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.