelerate progress in entrenched sectors33:37 – The biggest challenges to achieving this visionMIT Media Lab's Decentralized AI Projecthttps://www.media.mit.edu/projects/decentralized-ai/overview/Horizon 2023 | Ramesh Raskar | MIT Decentralized AIhttps://www.youtube.com/watch?v=SSSffQsbFo4About Vana:Vana's vision is for user-owned AI through user owned-data. Its mission is to be the world's first open protocol for data sovereignty. https://linktr.ee/vanahqVana on Twitter/X:https://x.com/vana• Vana ecosystem: vana.comVana introduces VRC 20 Token

In this episode of The People’s AI, presented by Vana, we explore the frontier of Data DAOs — decentralized projects that empower people to take control of their data and get rewarded when it’s used to train AI.Our data is everywhere—sleep tracked by Oura rings, heart rates logged by smartwatches, comments posted on Reddit, even the layout of our homes as mapped by Roombas. Until now, most of that data has been hoovered up by tech giants, often without our knowledge or consent, and then used to train AI models. Data DAOs -- part of the Vana ecosystem -- flip that script. They allow us to contribute our data voluntarily, protect our privacy, and get compensated in return.We feature two real-world examples of this in action. First, we speak with Veronica Kirin of Asterisk DAO (08:25), which aims to correct the long-standing gender bias in medical research by creating a privacy-preserving pool of non-reproductive women’s health data. We dig into the history of how women were excluded from clinical trials until as recently as 1993 (10:01), how Asterisk plans to bring this missing data to clinicians and AI developers, and how tokenization lets women “vote” on their own healthcare outcomes for the first time (19:18). Veronica also explains their phased rollout: the MVP app (23:32), future plans to integrate wearables (21:45), and AI tools that surface personalized trends and emotional insights over time (17:17).Next, we talk to Ryan Kuhel, founder of DLP Labs (32:23), which is turning car telemetry data—everything from battery life to braking patterns—into a valuable asset for drivers. Ryan explains how drivers can join DLP’s “data liquidity pool,” contributing their vehicle data and getting rewarded in tokens. We break down who actually uses car data (39:41)—battery companies, insurance firms, city planners—and why most drivers don’t realize their data is already being monetized without them. Ryan also shares insights from DLP’s explosive growth after launch (45:23), how it ties into the Vana ecosystem (55:46), and how this model could extend to self-driving car training, ride-share companies in emerging markets, and beyond.To learn more or get involved:• Asterisk DAO: asteriskdao.xyz• DLP Labs: dlplabs.aiABOUT VANA:Vana's vision is for user-owned AI through user owned-data. Its mission is to be the world's first open protocol for data sovereignty. https://linktr.ee/vanahqVana on Twitter/X:https://x.com/vana• Vana ecosystem: vana.comThe People's AI on Twitter/X:https://x.com/The_Peoples_AISubscribe to The People's AI on YouTube:https://www.youtube.com/channel/UCnLiYlJulQIcmvCjnVRYotwJeff Wilser on Twitter/X:https://x.com/jeffwilser

DeFi promised to make finance more accessible, but has it lived up to that vision? In this episode of The People’s AI, presented by Vana, we explore how AI agents are reshaping decentralized finance (DeFi) and whether they can simplify complex yield farming, crypto investing, and smart contracts for everyday users.We’re joined by Renç Korzay, co-founder of Giza, a project at the forefront of AI-powered DeFi automation. We discuss how AI can improve DeFi accessibility, optimize financial transactions, and bring new users into Web3. But what are the risks? If blockchain transactions are immutable, how do we ensure AI agents don’t make costly mistakes?Topics Covered:• The challenges of DeFi adoption and why it remains difficult for most users• How AI trading bots and autonomous financial agents could change crypto investing• The risks and benefits of AI-driven DeFi platforms• Whether AI in finance can finally deliver on DeFi’s original promise• The future of DeFi 2.0, AI-powered trading, and decentralized AI protocolsTimestamps:📌 00:00 – Intro to The People’s AI and today’s topic: AI + DeFi = DeFAI📌 02:30 – The biggest problems with DeFi today📌 05:00 – Can AI make DeFi more accessible?📌 10:00 – How AI trading bots optimize DeFi investments📌 15:00 – The risks of AI in crypto: Can AI agents make mistakes?📌 20:00 – The future of autonomous finance and its impact on Web3If you’re interested in crypto, Web3, AI investing, and automated trading, this episode explores what’s next for AI-powered DeFi and the evolving world of decentralized AI protocols.More on Giza:https://gizatech.xyz/Renç Korzay on Twitter/X:https://x.com/renckorzayAbout Vana:Vana's vision is for user-owned AI through user owned-data. Its mission is to be the world's first open protocol for data sovereignty. More on Vana:https://linktr.ee/vanahqVana on Twitter/X:https://x.com/vanaThe People's AI on Twitter/X:https://x.com/The_Peoples_AISubscribe to The People's AI on YouTube:https://www.youtube.com/channel/UCnLiYlJulQIcmvCjnVRYotwJeff Wilser on Twitter/X:https://x.com/jeffwilser

Welcome to 2025's "deAI Summer." Decentralized AI is emerging as one of the most significant shifts in artificial intelligence and blockchain. In this episode of The People’s AI, presented by Vana, we sit down with Jake Brukhman, founder and CEO of CoinFund, to explore the growing intersection of AI and Web3, the challenges of open-source AI, and what it means for AI investing and crypto-backed AI startups.We break down Jake’s investing thesis on decentralized AI, including:• Can decentralized AI compete with centralized models from OpenAI and Google?• The paradox of investing in open-source AI while maintaining a defensible moat• How AI agents are transforming DeFi, crypto trading, and smart contracts• The future of AI-powered DAOs and governance models in Web3• Key green flags and red flags in crypto AI startups• How AI regulation and crypto tokens will shape the future of decentralized AITimestamps:📌 0:00 – Introduction to decentralized AI and Web3 investing📌 3:01 – How to spot real AI crypto projects vs. hype-driven startups📌 6:36 – Why AI models trained on blockchain networks could challenge Big Tech📌 12:25 – The role of AI agents in DeFi and autonomous finance📌 18:26 – Open-source AI vs. proprietary AI—can decentralized models be profitable?📌 27:07 – Why crypto AI tokens could drive mainstream AI investment📌 32:04 – The future of AI-powered DAOs and decentralized governance📌 38:57 – AI regulation, crypto laws, and the role of policymakers📌 45:17 – Predictions: Where decentralized AI will be in one year—and fiveThe intersection of AI, blockchain, and crypto is evolving fast. Whether you’re an investor, builder, or just curious about where AI is heading, this conversation breaks down the real opportunities and challenges of decentralized AI.More on CoinFund:https://coinfund.io/Jake Brukhman on Twitter/X:https://x.com/jbrukhAbout Vana:Vana's vision is for user-owned AI through user owned-data. Its mission is to be the world's first open protocol for data sovereignty. More on Vana:https://linktr.ee/vanahqVana on Twitter/X:https://x.com/vanaThe People's AI on Twitter/X:https://x.com/The_Peoples_AISubscribe to The People's AI on YouTube:https://www.youtube.com/channel/UCnLiYlJulQIcmvCjnVRYotwJeff Wilser on Twitter/X:https://x.com/jeffwilser

AI is nothing without data. It doesn’t exist without it. It doesn’t get good without good data. It doesn’t get great without great data.Right now, two major problems are colliding in the world of AI:1. Data transparency – Most AI companies don’t disclose where they get their training data, leading to lawsuits and ethical concerns over how models are built.2. Data scarcity – AI companies are running out of high-quality data. Much of the internet has already been scraped, and without fresh, high-quality sources, progress could stall.In this episode of The People’s AI, presented by Vana, we sit down with the project's creator, Anna Kazlauskas.  And we explore a solution to both problems: data self-sovereignty. Vana is building a marketplace for user-owned data, allowing individuals to control, contribute, and monetize their own data while helping build AI models that aren’t just competitive with centralized models—but potentially 10x better.We break down:• How AI companies currently source their data (and why it’s a problem)• Why user-owned data could fuel the next breakthrough in AI• The role of data DAOs and how they work• The privacy-preserving mechanisms that allow users to contribute data securely• How decentralization could make AI more powerful, fair, and sustainableVana isn’t just about fairness—it’s about building better AI. We dig into how data sovereignty could shift the balance of power in AI, giving individuals a stake in the models that shape the future.Timestamps:0:00 – Introduction: AI’s biggest data problem2:00 – The hidden battle over AI training data6:45 – What does “data sovereignty” actually mean?12:30 – How decentralized data can create better AI18:10 – What are Data DAOs, and how do they work?25:00 – The economic model behind data ownership30:45 – The privacy challenge: Can user data stay secure?38:20 – The roadmap for decentralized AI’s futureAbout Vana:Vana's vision is for user-owned AI through user owned-data. Its mission is to be the world's first open protocol for data sovereignty. More on Vana:https://linktr.ee/vanahqVana on Twitter/X:https://x.com/vanaAnna Kazlauskas on Twitter/X:https://x.com/anna_kazlauskasThe People's AI on Twitter/X:https://x.com/The_Peoples_AISubscribe to The People's AI on YouTube:https://www.youtube.com/channel/UCnLiYlJulQIcmvCjnVRYotwJeff Wilser on Twitter/X:https://x.com/jeffwilser

Many are calling 2025 the "Year of the AI Agents." And AI Agents are one of the most exciting frontiers of Decentralized AI, or Crypto X AI. These autonomous systems, powered by blockchain and smart contracts, are changing the way we think about digital interactions, finance, and even governance. In this episode of The People’s AI, presented by Vana, we dive deep into the rapidly evolving world of AI agents with two experts who are leading the charge—Davide Crapis, co-founder of PIN AI, and David Minarsch, co-founder of Valory.We explore the most promising real-world applications of AI agents, from decentralized finance (DeFi) and prediction markets to the rise of social AI agents that interact and transact autonomously. We discuss the potential of user-owned AI, the role of crypto in facilitating agent-driven economies, and how blockchain is becoming an essential infrastructure for these systems.Some of the key questions tackled:• What’s the current state of AI agents, and where are they heading?• How can blockchain provide security, autonomy, and coordination for AI agents?• Are AI agents becoming employers—hiring humans to execute tasks?• What are the technical challenges that need to be solved for mass adoption?• How will AI agents reshape the digital economy, from trading to Web3 social platforms?We also break down the latest trends emerging from ETH Denver, where AI agent conversations are dominating the Web3 space. From NEAR ambitious “Road to One Trillion Agents” initiative to cutting-edge work in DeFi and autonomous finance, this episode is packed with insights on what’s next in AI and crypto.About Vana:Vana's vision is for user-owned AI through user owned-data. Its mission is to be the world's first open protocol for data sovereignty. More on Vana:https://linktr.ee/vanahqVana on Twitter/X:https://x.com/vanaThe People's AI on Twitter/X:https://x.com/The_Peoples_AISubscribe to The People's AI on YouTube:https://www.youtube.com/channel/UCnLiYlJulQIcmvCjnVRYotwJeff Wilser on Twitter/X:https://x.com/jeffwilserPIN AI:https://www.pinai.io/Davide Crapis:https://x.com/DavideCrapisDavid Minarsch on Twitter/X:https://x.com/david_enim

In our debut episode of The People’s AI: The Decentralized AI Podcast, Presented by Vana, we dive into one of the most critical questions of the AI era: Who should own AI? As artificial intelligence becomes increasingly embedded in daily life, its ownership and governance will shape the future. Big Tech dominates AI development today, but a growing movement believes AI should be decentralized, open, and user-owned.We speak with Anna Kazlauskas, co-founder of Vana, and Illia Polosukhin, co-founder of NEAR, to explore how decentralized AI could shift power away from centralized corporations and into the hands of individuals.Key Topics Covered:• What decentralized AI means and why it matters• How AI models are built and trained—and who controls them• The intersection of AI, data sovereignty, and blockchain• The potential risks of centralized AI, from bias to economic concentration• How AI assistants, autonomous agents, and data unions are reshaping the internet• Predictions for the next 1-5 years in AI and decentralized technologiesTimestamps:[00:00] Introduction: What is “The People’s AI”?[03:15] Why AI ownership is one of the defining issues of our time[07:24] The risks of centralized AI: control, censorship, and bias[12:05] How decentralized AI provides a solution[18:19] AI agents, peer-to-peer systems, and the future of automation[27:40] Defining the core categories of decentralized AI[39:41] The origin stories of Vana and NEAR[47:57] Predictions: What the next five years of AI could look like[51:21] Closing thoughts: Where is this movement headed?Join us as we navigate the complex but vital conversation about the future of AI. About Vana:Vana's vision is for user-owned AI through user owned-data. Its mission is to be the world's first open protocol for data sovereignty. Sign up for the first AI Data Summit, hosted by Vana, on Feb 28 in Denver. This will be the go-to event at Eth-Denver with leaders at the forefront of Decentralized AI tech and applications. AI Data Summit, hosted by Vana:https://lu.ma/aidatasummitMore on Vana:https://linktr.ee/vanahqVana on Twitter/X:https://x.com/vanaAnna Kazlauskas on Twitter/X:https://x.com/anna_kazlauskasNEAR: The Blockchain for AIhttps://near.org/NEAR on Twitter/X:https://x.com/NEARProtocolIllia Polosukhin on Twitter/X:https://x.com/ilblackdragonThe People's AI on Twitter/X:https://x.com/The_Peoples_AISubscribe to The People's AI on YouTube:https://www.youtube.com/channel/UCnLiYlJulQIcmvCjnVRYotwJeff Wilser on Twitter/X:https://x.com/jeffwilser