Data, Deals, and Disruption The Real AI Shift in Private Equity

In this episode, Alex Rawlings speaks with Jamyn Edis, digital transformation expert, former VC-backed CEO, and NYU professor. Jamyn shares actionable insights on using AI, data, and automation to drive performance across private equity firms and their portfolio companies.

From dynamic pricing models and A/B testing to agentic AI and accelerating product cycles, Jamyn explains how to cut through the hype, identify real opportunities, and turn AI into measurable value.

⏱️ Episode Highlights

00:00 – Meet Jamyn Edis
30+ years in consulting (Accenture), corporate (HBO, Take-Two), startups, and teaching at NYU. Focused on the intersection of tech, data, and private equity.

02:23 – Common Mistake in PE
Most firms treat data as plumbing, not strategy. Shift from backward-looking reporting to predictive and prescriptive analytics.

03:48 – AI That Delivers ROI
AI must be embedded into daily operations, not stuck in R&D. Productize, operationalize, and commercialize—or risk irrelevance.

05:15 – How AI Builds on Data
AI is powered by historical data but creates forward-looking insights. Use it to improve pricing, marketing, and operations.

06:42 – Case Study: Take-Two Interactive
Jamyn led data science initiatives to optimize pricing and marketing—replacing gut decisions with data-driven experimentation.

08:08 – AI as PE’s Next Value Driver
AI is the new operating system for PE. After leverage and digital transformation, the next edge is intelligence at scale.

09:31 – Advice to PE Firms
Skeptical? “Get out before the business gets out of you.” Overwhelmed? Start small, build prototypes, and focus on real use cases.

11:04 – Agentic AI in Action
Jamyn shares examples of cutting development cycles, boosting campaign ROI, and improving customer service with AI agents.

13:26 – From Problem to Prototype
Diagnose real pain points, create business and technical requirements, and build targeted AI tools that solve specific problems.

14:50 – AI Won’t Replace Humans, But...
Those who use AI will outperform those who don’t. Jamyn explains Jevons Paradox and why productivity leads to new jobs.

18:46 – How Jamyn Diagnoses Opportunities
Structured discovery process, stakeholder interviews, and clear roadmaps to identify where AI and tech can drive value.

22:10 – Every PE Firm Needs an AI Stack
Soon, AI tools will be as essential as data teams or web developers. Invest now in people, platforms, and processes.

23:06 – The Future is AI + Human Judgment
Firms that embed AI deeply—and pair it with operator judgment—will unlock the next generation of alpha.

24:21 – Jamyn’s Book: The Fifth Horseman
An exploration of how AI mirrors humanity and transforms the way we live and lead. AI is trained by us—and reflects us.

29:07 – What Jamyn Reads & Recommends
Books: The Hard Thing About Hard Things, Understanding Media
Podcasts: Pivot, Ezra Klein
TV: Succession – “Still the best corporate training program ever made.”

🔗 Connect with Alex Rawlings on LinkedIn: https://www.linkedin.com/in/alexrawlings/
🌐 Visit Raw Selection: www.raw-selection.com

Looking to grow your team? Check out our Hiring Guides

for proven strategies, templates, and best practices to make smarter hires. 

Transcript

00:00
Welcome back to the Royal Selection Private Equity Podcast. Joining us today is Jamin Ides, a digital transformation operator and a previous VC-backed chief executive located in the US.  AI projects examples, how you can use technology,  AI data, machine learning  to achieve more with less in your private equity  and your portfolio companies.  Let's dive in.

00:28
Share with us a brief insight into you, Well,  it's great to be here. Thank you. My name is Jamin Edis. uh I've spent my career at the intersection of technology, media, and telecoms  for the last 30 years. uh spent  beginning of my career in strategic consulting with Accenture for 10 years,  focused on uh digital transformation, data analytics for uh primarily TMT clients.  I spent 10 years in a variety of corporate roles  between HBO, uh

00:56
Take-Two interactive video game company and Pearson education company really helping them transform their innovation practices, their technology product development,  their data science and  AI practices.  So I'm kind of in between that. also spent seven years as a CEO and co-founder of a venture backed startup that focused on telematics data, how the data that we earn from consumer drivers could then be turned around and monetized with enterprise clients.  it's really the reason I bring that up.

01:25
What informs me today as I go to work is really just this layer cake of 10 years of professional services, 10 years in corporate roles, and seven years  with us entrepreneurial bent. So I'm really a recovering corporate executives who at this moment in time  really  bridges, you will, between private equity,  portfolio company, and consulting clients, focusing primarily on technology, data, and AI.  One  of the other strings in my bow, I guess, is that I have been a professor at NYU's business school.

01:55
Stone School of Business,  really there I'm taking to my classroom and to my students  the  lens of my personal experience  of being an operator  and turning that into  lessons that my students can then weaponize as they go off and find their jobs and rise up the corporate ladder  and become leaders in business.  So that's broadly  the background and the lens through which I approach my work. What's one mistake that you...

02:23
see private equity or portfolio companies making and what would you suggest to correct them? Well, given my focus on technology, data, artificial intelligence, I think that  I often  find leadership and when I talk about leadership, it's people who've been in these roles, whether entrepreneurs who have been running their companies for 10 years,  portcos, whether they are practitioners, whether uh GPs, operating partners and private equity firms, who have a long, distinguished career. uh

02:52
A lot of them view data  as really just plumbing instead of strategy. Most firms are obsessed with reporting, so backwards looking, what we call uh descriptive analytics. And really what they should be focusing on is prediction and prescriptions, telling them what they will happen, forecasting what they should be doing.  That requires a lot of investment, foundational investment in  technology,  in data architecture,  and machine learning capabilities.

03:21
That is building products, putting the right people in place, having that bright process in place. Sometimes that's not terribly interesting, right? A lot of it is Greek to these people. uh When you start talking about data governance and QA and data engineering and uh table mapping databases, data lake houses, um hybrid, Bayesian machine learning models, a lot of it is just jargon to them. um

03:48
I don't expect them to become scientists or mathematicians, but I do expect them to understand is how you take those inputs, the cognition that you put in the middle and what the outputs yield. And all of this data science, all of these advancements in AI are meaningless. If you are not productizing it and solving a problem for your end user, if you are not operationalizing in the daily fabric of the business, and if ultimately you're not commercializing it and driving it to attributable ROI, otherwise the CFO is not.

04:18
funding this. uh So quickly it needs to move from R &D and sandbox to actually something that's used daily and something you can point towards value.  And that's what I see is often is a misunderstanding. One of the largest things I find is a miscommunication, almost like someone speaking Portuguese to someone who speaks Mandarin. They don't understand each other. So I think, you know,  a role that an operating partner, a role that a, um a tech know optimist can play, you, if you will, is sitting in the middle of understanding those two

04:47
tribes and making sure that they can work together. I think that's one of the biggest problems that I find. Good news is that that kind of friction creates opportunities for effective operating partners  and people who can be that translating engine. Perfect. So you've shared your insights into or your expertise around data and AI and that being an area of passion for you. How do you describe, given that AI is a kind of a new area but...

05:15
data driven decision making has always existed, or it should have always existed in  private equity in some lens.  What's your kind of take on data and AI currently with a private equity vision on it? Well, as we learned in finance, past performance is an indicator of future returns necessarily. um We need to turn that on its head. Past performance is data. Data is what you feed and input into models. When you look at

05:43
And again, I think it's important to pull back here a little bit and understand what we mean by AI. AI is a casual term. It's a simulation of human intelligence  and it has many sub-flavors to come with it. so AI has been around for decades.  We use it every day with natural language processing, which is what powers Alexa and Siri and the voice computing that we've become used to. It's computer vision. uh It's machine learning, which is basically just advanced statistical analysis in many ways reductively.

06:12
linear regressions, given neural networks, you have deep learning.  Now you have large language models. All of that has been built using the corpus of historical data, which is backwards looking. Now, as you focus on the future, you're making predictions based on that and the prescriptions as well. What will happen again or what you should do.  Now that how does that translate to something that's actionable? uh When I was at Take Two, which is a video game company, it's a holding company that owns  game developers like Rockstar Games that make Grand Theft Auto.

06:42
Zynga for mobile games, 2K that make sports games. I led our data strategy, Alex, data science and AI practice. We were building models for our sales team on dynamic pricing. What exactly should we be pricing a particular skew of a particular title on by geography, by console, by season? Is it a premium edition, the base edition? How long should that promotion run? Should it be a percentage? Should it be an absolute dollar amount?

07:09
And these are things that historically the sales team did based on instinct and relationships and their experience. We were able to take the corpus of pricing decisions that were made  and the historical output. And we were able to build models that would predict what would happen with particular price points and run prescriptions for our sales team. The only way to prove if that's right is to then be scientific about it and run A-B tests. So trial that promotion, what is the expected revenue or unit uplift? Did it work or did it not? If it does in that particular experiment.

07:38
then run it at scale and then read the rewards of that. Similarly, we were using data science models to predict the best deployment of marketing budgets, what channels, what kind of budget should we put in there, what's the expected ROI. Again, you run A-B tests, you see what works, not all experiments work, failure is as much of a lesson as a win, and then you scale up based on that output. So I think that for AI in private equity today, whether it's the investment decision or whether it's how you are advising your portfolio companies,

08:08
We're really in a situation where it's kind of like a barbell right now. On the one side, you've got a lot of hype, this endless AI transformation, McKinsey, Dex, Bain, BCT, Accenture, where some boards still just view chat GPT, chord, perplexity, Gemini, all of these are kind of a party trick. The truth is somewhere in the middle. And AI is really becoming a new operating system for value creation. You know, in the 2000s in PE, was all that leverage. In 2010, it was the...

08:36
forceful move towards digital transformation. And now it's about intelligence, but embedded in every process, every contract, every customer touch point. the winners are going to be the ones that fund that industrialized leveraging of knowledge, but most importantly about judgment. Because data without insight, data without decisions is just worthless.  So AI to  a lot of people is new and...

09:03
I've had people on the podcast who've spoken about it and been like, I've been doing this for 10 years and I'm now cool type scenario. But I  still notice private equity firms that still believe that it's a fad and it's kind of, yeah, it's chatty PT and can be used.  What advice would you give to those private equity firms that think it's a fad? And then secondly, what would advice would you give on the other lens of the barbell are the people that are kind of just a bit lost in, we want to bring this into our firm or our portfolio companies more.

09:31
We just don't know who to speed to, everyone's an expert, et cetera.

09:37
Well, I mean, this for the people who think it's a fad, I was like, get out of the business because otherwise the business is going to get out of you. You have, there's no getting away from this. Um, you know, I'm, I'm 50 years old. I've had the benefit of working for 30 years in this industry. Um, I could not be more excited and energized about the, the, the wave of innovation we're seeing right now in this world of AI. And again, AI and all the self-favors that we mentioned earlier on.

10:06
You know,  it's coming.  For me, the first wave was, of course, the dawn of the internet. And sure, there was a bubble. People made bets. People made rash bets. They were reckless. They invested in what they didn't know. The most important thing is to understand it. You understand it by doing it. You understand it by building it.  So private equity companies should not just be investing in companies that have hashtag AI slaps all over their Sims and our decks. um They should be leveraging it themselves as they do their own dealings, as they

10:36
as they do their own analysis on management decks,  as they hone their thesis, they should be doing their own A-B tests.  They should be using it internally for reviewing contracts. They should be doing it for HR decisions.  They should be doing it for their own marketing and the creation of their own thought leadership. Three main areas where um AI is impacting business today most forcefully across industries and verticals is around software development. It's around content creation.

11:04
It's around customer service and communications. All of these things are areas and functions of private equity have to be involved in again, just not only investors, but also the port codes beneath them. you know, for example, I've been working for a port code, which is a fundraising analytics platform  for nonprofits,  users, prescriptive data science models to tell nonprofits who to talk to, who to target, how to target them, what messaging, personalization, et cetera. We've seen the use of agentic AI products to automate their workflows.

11:34
used to take humans weeks to do can now take human with a AI exoskeleton and an agent to AI and boosting ROI on campaigns by 30%. Seeing customer service costs reduced by 15%. Seeing the software development lifecycle shrink from three months down to two weeks because the creation of code, the QA of code, the automation documentation around that code to explain what it is, the monitoring of

12:02
data drift and model drift, running of A-B tests at scale. All of that is enabled with the sheer metric tonnage of data that's available  and the tools that we have today with AI. um In a recent example, that piece of analysis, I was engaged on a three month piece of work for this portfolio company with one with me and a computer and a variety of AI tools. I was able to do work probably at a team of five.

12:29
I worked for Accenture for 10 years. This is the type of work that might've taken six months and a team of five, but it took me less than three and a team of one. I was able to, in 24 hours, build an agentic AI prototype for them using Anthrobics Clawed, just me and a computer and from the light medication to make it work and actually show them the art of the possible, something that they thought might take six months to develop. did in 24 hours.

12:58
That's the kind of velocity and momentum that's enabling us to really, as long as one understands how to, again, leverage, operationalize it, prioritize it, and commercialize it. That's the important thing. So what I would encourage  is people not just to read decks, don't just go to McKinsey and BCG and Bain's website or Accenture or IBM or Deloitte. Get your hands dirty. That doesn't mean opening chat GBT. It means going in, building your own agents, building your own co-pilots.

13:26
seeing the art of possible, doing deep research, building products. And the product isn't just an output in words, it's building UIs, UXs, front ends. And it all begins with, because this is more than just like a foundational frontier models, I think is really going come down to probably five or six in the next few years. It's going to be OpenAI, it's going to be Google's Gemini, it's going Professor Lee's CORD, DeepSeek, whoever it is. Very similar to the way that...

13:53
cloud computing was promised for a long time, but what does that come down to? It's really come down to AWS, Google cloud platform, Azure, whatever it might be. There's a handful of them. There'll be a handful of frontier models. That's great. That's going to become commoditized. The real value added is going to be when operating partners, when GP's that go into their portfolio of companies saying, what problems do you need to solve? What does your company do? Medical imaging, contract reviews, real estate deals. Go and find out the front line.

14:21
with your stakeholders, what problems are you trying to solve? What takes the most of your time? And then go out and build the Gentic AI work products that will automate those workflows. And by the way, one thing I'll say is that oftentimes people wave their hands and make broad assertions about how this is going to destroy jobs, or you're going to need fewer people to do this stuff. Now, I won't argue with the year efficiency that a lot of two, three years of efficiency that a lot of these tech companies have done. They've gutted a lot of their middle management.

14:50
I think the pendulum will eventually swing back. I think it will swing back because we still need human judgment. AI is presenting us the pantheon of corpus of  humanity's knowledge, but what it doesn't do is replace our judgment as human beings. So I think you're going to see a reversal of that, of needing leadership to come back in. People are humans. Relationships seem to be managed. need empathy. um But that said,  what's interesting about the idea of job replacement and job losses, um

15:19
to think about this concept called Javan's paradox. you'll listen to something you heard about, Javan, J-E-V-O-N-S, was an economist who studied the impact of new technologies that increase productivity. And people's initial reaction is, when that happens, people lose jobs, the automation of factories. You didn't need people weaving looms. had machines that did all this. Yes, it got rid of a certain class of worker, but they were replaced by knowledge workers.

15:49
And in fact, the paradox that Jayvon talks about, here's a good example. You think about secretarial work. Historically, that was legions of human beings writing things down. Then the typewriter came as a new technology. It meant that people could type and document things faster. People thought, oh, well, that's going to lead to a reduction in the workforce of secretaries. Well, in fact, they did the opposite. That's Jayvon's paradox. It increased the body of people who were doing secretarial work. Why? Because secretarial work and administrative work bled into functions that never did history.

16:20
and other more modern examples to think about fuel efficient cars. There was the thesis that that would reduce fuel consumption.  Well, it did the opposite. Why? Because people bought hybrid cars and they drove further. Why? Because it was cheaper and they used more fuel. They just drove more. And so you see this kind of paradox. We're going to see the same thing with AI. I  am not contending that people in certain job classes will lose their jobs in the way that coal mining went away to some degree.  But it's going to be replaced by a new

16:51
set of jobs, a new type of job,  what that is, what we're meant to be seeing. All I know is that the people who are able to harness what AI gives us today are going to be able to do their jobs better, faster, and more efficiently. You are going to be more valuable. And you may have heard a saying out there that people are scared that AI is going to take your job. It's not AI. It's people who use AI who are going to take your job. Yeah. It's the old adage of looking at history and go back and going,

17:20
Most people used to work on farms, collecting food, and then they invented tractors and we're all still working. So  the opportunities there just moves us further with the supply chain to add more value to solve bigger, better and brighter problems. Sorry to interrupt. Just a quick mention of our longstanding partnership with Grata. As you all probably know, the private equity scene is constantly evolving and DealFlow is moving now to proprietary and data-driven processes. Grata provides you with the data and information

17:49
of over 7 million private companies. So if you're looking to improve your proprietary deal flow and improve the data access and reach out to Grata today. Now back to the podcast. So you went through a few examples of  kind of projects that you've worked on there. What I'm interested, especially from, you know, I run this executive search firm, we're looking at AI, we've implanted different bits and pieces, working on a project at the moment. um How do you...

18:18
When you're kind of looking at portfolio companies and even private equity firms, VC firms, and you're those conversations, what's the kind of diagnosis process that you held them with identifying where they can solve problems with,  let's just put it as technology, data, AI, everything rolled into one because  AI is an overused term at this point.  how do they go,  this is a problem we've got,  here's a thousand, can AI solve them all? And you're like, okay.

18:46
action paralysis, there's too many problems here. So  what's your kind of process? Well, typically I approach these things in discovery and  I identify the key stakeholders,  draw up an template for questions, ask them how they do their job, what works, what doesn't, where their pain points are, where they think the opportunities are, their thesis around where the future is. And then you pull that together into a set of insights and identify the situation, complication, key question. m

19:14
you come back with the answers and the answers may come directly. You know, it's the old adage of a consultant asking the client, ask them what time it is and you take their watch and you tell them what time it is. Oftentimes they know the answer. They just need to hear it back in a way that's synthesized and actionable.  Um, and so, you know,  I will go in there. I'll run discovery typically for the front end of a process.  Um, come back with insights,  uh, some provocations. And then from there you have to turn that into something that is actionable. Now, typically again, with my

19:44
product development lens on. Let's say for the sake of argument, you identify a problem or you see an opportunity for automating workflows. And again, in this day and age, that's called agentic AI, right? You then turn that into a requirements document in technical speed. What's a requirements document? Typically a requirements document is threefold. It's a business requirement. Like, why do we need this? What's it going to do for us? It's a functional requirements document, which is how does it work?

20:14
And then it's a technical requirements document, which is, all right, how do we build it? Once you have that, you then go out and find the resources to build it. You may have it internally, you may need to outsource it. That build by partner question is eternal. I'm in the middle of running an RFP process for multiple vendors to go and build an agentic AI product. um What I've directed the team that I'm working for in the PortCo is you've done a magnificent, heroic job with your tech teams and product teams, but you have a lot of technical debt. Why don't you and fix that and outsource the development of the new innovation?

20:44
ring fence it and treat it as a skunk works here, but it succeeds or fails on its own merits. You take that requirement, stock business, functional and technical, and then you turn that into a roadmap. That roadmap has timing. It has gates. It has deliverables, has resourcing and has budget and has ROI expectations. There you have a blueprint on how to move forward. That is a very typical approach for what I do is I work with my clients and it needs to get buy in. A lot of it is politics. It's egos.

21:13
resource scarcity.  It's finding where the, it's finding where the capabilities are, especially for a lot of this stuff on the bleeding edge of AI. um Again, whether it's people who understand what agentic AI is in the first place, people who understand how large language models work. How do you take a foundational model like chord or, or Gemini and then take your proprietary data set from  inside the company. uh How do you match those two things in a way that

21:42
text your proprietary data and isn't just hoovered up into the LLM. So you have guardrails around that. And the way to retrieve that and augment your answers is using something called retrieval augmented generation or RAG. It's a wonky term. Not everyone's going to know what it is. Third party data sets. You may be working for a client. You may have APIs that go into your model that pull in Salesforce data, historical performance data from your client. You need to pull those three things together.

22:10
One uses a protocol called RAG, one uses a protocol called MCP, which is Model Context Protocol, which is largely a way that these AI companies today are allowing third party data sources to interact with their LLMs. All this is frontier technology and a lot of companies simply are not equipped to deal with it. That's where you go to the experts. You go to the consultancies, you go to the vendors, you go to the researchers, you go to the specialists. At a certain point, you have to absorb this into your organization in the same way that most companies now will have.

22:40
web developers or mobile developers or database engineers, right? You didn't used to need that 30 years ago. You do now. You're to have to have the same arms raised for technology, for resourcing, for processes today as you fortify yourself for the future of what AI can bring. So just building on that further, how would you describe what the future looks like for data and AI?

23:06
through the private equity lens. mean, think historically data used to be treated as a kind of exhaustive operations. Again, was backwards looking, it was historical. It needs to be forward looking now.  It's really a fuel for valuation. And in the next five years, every P  firm is going to need an internal AI stack. um Again, that's people, it's products, it's processes,  not just for diligence, but for growth, for attention, for exit planning.  The next wave of alpha, if you will, won't really come from.

23:34
cheaper capital, it's going to come from smarter cognition,  using LLMs, using AI  as a foundation for knowledge. But the secret source and the differentiation is going to be judgment. And judgment is born from our human experience, our individual experience.  And that comes from, again, operating partners, it comes from LPs, comes from GPs, et cetera. So that's what I would argue. And the funds that treat AI as a productivity lever will

24:02
be the ones who really secure that incremental return. And the firms that treat it as an intelligent architecture infrastructure, they'll be the ones who build their densities. Perfect. And tell us about your upcoming book, The Fifth Horseman.

24:21
So the fifth horseman, the fifth horseman is an allegory, if you will. It's a metaphor. It's about how AI is changing, not just business, but the human operating system itself. If you think back in literature and religion for the last 2000 years, these four horsemen of the apocalypse, which are conquest, war, famine, and death, they all define some collapse. The way I'm framing it is artificial intelligence is the fifth horseman.

24:49
It doesn't ride in from their eyes and it actually just looks at us from a mirror. We are the ones who are training AI, every prompt, every search term, every click, scroll, button, share. We are training the model. um If you look at the work of the media theorist, Marshall McLuhan, he said that media are the extensions of mankind. So we have five senses, sight, taste, smell, touch, hearing. um

25:19
Those are just sensors. Those sensors in the same way that your iPhone is not a smartphone. It's just a collection of sensors, gyroscope, accelerometers, cameras, depth sensors, light out. So it's just like as if humans are flesh computers, if you will. Inputs, we have cognition, that's our Nvidia chip. And then we have outputs, whatever we create. uh And AI is really just a reflection of us. It's trained on the corpus of

25:49
Humanities, data, output, creativity, innovations, discoveries, um fire, the wheel, the good and bad press, personal computing, you name it. um So it's not about machines replacing us. It's about us almost becoming those machines. Again, putting on an AI access score and it's algorithmic, it's optimized, it's predictable. So the book argues that the leaders who survive this age won't be the ones with the most data, but the ones with the most judgment.

26:19
It's a war between knowledge and judgment. think about the central thesis of the book, the fourth horseman, fifth horseman, me. It really is. Um, that's kind of, brought, I, I surround it with my own voice, my, my own experience, my own anecdotes, but it's also woven in to not just technology, but history, literature, linguistics, philosophy, psychology. It really looks at the motivators of, of, of humanity. Um, I studied.

26:49
psychology at university. And it's really, I would say, been a through line in all the work that I've done across multiple industries, across multiple types of work. So it's a book that I want to be informative, accessible, actionable, entertaining. So think of it as a sort of art of war meets black and white if you will. But I'm a techno optimist. I think the punchline of the book is

27:18
If I just rewind one more piece of context, you know, um, I teach at NYU's business school. I've done that for the last 15 years where I teach MBAs and executive MBAs and undergrads. A course that focuses on marketing on analytics, data science, digital transformation. And the book is really a reflection of that experience and ideas that I've sort of forged in that classroom and boardrooms of my clients. a fellow colleague at NYU, Stan is a guy called Scott Galloway. Scott Galloway has made a great

27:47
success through his own bombastic provocations and the books he's written and the podcasts he leads with Kara Swisher and the speaking engagements and the consulting that he's done. His first book was called The Four and examined four big companies, Amazon, Meta, Google,  and uh Microsoft, I believe,  and talked about how they really weaponized our instincts. My thesis is that

28:17
fifth horseman is the AI that sits atop all of those and the rider is us. And that is the punchline. AI is built on our inputs. Our inputs in the middle of the Nvidia chipsets, GPUs, the models, the LLMs and the outputs are what we use today for AI and helping us solve problems. you know, whereas we thought that Google handed us the pantheon of human knowledge, um, AI today is, is, is really

28:47
weaponizing that in a way that we've never seen before. In the right hands, that can do a lot of good. But that's the punchline of the boat. The punchline is that that's us. We are the fifth horsemen. I haven't thought of it like that, but I do like that a lot. What do you read, watch, listen to? Do you recommend others should check out?

29:07
Gosh, so much of media is there  jostling for our attention. um It's always a constant battle between books, podcasts, online articles, television,  music, art,  whatever stimulates the senses. You know, uh I kind of oscillate between intellect and chaos. I like reading books like Marshall McLuhan's Understanding Media, Ben Horowitz's The Hard Thing About Hard Things, anything by Mary Oliver. She reminds me a lot, still matters. Suzy Walsh.

29:37
got Galloway, um terms of podcasts, really pivot for industry gossip with a bit of bite.  Um,  Charis Fisher again,  Galloway, Azure Klein, that sort of thing.  Um, TV succession, still the best corporate training program ever made.  Um, but in general, I like to observe the world and sit back,  um, see what stimulates my senses, learn something new. You know, I took my daughter to

30:08
Japan when she was seven, I won a venture capital pitch competition and they flew us out to Tokyo and we took the bullet train down to Kyoto and Kyoto there is a temple called Rio Anji and uh it's a beautiful serene temple and you walk there and there's a uh stone bowl, a wash base where one washes one's hands before  one goes into the temple to think, to meditate.  And around there,

30:37
and kanji in the inscriptions.  I don't read Japanese, uh but I found out what it meant.  And as you walk into this temple, this thing greets you and said, I learn only to be contented. And I realized being an educator for 15 years, having had the benefit of great education for 50 years of my life, I realized that that's what I do. I learn only to be contented. And in the hopes of passing some of those lessons down to my students, my children,

31:07
my colleagues to people I sit next to on a park bench. That's enough for me. Perfect. Appreciate the insight there. was incredibly detailed. If anybody wishes to reach out to you, Jamin, how best do they get in touch, Find me on LinkedIn. My email is open to all. I my personal email. It's jamin.edis.gmail.com. First name dot last name at Gmail. Don't follow me on Instagram. Nobody needs to see that. But other than that,

31:37
plenty of to find me. You can find me through NYU's business school directory.  I love to meet people in person. I'm based in New York City. I travel frequently. um So whether it's an opportunity for a one-on-one or whether it's an opportunity to speak to your colleagues, speak at a conference, speak to an audience, um I look forward to meeting out around the world and learning as much from you as hopefully you learn from me. Perfect. Well, thank you very much for coming onto  the podcast today. Thank you. It's great pleasure.

32:06
Thanks to everybody for tuning in. Till the next time, keep smashing it.