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 want

Guests:

Steve Brown — AI Futurist
Kevin Surace — AI Futurist
Derek Rydall — Author, A Whole New Human
James Bellingham — Executive Director, IAA at Johns Hopkins

The 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 intervention

Guests

• Dr. David Fajgenbaum — Co-founder and President, Every Cure
• Dr. İlkay Altıntaş — Chief Data Science Officer, San Diego Supercomputer Center (SDSC)
• Dr. Marschall Runge — Author, The Great Healthcare Disruption
• Art Abal — Co-founder, Vana

The 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.

Guests

• Thomas Frey — Futurist (former IBM engineer)
• Dr. Aniket Bera — Director of the IDEAS Lab at Purdue University
• Jeff Mahler — Co-founder & CTO, Ambi Robotics

What we cover

• Why 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 sense
• Homes, 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 case
• Lab 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 gap
• LLMs 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 debate
• If 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 expect
• We 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 Vana

The 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 cover

• A 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)

Guests

• Rachel Vail — Book author (children’s + YA)
• Mark Lemley — Director, Stanford Program in Law, Science and Technology
• Tony Stubblebine — CEO, Medium

Presented 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.

Takeaways

• Kids 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 Sense
• https://www.commonsensemedia.org/bio/michael-robb
• Dr. Sonia Tiwari — Children’s Media Researcher
• https://www.linkedin.com/in/soniastic/
• Dr. Mhairi Aitken — Senior Ethics Fellow, The Alan Turing Institute
• https://www.turing.ac.uk/people/research-fellows/mhairi-aitken
• Kate Morgan — Journalist

Presented by the 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.

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 episode

• Justin Harrison’s origin story and the creation of a “Versona”
• What griefbots are, how they’re trained, and why fidelity is hard
• The ethics: dependence, delusion risk, and “second loss”
• Consent, rights, and the economics of data after death
• Cultural attitudes toward death and why Western discomfort shapes the debate
• A 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” rule
• 2:40 – What is data labeling, and why AI can’t exist without it
• 6:20 – RLHF, safety, and the hidden workforce grading AI outputs
• 9:53 – Amazon Mechanical Turk and building Alexa, image datasets, and more
• 14:42 – Labeling border crossings and the ethics of unknowable end uses
• 25:00 – Kenyan content moderators, trauma, and extreme exploitation
• 32:09 – Turker organizing, Turker-run ratings, and early resistance
• 33:12 – DAIR, the Data Workers Inquiry, and workers investigating their own workplaces
• 36:43 – Unionization, political pressure, and reasons for hope
• 41:05 – Why humans will keep “labeling” AI in everyday life for years to come

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.

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 abroad
• Why 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 chain
• How consent language like “product improvement” quietly expands to include AI training
• The New York Times’ legal theory against OpenAI and Microsoft, and what’s at stake for copyright, fair use, and the creative class
• How AI-generated “slop” can flood the internet, dilute original work, and undercut creators’ livelihoods
• Why everyday workplace output—emails, docs, Slack messages, meeting transcripts—may become fuel for corporate AI systems
• The emerging risks of pervasive data capture, from license-plate tracking to always-on devices, and the pressure this puts on Fourth Amendment protections
• Michael 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 like

Guests

• Eileen Guo – Senior Reporter, MIT Technology Review
• Professor Alan Rubel – Director, Information School, University of Wisconsin
• Jennifer Maisel – Partner, Rothwell Figg, counsel to The New York Times
• Steven Lieberman – Partner, Rothwell Figg, lead counsel in the NYT v. OpenAI/Microsoft case
• Michael Casey – Chairman, Advanced AI Society

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.

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 risk
• Why federated learning and encryption may hold the key
• The case for decentralized AI in healthcare and beyond
• Why tokenomics, staking, and governance matter for AI trust
• What a privacy-first future of agents and personal models could look like

This isn’t just a crypto or Web3 issue — it’s a business imperative.

Flock:
https://www.flock.io

Filecoin:
https://filecoin.io

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 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 AI

This 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