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