Jessica and Kimberly sit down with Heather Mellquist Lehto, PhD.

Heather is a mathematician, anthropologist, former Harvard faculty, Vatican AI advisor, and founder of Guilded AI. They asked her to pull back the curtain on data annotation: the human labor that makes AI possible and one of the least visible, least understood, and most exploited parts of the entire industry. From pennies-per-task gig work to expert PhDs clicking through unpaid tests, they dig into who is actually building these models, what they are being paid, and why the workers creating billions in value are locked out of the wealth they generate. Heather shares why she got fed up with the recruiting playbook, what she is building differently at Gilded AI, and why treating workers well is not just an ethical argument but a data quality one.

Topics Covered:

• What data annotation is and why it still requires human expertise at every level of AI development
• The difference between data annotation and reinforcement learning from human feedback
• How workers go from labeling apples to annotating molecular structures and advanced mathematics
• Why the effective hourly rate for data annotators is much lower than advertised
• Scale AI, the $29 billion valuation, and the Department of Labor investigation
• How Guilded AI is structuring equity so annotators share in the upside
• Garbage in, garbage out: why worker treatment is a data quality issue
• AI chatbot vibe checks as expert vetting, and why that fails everyone
• The Gilded Age, guilds, and what banding together could look like
• Why the perfect cannot be the enemy of the good

Referenced in This Episode:

• Empire of AI by Karen Hao
• The Worlds I See by Fei-Fei Li
• Surveillance Capitalism by Shoshana Zuboff
• Rerum Novarum by Pope Leo XIII
• Guilded AI
• Scale AI and the Meta investment

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What does it mean to do meaningful work? And what happens to that meaning when AI enters the picture?

This week we're joined by Valerie Morris, co-host of the podcast Inside Work and Relational Intelligence chapter lead at Culture First. Valerie works with employees and organizations navigating the human side of AI adoption, and she brings both an organizational psychology perspective and a practitioner's honesty to a conversation that gets personal quickly.

We talk about why so many employees feel they can't voice real concerns about how AI is being rolled out, why the skills that create meaning at work (connection, relational intelligence, the ability to just be present with another person) are exactly the ones being sidelined in the rush to automate, and what it looks like to push back on that, quietly and practically, even when you can't change the culture around you.

Woven through all of it is a question the three of us keep circling: What are we willing to give up in the name of efficiency?

None of it is anti-AI exactly. It's more like a case for paying attention to what you're trading away.

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AI governance sounds like something for IT departments and government committees. It's not. According to computer scientist, philosopher, and AI governance expert Clara Hawking, it's really about behavior — how we use technology, who gets harmed when we use it carelessly, and whether the systems we're building deserve our trust.

In this episode, Clara breaks down what AI governance actually looks like in practice ... including a professor who unknowingly violated GDPR by grading students through his personal ChatGPT account, to the risks that compound (not just add up) when AI, biotech, robotics, and quantum computing start feeding into each other. We also get personal about what it means to govern ourselves first, before we can ask anything of institutions.

If you've ever seen the words "AI governance" and assumed it had nothing to do with you — this one's for you.

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