unSILOed with Greg LaBlanc

Even without the attention-consuming technology we’re immersed in today, humans have always been easily distractible. So what distracted the mindful monks of the past? And are there lessons from the monks’ practice that we can apply to today’s level of distraction?  

Jamie Kreiner is a history professor at UCLA and the author of The Wandering Mind: What Medieval Monks Tell Us About Distraction. In the book, she details the strategies monks used to improve concentration and connects the past to the present – examining why the human mind has persistently been prone to wandering. 

Jamie and Greg discuss monks’ theories of distractibility and why concentration mattered for salvation, ethical obligation, and getting beyond self-fixation. The conversation also contrasts monastic aims with Stoicism and Buddhist mindfulness, and considers what modern people can learn from the monastic approach.

*unSILOed Podcast is produced by University FM.*

Episode Quotes:

How monks managed distractions internally

23:02: This is a really basic but really profoundly smart way that monks tackled having thoughts they did not want to have. So rather than kind of ignoring them or hoping they are not real or just not paying attention at all and letting something fester and get bigger, you sort of act as a bouncer or a screener of anything that comes through, and you have to evaluate with each one, "Do I want this here? Is this a good thing?" And sometimes that can be really hard. Sometimes they thought that work of deciding whether something was good or bad was so difficult you actually needed to consult another person about it, like a mentor. We would think of it maybe as a therapist now, but they had a very different idea of what that kind of mentoring involved.

Books like technology can be distracting too

29:40: Books are really essential to that initial process of learning, meditating, and internalizing, and part of the memory training involves interacting with books in these ways. But it was also, like any piece of tech, distracting on its own.

How monks strengthened attention over time

26:59: Probably the most basic difference is that they just treated the memory like we would treat a muscle or something. It needed exercise, it needed training. You had to keep it in good shape if you wanted it to behave properly. So the memory for them was both a storage unit, but also a kind of construction site or toolkit that could work with the memories. It had to be a kind of exploratory place to build new ideas out of. So you would train the memory in two different ways. One was training it to store things that you actually wanted to retrieve. They knew it was really hard to get rid of memories, but if you kept stocking new ones that mattered or kept revisiting the ones that you thought were most important, that would more effectively, over time, edge out the things that you thought were intrusive. 

Show Links:

Recommended Resources:

Stoicism

Akrasia 

Monasticism

Guest Profile:

Faculty Profile at UCLA

Google Scholar Profile

Guest Work:

The Wandering Mind: What Medieval Monks Tell Us About Distraction 

Legions of Pigs in the Early Medieval West

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It might sound counterintuitive, but when it comes to the art of persuasion, using a logic-first approach is the last thing you want to do. This is because, in reality, every human being is wired to feel first, and reason later. 

Josh Bandoch is Senior Vice President for Policy Research at the Foundation for Research Into Equal Opportunity and has spent years becoming a master of persuasion. His latest book, How to Get What You Want: Mastering the Art and Science of Persuasion, is the ultimate guide to understanding the techniques that will actually move people, and the science behind why. 

In this episode, Greg and Josh discuss the critical role of emotional intelligence in effective persuasion, how to listen and engage with your audience, “the granny test,” and how Montesquieu’s teachings on particularism can apply to persuasion.  

*unSILOed Podcast is produced by University FM.*

Episode Quotes:

Research rigor isn't enough on its own

[05:44] We want to marry the IQ that the research rigor, the intellectualism gives us, you have to marry that with emotional intelligence. Because you could have the most rigorously researched paper in the history of mankind and if you deliver that in a way that is not emotionally intelligent, might or might be emotionally unintelligent, then it’s going to be counterproductive. So, you need to marry those two together and think about how you deliver this information in a way that your audience receives it warmly and they’re willing to actually do something that you want them to do with it. That’s where the sweet spot is.

Every human being is wired to feel first, and reason later. 

[05:12] All a billion of us are wired to feel first then reason. Sometimes it’s feel then reason. Sometimes it’s a feeling we never get to reasoning. And moreover, people who tragically suffer brain damage and lose the ability to emote, they actually see their reasoning impaired, which means that the logic-first approach to persuasion is actually illogical.

Why we can't stop talking about ourselves

[12:02] There are five aspects of our cognitive wiring that are foundational to persuasion that are central to the book. One is that it’s about them, not you, but we’re wired to think and talk all about ourselves, which isn’t persuasive. So, we’re wired to think and talk about ourselves. Talking about ourselves generates the same sensations in our brain as sex and money. It feels really good. And it’s just, it’s a survival mechanism. So, like if we don’t think a talk about ourselves, who’s going to? Quite possibly nobody. 

On what he thinks about AI being more persuasive than humans. 

[51:52] I’m open to the prospect that the average AI can be more persuasive than the average human because maybe the average human doesn’t bring the calm, or maybe there are some basic, sort of, persuasion tools that AI can use. I still don’t think, and I don’t think we’ll ever be at the point where the most persuasive AI is going to be as good as the most persuasive humans. And even if we are, it’s still the humans who are making the decisions. 

Show Links:

Recommended Resources:

Thinking, Fast and Slow

How to be more persuasive | Joshua Bandoch | TEDxIllinois Tech

Milton Friedman

Modes of persuasion

The Spirit of Law 

Immanuel Kant

John Locke

Guest Profile:

Vice President Profile at The Foundation for Research on Equal Opportunity

Professional Website

Professional Profile on LinkedIn

Guest Work:

How to Get What You Want: Mastering the Art and Science of Persuasion 

The Politics of Place: Montesquieu, Particularism, and the Pursuit of Liberty

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What does the history of human innovation tell us about the current moment we’re in? It tells us that, despite the computational and AI gains of the last few decades, the world’s progress is a bit flat. Why? 

Carl Benedikt Frey is an associate professor of AI & Work at the University of Oxford and directs the Future of Work Programme at Oxford. His latest book, How Progress Ends: Technology, Innovation, and the Fate of Nations, examines the world’s start and stall relationship with progress over the last thousand years. 

In this episode, Carl and Greg dive into the history of how centralized and decentralized systems have impacted the world’s technological innovation. They contrast China’s earlier technological lead and bureaucratic state-building with Europe’s decentralized politics that enabled the Scientific and Industrial Revolutions, Prussia’s critical role in advancing progress, and why despite the breakthroughs with AI, the world could be facing an era of less progress and more stagnation.

*unSILOed Podcast is produced by University FM.*

Episode Quotes:

The real driver of progress

[49:28] Most progress comes from doing new and previously inconceivable things. And if we begin seeing more entry in the AI applied to entirely new products and industries, then I think it can be a much more sustained productivity growth. And so how progress ends refers to the pattern that what we've seen globally for the past two decades is productivity stagnation. And the point is not that that trend cannot be reversed, but doesn't look too encouraging at the moment, I would say. 

Is US innovation actually paying off?

[47:16] I think even if you look at space innovation, the, sort of, real upsurge comes in the 1990s with privatization. And so since then if you look at patenting we haven't seen anything similar. And I don't want to diminish the compliments of SpaceX, but I think it's a continuation of what happened in the '90s, not the new pattern. And clearly there's innovation happening in the United States, but it's not yet showing up in the productivity statistics. And so, you can have exponential technologies without exponential growth. 

How historical incentives created the meritocratic state capacity behind East Asia's successful industrial policy.

[14:00] To study for the civil service examination, you had to memorize the Confucian classics. There was no emphasis on science, technology, and engineering. And it was the way that you could move up in the social hierarchy. It was the best escalator for social mobility there was. And so if Galileo had been born in China, he would probably have been a bureaucrat rather than a scientist, and such were the incentives. And I think it's interesting, and I think it's a point worthwhile making, that really long-lasting legacy of a meritocratic civil service is still there in China today, and it's still there in East Asia more broadly, and it's still there in the countries that we think of as being the most successful in industrial policy, right?

Show Links:

Recommended Resources:

Alexander Gerschenkron

Escape from Rome: The Failure of Empire and the Road to Prosperity by Walter Scheidel

Voltaire

René Descartes

Huguenots

Prussian Reform Movement

Crédit Mobilier scandal

Losing the War with Japan | PBS Frontline

Manhattan Project

MIT Radiation Laboratory (Rad Lab)

Guest Profile:

Faculty Profile at University of Oxford

Professional Website

Professional Profile on LinkedIn

Guest Work:

How Progress Ends: Technology, Innovation, and the Fate of Nations

The Technology Trap: Capital, Labor, and Power in the Age of Automation

If you enjoyed this show, here are some other unSILOed episodes you may like:

Economic Growth in the Age of Automation with Carl Benedikt Frey

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Vivienne Ming is the executive chair at Human Trust, Founder and Executive Chair at Socos Labs, Professor, and author of the book Robot-Proof: When Machines Have all the Answers, Build Better People.

Greg and Vivienne talk about  how AI will reshape work and why the common narratives are really “lazy myths.” Vivienne argues that AI is superhuman at well‑posed problems but humans still retain a relative advantage in ‘ill‑posed problems,’ contributing to a U‑shaped labor demand with erosion of jobs in the middle of it. She criticizes benchmark-driven, autonomous AI development and calls for optimizing hybrid intelligence, better education focused on foundation/meta‑learning skills (curiosity, working memory, perspective-taking, intellectual humility).

Vivienne and Greg debate the merits of UBI as a solution, when discussing how destabilizing mass unemployment would be, and describes experiments where human‑AI “cyborgs” matched expert prediction markets when participants actively challenged AI rather than copying it. She ultimately delivers her message of hope for the future and belief in the humanity of humans.

*unSILOed Podcast is produced by University FM.*

Episode Quotes:

Where humans beat AI

13:47: The most interesting problems that feel rare to us, but in almost the entire game are the ill-posed problems. These are the things where, forget right answers, we don't even know what the questions are. And, it may seem sort of trivially, almost axiomatic, to say that while the space of well-posed problems is vast, everything humanity has figured out. Play around with Gemini or GPT or any model you want and be proud. What it can produce is a testament to what humanity has discovered about the world. And sometimes it produces ugly things, and that's a reflection of us too. But the set of things we do not yet know is infinite. Our relative advantage is in ill-posed problems.

The importance of foundational skills

45:25: Developing foundation skills isn't just about a better job. It's about a better life. What an amazing opportunity to take this moment in history and not just concern ourselves with whether I'm gonna boo Eric Schmidt because he's doubling down on AI and I feel like I'm losing a job, but to actually see that moment as an opportunity to invest in humanity in the way, almost paradoxically, we always should have been, but we didn't have to, and so we didn't.

On transforming people relationship with work

22:56: Viewing your college education simply as a license to earn money, I think, also deserves a lot of self-skepticism. So we need to really transform work, but we also need to transform people's relationships with work, because we simply don't run a factory line economy anymore. I don't need you to be a very sophisticated cog where you're given orders and you execute them, and only a few people are smart enough, like you, to execute those orders. Pretty much everyone's job now, if I may be so self-elevating, is my job. People bring you unknown problems, you gotta figure them out, and I think that's terrifying, and no one's educating the next generation workforce on how to do it.

Show Links:

Recommended Resources:

Jevons Paradox

Generative AI

AlphaGo

Daron Acemoglu

Jacquard Machine

Industrial Revolution

James Heckman

Raj Chetty

Guest Profile:

Profile at Socos Labs

LinkedIn Profile

Wikipedia Page

Social Profile on X

Guest Work:

Robot-Proof: When Machines Have all the Answers, Build Better People

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Dr. Sebastian Wernicke is a data scientist, a partner at Oxera Consulting, and author of the new book Data Inspired: Building an Organizational Culture of Inquiry for Lasting Transformation.

Sebastian talks with Greg about why he thinks culture, not tools, is the biggest obstacle to effective data/AI adoption. Sebastian critiques the “data deficit” assumption that more data yields obvious answers, arguing data often increases complexity and should drive inquiry, psychological safety, and willingness to be proven wrong. They contrast data-driven optimization with a data-inspired mindset that also asks “what if” questions to enable transformation, noting A/B testing examples like Google’s “50 shades of Blue” experiment don’t generalize out to most business decisions. 

Sebastian also explains why initiatives fail when they treat technology as the hard part, while they ignore tacit decision-making, incentives, and workflows, and rely on pilots/lighthouse projects that don’t scale. He outlines strategy using Rumelt’s framework, discusses silos vs “swamps,” leadership behavior, and why CDO/AI roles need explicit transformation mandates, resources, and CEO-level support.

*unSILOed Podcast is produced by University FM.*

Episode Quotes:

Culture not technology, drives transformation

11:36: I think when it comes to data and AI specifically, it's really focusing on the wrong problem as the hard problem. I think whenever you're tackling one of these bigger initiatives, you're sort of trying to figure out in the beginning to say, "Well, what's hard about this?" Right? Because that's what we should focus on initially. And usually, I think what many of these projects focus on is clearly technology. So they say, "Well, what's hard about this? It will be hard to get the data together. It will be hard because there's all these fancy algorithms, and we need the cloud infrastructure, and we need the experts," and so on. And the other elements, you know, we need to act differently. We have to bring 20 different stakeholders together because many of these data and AI projects are interdisciplinary, and they all need to agree, and they need to agree in spirit, not just say that they agree and we're actually going to change the way we work, so we're going to redistribute power, maybe even in our organization, or who makes a decision, that's the hard part.

More data isn’t enough to make better decisions

02:29:  I think we have this inherent assumption that when we want to make better decisions, all we need to do is add a bit of more data to our organizations, right? So, as soon as we see the right data, as soon as we see what the data is telling us, we will know what to do, everybody in the room will agree, and we know how to move forward. And of course, it's not at all like that. 

Optimization is not transformation

02:31: We have this inherent assumption that when we want to make better decisions, all we need to do is add a bit of more data to our organizations, right? So as soon as we see the right data, as soon as we see what the data is telling us, we will know what to do. Everybody in the room will agree, and we know how to move forward. And, of course, it's not at all like that.

Show Links:

Recommended Resources:

Google’s ’50 shades of Blue’​ experiment

Marissa Mayer

Goodhart's Law

Richard Rumelt

Guest Profile:

LinkedIn Profile

Professional Profile on Oxera

Guest Work:

Data Inspired: Building an Organizational Culture of Inquiry for Lasting Transformation

Sebastian’s TED Talks

Data Inspired | Substack Newsletter

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