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

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 layers

We 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/dbarabander

Alex Odagiu:

https://x.com/odagius

And 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-partnership

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

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 necessity

Whether 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/vanahq

Vana on Twitter/X:

https://x.com/vana

Vana ecosystem: vana.com

Vana at MIT Decentralized AI Summit, Builders Workshop

Subscribe to The People's AI on YouTube:

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

Jeff 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 proliferation

Bryan 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 Labs

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/vanahq

Vana on Twitter/X:

https://x.com/vana

Vana ecosystem: vana.com

Vana at MIT Decentralized AI Summit, Builders Workshop

Subscribe to The People's AI on YouTube:

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

Jeff Wilser on Twitter/X:

https://x.com/jeffwilser