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

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 collaborator

We 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.

Botto

Meet Eva Here

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

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 teams
• How cryptographic trust can secure AI systems
• Whether AI agents will replace white-collar jobs
• What a decentralized, AI-native internet might look like

We 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 - Discord

Eliza 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 legislation
• Whether a moratorium on state AI laws could backfire
• AI’s environmental footprint and the hidden cost of data centers
• National security, China, and the myth of technological containment
• The nuanced risks (and rewards) of open-source AI models

This 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 hope

If 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 it
• The limitations of vertical scaling, and how “horizontal scaling” might change everything
• What determinism really means in ML, and why it’s essential for verification and trust
• The nuance behind hallucinations in GenAI — and why they’re not always a bug
• How agentic systems and a “machine economy” might transform our future interactions

We 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.ai

And 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/vanahq

Vana on Twitter/X:

https://x.com/vana

Vana ecosystem: vana.com

Subscribe to The People's AI on YouTube:

https://www.youtube.com/channel/UCnLiYlJulQIcmvCjnVRYotw

Jeff 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 pipelines
• How “data market makers” could unlock liquidity in user-owned datasets
• The mechanics and philosophy behind the VRC-20 token
• Real-world case studies, like data DAOs for Reddit and electric vehicle telemetry
• The path toward Universal Data Income (UDI) — and how it might reshape AI’s future

Whether 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-partnership

More on Vana:

https://linktr.ee/vanahq

Vana on Twitter/X:

https://x.com/vana

Subscribe to The People's AI on YouTube:

https://www.youtube.com/channel/UCnLiYlJulQIcmvCjnVRYotw

Jeff Wilser on Twitter/X:

https://x.com/jeffwilser