🎤 As AI systems become more capable, who or what can reliably judge whether their behavior is correct, safe, and aligned? In this episode of the @HumansofAIPod, I sit down with @shubadubadub and @joshnotjacob, co-founders of @SampuraResearch, an independent nonprofit exploring how humans and AI can work together to oversee increasingly powerful systems. Rishub and Josh explain why better AI judges are central to the future of alignment. Many of the hardest behaviors to evaluate are subjective, ambiguous, or difficult to verify and neither humans nor AI systems are reliable enough to handle every case alone. Sampura’s research on "human–AI complementarity" asks how the strengths of each can be combined to create more trustworthy evaluations, stronger benchmarks, and oversight methods that continue to work as models improve. We discuss what meaningful benchmarks for AI judges should look like, where human judgment remains essential, and how scalable oversight connects to the larger challenge of robust alignment. Rishub and Josh also share why they left Google DeepMind to start Sampura, how they are shaping the organization’s culture and research agenda, and the unexpected operational realities of building an independent research lab.

🎤 How is AI reshaping not only what we do, but who we are? In this episode of @HumansofAIPod, I chat with Andrew McLuhan (@amicusadastra), a poet, writer, researcher, and the founder and director of The McLuhan Institute (@McLinstitute), where he develops and teaches ways of analyzing the nature of technologies and their personal and social effects. ​As the grandson of renowned media theorist Marshall McLuhan and the son of Eric McLuhan, Andrew brings a perspective shaped by three generations of thinking about how media and technology transform human life.​ Andrew explains why artificial intelligence should be understood as more than just another tool. AI is becoming part of the environment around us, reshaping how we think, create, communicate, trust information, and understand ourselves. ​We unpack Marshall McLuhan’s famous idea that “the medium is the message,” why focusing only on the content produced by AI can distract us from its deeper effects, and what earlier shifts—from oral culture and print to radio, television, and the internet—can teach us about the present moment. We also explore how AI overviews and agents are changing search, discoverability, and the economics of publishing; why the risks of AI extend beyond automation to mental fatigue, weakened judgment, trust erosion, and value misalignment; and how every technology strengthens certain human abilities while allowing others to fade. Andrew offers a practical way to evaluate new technologies against our personal, family, and community values and asks which parts of ourselves we should be unwilling to outsource. Andrew also shares his unconventional path from punk rock, poetry, and furniture upholstery to carrying forward the work of Marshall and Eric McLuhan. The result is a wide-ranging conversation about media, culture, identity, consciousness, and the question underneath nearly every debate about AI: not only “What can this technology do?” but “What is it doing to us?”

In the gambling industry, a customer-service chatbot might be designed to answer account questions then drift into giving betting advice. Because gambling is highly regulated, that unexpected behavior can create real harm and serious fines. That's one reason why adoption of generative AI has been slow in Vegas. Watch the full @HumansofAIPod episode on YouTube: "Kasra Gaharian: AI Can Flag Gambling Risk. But Can It Reduce Harm? | Humans of AI #20"

The question we should be asking more towards AI is not just “What can this tool do?” but “What kind of person am I becoming while I use it?”

Good to see more independent research studying how AI responds to mental health crises. @TransluceAI was able to get anonymized data from @OpenAI and @AnthropicAI on how users talk with ChatGPT and Claude specifically on queries related to mental health support. I appreciated their separation of studying the harness (the chatbot) vs the raw model (the API) which is very much the methodology we take in the "When AI is Your Pastor" paper (fideai.org/research/fmg-benc…). Looking forward to reading through the results and seeing what lessons and future research can be done through @FideAILabs

A concerning trend that calls for new measures for how we evaluate the use of AI in the classroom

What happens when AI needs knowledge that still lives in the physical world? In this episode of @HumansofAIPod, I sit down with Dilo Wijesuriya, President of @ARCDocSolutions, to explore how non-destructive book scanning can help unlock physical archives for the age of AI without sacrificing the books themselves. ARC works with organizations around the world to digitize physical information at scale, including books, documents, archives, and other materials that are still difficult for modern AI systems to access. We talk about why so much valuable data remains offline, how digitization is becoming part of AI infrastructure, and why preserving physical books matters as companies race to turn more of the world’s knowledge into machine-readable data. The conversation is especially timely amid growing attention around projects like Anthropic’s Project Panama, where destructive scanning was used to digitize books for AI training. Dilo offers a very different vision: scan the information, preserve the original, and treat digitization as both an AI problem and a stewardship problem. Recorded live at @Ai4Conferences in Las Vegas as part of the Humans of AI field interview series. Whether you work in AI, libraries, archives, research, enterprise data, knowledge management, or are simply interested in how the physical world becomes accessible to machines, I hope you enjoy the conversation.

What does the future of sound look like? At @Ai4Conferences, I sat down with Jos Daniel, President of Marziani InMersion Systems on the @HumansofAIPod, to talk about the technology behind truly immersive audio and how rethinking the way we engineer sound could transform the experiences we create. #HumansOfAI #AI4 #ImmersiveAudio

🎙️ What is the future of learning? And how do we balance using AI and human wisdom? In this episode of the @HumansofAIPod, I chat with Elijah Rivera, an Assistant Professor of Computer Science at Brandeis University, whose research looks at how people learn computer science, and at how programming languages themselves are designed. Elijah earned his Ph.D. at Brown University, and before that a bachelor's and master's at MIT. At Brandeis, he developed and teaches a course on automation in software development that gives students hands-on experience with the modern software toolchain, while also rooting those tools in their respective histories and in the related questions philosophers of technology have continually raised. Overarching this is a broader conviction Elijah keeps pursuing: that computer science is best served by learning to communicate effectively with other disciplines such as cognitive science, philosophy, and theology, and, more importantly, by being willing to listen to their challenge and critique. Whether you’re a student, an AI engineer, founder, researcher, or just someone curious about the future of education, I hope you learn as much as I did from my talk with Elijah.

🎙️ How can AI be more humane? In this episode of the @HumansofAIPod, I chat with Erika Anderson (@ErikaOnFire) the founder of Building Humane Technology and co-creator of HumaneBench — an open-source benchmark that asks the question most of the AI industry isn't: not whether AI can perform, but whether it treats you like a person. As a former UN press correspondent and writing professor, Erika combines technical rigor and humanistic grounding to the field of AI accountability. Find the podcast on: YouTube: lnkd.in/gSwu_W46 Spotify: lnkd.in/gNrcWrvu

"With great power comes great responsibility." - Uncle Ben When you build for people especially in open-source, you accept a lot more responsibility than just producing functioning code. From keeping documentation up to date, to hosting office hours, to engaging with the community, open-source maintainers do a lot behind the scenes. Between Semantic Kernel, AutoGen, and GraphRAG, @ekzhu and I have been in the driver's seat building some of the earliest and most popular open source projects in AI from our times at @Microsoft and @MSFTResearch. As exciting as those times were, there were also a lot of lessons learned being stewards of open-source communities. Watch Eric and I share those reflections and more in this episode of the Humans of AI Podcast. youtube.com/watch?v=bGupUpbk…

Why did early multi-agent autonomous systems like @pyautogen initially fail to meet the hype? Multi-agent systems mapped well to our human understanding of how to organize work amongst ourselves (i.e. delegation, role responsibility, worker-to-worker communication). But despite being amassing a ton of developer interest and producing some eye-opening demos, enterprises struggled deploying these implementations to production and opted for simpler single-agent approaches instead. So why did they fail and what can we learn from the experience? Watch @ekzhu and I do a retrospective on @pyautogen from our times at @Microsoft and @MSFTResearch in this clip from the latest episode of the Humans of AI Podcast. youtu.be/2cnxea3xkzM

🎙️ The Hard Truth About AI Agents: What We Learned Building in Open Source In this episode of the Humans of AI Podcast, I sit down with my friend @ekzhu to unpack what are the frameworks and architectures powering modern AI products today. We dive into how AI systems are actually being built today, why early agent frameworks were brittle, and what’s changing as models, tooling, and abstractions improve. Eric shares insights from hands-on experience building AI systems, including lessons around memory, orchestration, open source, and why building has become dramatically faster than even a few years ago. We also talk about the human side of all this—how builders should think about careers in AI, avoiding burnout, and navigating a fast-moving field without getting overwhelmed. Whether you’re an AI engineer, founder, researcher, or just curious about where things are headed, this conversation goes deep into the why behind modern AI systems.

The OpenClaw Saga: When AI Stopped Chatting and Started Acting 🦞 OpenClaw is the open-source AI agent that went viral by crossing a line most AI tools haven’t: it didn’t just talk, it acted. @tulamasterman and I unpack how OpenClaw exploded almost overnight, why its rapid name changes and meme-driven hype mattered, and how excitement quickly turned into alarm as real security risks, malicious extensions, and corporate bans entered the conversation. Beyond the headlines, we debate whether OpenClaw represents the inevitable future of autonomous AI agents or a warning sign that the industry is racing ahead of its ability to make these systems safe, understandable, and controllable. Check out the clip for the retrospective!

🎙️After a long break, the Humans of AI Podcast is back for Season 2! With a new look, new format, new cohosts, and guests, this season will be one you won't want to miss. This week my cohost @tulamasterman and I recap the saga that was OpenClaw — the open-source agentic project that took the internet by storm, spawned an AI social network where agents allegedly conspired to create their own language, and ended with its solo founder joining OpenAI. Plus, we cover new research from Anthropic and OpenAI that reveals what actually happens when humans hand the wheel to agents. We also explore are computer science majors disappearing? Recent data from the University of California seems to suggest so. And we wrap with Humans of AI Office Hours, where we cover user-submitted or curated questions worth digging deeper into. This week's question: "When AGI?"