What if the future of AI is not bigger models in bigger data centers, but smaller ones running quietly on the devices you already use every day?

In this episode of AI-Curious, we talk with Jeffrey Li, COO of Liquid AI, about why the next phase of AI may depend less on giant cloud models and more on small, specialized models that run directly on phones, laptops, cars, and other edge devices. We explore the case for on-device AI, why large models are only part of the story, and how companies should think about speed, privacy, cost, and real-world deployment as AI moves from experimentation to everyday products.

We also dig into the economics behind this shift. Along the way, we discuss why cloud-based AI can break down when every query has to travel to a data center, why enterprise ROI gets harder as AI subsidies fade, and why many real-world use cases do not need a giant model capable of doing everything. Instead, they may need a smaller, more tailored system built for a specific task, domain, or device.

We also get into Liquid AI’s research roots at MIT, the origins of liquid neural networks, and what it looks like to bring production-quality AI into places like Mercedes vehicles and e-commerce systems. This is a practical conversation about the future of edge AI, specialized models, privacy-preserving AI, and what happens when intelligence moves closer to the user.

Guest
Jeffrey Li — COO, Liquid AI

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It’s happening everywhere. And no one’s really talking about it. What happens when your employees are already using dozens of AI tools your company never approved?

In this episode of AI-Curious, we talk with Rick Caccia, co-founder and CEO of Witness AI, about the rise of “shadow AI” inside enterprises and why it has become one of the biggest practical challenges in AI adoption. We explore how employees, often with good intentions, are quietly using ChatGPT, Copilot, and thousands of other AI apps to do their jobs faster, sometimes with sensitive data that should never leave the company.

We also dig into what happens when that behavior scales. From customer support teams pasting financial information into AI tools, to marketers uploading customer lists, to developers sharing source code with external models, we look at the real security, compliance, privacy, and cost risks companies are now facing. We also discuss why this problem gets even harder with AI agents, which can take actions, access systems, and create new forms of risk far beyond a simple chatbot prompt.

Along the way, we talk about prompt injection, jailbreaks, token costs, insider risk, enterprise governance, and how leaders can build an AI strategy that enables productivity without creating chaos. This is a practical conversation for anyone trying to understand how AI is actually being used inside organizations right now, and what it takes to manage that responsibly.

Guest
Rick Caccia — Co-founder and CEO, Witness AI

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For anyone interested in Jeff’s AI Workshops for their company:

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What happens when an AI prediction does not just forecast the future, but helps create it?

In this episode of AI-Curious, we talk with philosopher and ethicist Carissa Véliz about AI ethics, AI privacy, predictive AI, and the hidden power of algorithmic decision-making. We explore how AI systems used in hiring, lending, insurance, and other high-stakes settings can become self-fulfilling prophecies, shaping outcomes rather than simply measuring them.

We also examine the growing privacy risks of large language models and AI agents, especially as they gain access to more personal data, communications, and systems. Along the way, we discuss automated decision-making, surveillance, human autonomy, and why predictions about people are far more ethically fraught than predictions about things like the weather.

This conversation also goes beyond policy and into philosophy: how narratives about AI shape public thinking, why humor can be a response to technological power, and how individuals and companies can use AI responsibly without giving up judgment, control, or resilience.

If you are interested in AI ethics, algorithmic bias, AI privacy, AI agents, responsible AI, predictive algorithms, self-fulfilling prophecy, and the future of AI, this episode offers a clear and thought-provoking framework for understanding what is at stake.

Guest
Carissa Véliz — Philosopher, Associate Professor at the Institute for Ethics in AI at the University of Oxford, and author of Prophecy, Prediction, Power, and the Fight for the Future: From Ancient Oracles to AI.

Carissa's TED talk:

https://www.ted.com/talks/carissa_veliz_beware_the_power_of_prediction

Carissa's new book: Prophecy, Prediction, Power, and the Fight for the Future: From Ancient Oracles to AI.

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For anyone interested in Jeff’s AI Workshops for their company:

Reach out directly at jeff@jeffwilser.com