VC And Author James Wang Unveils The Secrets To Unlocking AI’s Potential For Entrepreneurs (#405)

405 James Wang
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Jeffrey Feldberg: [00:00:00] James Wang is a general partner at Creative Ventures, a deep tech venture firm investing in early stage companies solving critical global scale challenges like rising health care costs, labor shortages, and the causes and effects of climate change. They have invested millions of dollars in over 50 companies, including climate tech startup 3E Nano, Onco Precision, which is developing patient micro avatar technology that seeks to improve cancer patient outcomes, and Reelectrify, a world leader in advanced cell level battery control solutions also backed by Toyota. 

James spearheads Creative Ventures AI Investments, leveraging his MS in Computer Science, specializing in AI ML from Georgia Tech.

Previously, he oversaw the launch announcement and branding for Google X's Mekani project, was on the core investment team at Bridgewater Associates, and co founded and managed a non profit consulting group [00:01:00] specializing in microfinance in the developing world. 

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Deep Wealth Nation, welcome to another episode of the Deep Wealth Podcast. While we [00:03:00] have an alumnus of the Deep Wealth Podcast, James Wang is back. I'll put in the show notes our very first interview, but we have a VC investor, a thought leader, an entrepreneur, and now an author, all All things AI. So James, welcome back to the Deep Wealth Podcast.

It's an absolute pleasure to have you with us. Why don't you share with us what's been going on since we last spoke? Because I know offline, I think, my goodness, you have been super busy, but share with us what's been going on with you. What's keeping you busy?

James Wang: Yeah, absolutely, and thanks for having me on again, Jeffrey, and I'm really excited to talk with the Deep Wealth Nation here again. mean, in terms of since the last time, made more investments, of course, that's the typical thing in terms of VC, but I've been getting out there with my sub stack, which I've been trying to really Spend more time on just in terms of educating people on AI, giving them a better sense of what's going on because I think some people, and we were talking a little bit before we started recording, I think a lot of folks were making, having a question of, is this just hype and I think at this point, given [00:04:00] all the things that have occurred, many people are waking up that it's not quite hype. It is probably much more than that. and are suddenly trying to get on the train of, okay, I really need to understand what's going on. So I've been trying to do a lot of that education.

I've also been as you alluded to, writing a book. Right now, at least the tentative title with the publisher is What You Need to Know About AI, and it's coming out later in 2025, doing a pre sale pretty soon. So really been amping up some of these activities because I think it's really important for people to understand what Really, what this stuff can or can't do because at this point, with all the hype, with everything going on there's just a lot of misleading or just, frankly, misinformation out there on what this stuff is.

And I think it's really important for just business owners, just citizenry in general, to understand What this stuff is, what the implications are, so that we can make informed decisions on what we do going forward.

Jeffrey Feldberg: Absolutely. And Deep Wealth Nation, again, this is all in the show notes, go to [00:05:00] WeightyThoughts. com, weightythoughts. com, and that's the sub stack. There you can sign up, you'll get these incredible emails that are sent to you of the articles that are being written, but you can also reach out directly to James that way and have some conversations.

What I love with what you're doing, James, is you take the seemingly complex and you make it understandable. And you have all these different perspectives. One as a citizen, of course, but then as a VC investor, as a tech guy, and you've been around some fairly big companies and some successful projects. So you bring all that in.

It's a very unique perspective. So as we sit here today, and I know things are changing, not by the day, but it seems by the hour. What's been most impressive with you so far, with what you've seen with generative AI and where it started from versus where we are now and where it's going?

James Wang: Yeah, and before coming on again, I took a listen to the last podcast just to remind myself what all the predictions are, and I think they've held up pretty well, just in terms of AI [00:06:00] becoming more and more of a thing. I mean, the thing that I've been really excited about is still a lot of the developments in that space, as well as the progression with the way that AI has been going, especially within the reasoning quote unquote space, and we can dig into this a little bit more.

It's not true reasoning, but the ability for us to actually scale a lot of these different generative AI products, or just deep learning products in general, have, has jumped in a significant fashion. And I think the funny thing is, through 2024, which is, you know, when we had last talked, one of the big questions within the media was, Is AI scaling over?

At least within my world, I kept seeing articles where people were asking this question, and I think by the end of the year, in 2024, with all the things that suddenly had come out, I think the question went away quite quickly. Dramatically, just in terms of, yes, we actually do have a lot of scaling and some of [00:07:00] the scaling is actually very expensive and could change our physical landscape quite a bit with things like Microsoft trying to reopen Three Mile Island with Facebook asking for a four gigawatt nuclear power plant in terms of proposals.

And just for listeners the thing that Homer Simpson worked at, that giant facility, That's a one gigawatt nuclear power plant, just to put into perspective. So Facebook's looking for a four gigawatt one. A lot of these changes are coming up really fast. I think a lot of the things that I was expecting have actually just come to realization much later.

Faster than I had predicted to. And it's just coming through the horizon and we're just seeing more and more of these developments come to pass.

Jeffrey Feldberg: It's interesting because here with this technology is potentially an unintended consequence, not necessarily in a negative way. So, if you look at us in the U. S. and relative to China, I'd say right now China in terms of their power grid is way further ahead and if things were to stay the same, I know they're not, [00:08:00] but if things were to stay the same, we're going to be at a competitive disadvantage in terms of China having much more efficient, cheaper electricity to fuel all these data centers for AI.

I would love some of your thoughts on that and the implications and even potentially a new investment opportunity on the horizon here.

James Wang: Yeah. And I, I've had a lot of fun actually talking with some of my former colleagues at Bridgewater who are much more plugged into the commodity market and everything than I am. But one of the things here is just. With AI, one of the things that I think people don't realize is you can't connect it into the existing power grid.

You can't connect the data centers into the existing power grids. You could actually for some of the old data centers for, you know, internet companies because it just didn't make that much of a difference. They don't take as much power as, say, something AI on would. So we are changing the physical landscape quite a bit in terms of trying to figure out where the Place these things.

But you're absolutely China has much more of a [00:09:00] strategic imperative in terms of building these things out. And the challenge for the U. S., fundamentally, is we just have a lot of trouble building things, period. So being able to build out a lot of these big projects, that's a big question on whether or not we'll actually be able to do it.

I mean, in terms of while we're recording this. We actually had the Biden executive order come down not that long ago. To try to actually push a lot of these different things, streamline a lot of them, and also impose some kind of strange that, again, we can get into this, the restrictions on it as well, but the attempt is to actually make the U.

S. the data center capital of the world, where most of the data centers will be built here. But a big question for us is, with what power, ultimately, it's going to have to be an all of the above kind of thing. And if you're talking about investment opportunities, between, you know, uranium mining, between needing a lot more natural gas and gas, between different renewables, we actually need a lot more of all of the above in order to actually satisfy the kind of the [00:10:00] demands that we're seeing come through.

Jeffrey Feldberg: Yeah, big implications and not to get into the political scene, we'll look to stay away from that. But it's interesting because one of the criticisms with the U. S. has been there's way too many regulations far too long. There's no way where we are today we're going to be able to not only catch up but exceed where China's going.

Well, we've had a change in the political winds, so to speak, now a new administration, a new regime. We have the Doge initiative of trying to cut back wasted efforts and trying to streamline, make things faster. So it'll be interesting to see how that's going to play out in something that, as I look today, and we spoke about this in our last interview, if you go back in history, the countries that controlled the seas with their ships, they controlled the world for the next century.

And in many ways, it's exactly what's going on today, except instead of the oceans, It's now with AI and now we're talking data centers and who's going to house all that. So it's fascinating. From your VC hat, what are you seeing with that before we go back to the technology [00:11:00] for just a moment, but what are you seeing from the VC side in terms of investments and opportunities and the risks and all those other things that go along with that?

James Wang: Yeah, and it's been something we've been looking at for a while, but you can almost look at it from a picks and shovels perspective where, yeah, you can invest in the AI companies directly, and personally, I think that a lot of the large AI companies and foundational model companies are probably gonna have trouble realizing some of the lofty valuations that they've gotten to, which is fine.

That's sort of how these tech cycles and stuff work. But if you're looking at, say, data centers and a lot of the things that need to enable these things, you have a lot of interconnects. You have a question of how do you actually handle cooling. We've seen a lot of interesting things in terms of heat batteries and even ideas around co locating, say, steel foundries, which need a lot of heat, next to some of these data centers, which just generate that heat for free as basically waste product that you actually need to get rid of.

A lot of innovative things, a lot of questions still around whether or not we're actually going to be able to build them, especially here, but there's a lot of interest, there's a lot of [00:12:00] developments within the area, and there's a lot of need as well.

Jeffrey Feldberg: Yeah, it will be very interesting to see how it plays out. I suspect fortunes to be made, fortunes to be lost, all depending on which side you end up on. But once again, it seems like we're in the very early stages of now industries to support AI, which took a while to get going, but now that it's going, we're not stopping.

And I want to put something out there in terms of AI, the technology, just for the listener in the community, in the Deep Wealth Nation to begin to understand. Because it's not just AI, we say AI generically, but behind the scenes, we'll have what we call closed systems and open source. The closed systems being companies that are for profit, whereas the open source, as we know, the open source software, it's open to anyone and it's a community of people.

So, both from the technology side and, James, also from the investment side, closed versus open source, right now it seems that closed has been leading the way in terms of the headlines and what we're seeing. Where do you see that going? Is that going to change, [00:13:00] remain the same, and the implications of that?

James Wang: Yeah, so that's actually fascinating, and I had someone ask me this recently where it's why are there open, maybe to clarify, open weight in terms of some of these LLMs and deep learning companies in particular, why are there open weight companies in general anyway? If you're just giving away your product.

How does that work? It could work in the way that open source traditionally has, and that's a little bit of what Mistral is trying to do and whatnot. Just in terms of context, that's the French company that also publishes their thing their weights and everything, and basically gives, has support and other paid and hosted platforms and whatnot.

But if you look at meta If you look at now China, which is catching up in terms of DeepSeek and Alibaba's Gwen, those are all open weight. And part of the reason for why they're doing this is if you have a business that is separate from needing to charge for your AI, right, versus, say, OpenAI, which Desperately needs to get more revenue and to both justify [00:14:00] its valuation, but also pay for all of the costs that they're incurring is, you know, they recently just raised a multi billion dollar round, and they're probably going to run out of it in less than a year.

And I can say this, and, you know, you could publish this podcast way later, and that could still be true in terms of the next round that they raise. With all of these different things. The closed source models have gotten less and less of a lead versus the open weight models that have caught up. And this is a deliberate business strategy as a whole because these are companies that have a huge amount of money in terms of, say, Meta or DeepSeek, which does not need to make any money from its platform because basically they also have a hedge fund backing them in China.

if you're not the leader, You want to actually try to hold back the leader so that they don't just run away with the prize. So if you think about OpenAI, if they were just left alone to go after this stuff, then they could charge what they want. People would pay it because it actually gives enough productivity to do so.

[00:15:00] And then they'd be able to build more data centers, they'd be able to do more of these things, and suddenly they'd get to huge scale. And sort of take away run away with the prize. In reality, it's probably more like Google would do that versus OpenAI, but you basically have that dynamic. And what the OpenWeight models are trying to do, which I think a lot of people are confused about, is they're actually doing this so that Crown can't be run away with.

So it's burning billions of dollars to ensure that you still have a shot at it.

Jeffrey Feldberg: Sure, and so, you can tell me, James, Jeffrey, you're on base or off base. We'll use meta as an example, and actually, their model has been getting better. And, again, Jeffrey, you're cynical with what you're about to say, I would say MED is not doing this from the goodness of their heart. They were way behind when they got into this.

OpenAI had a huge lead and they said, okay, let's try and shore up some of our losses. We're going to throw off some of these billions of dollars of profits that we have. We're going to put it into exactly what you're saying. We're going to make it open, this open weight model for people, and hopefully we'll take some business away [00:16:00] from OpenAI, give us some time to catch up.

And perhaps at one point we'll have something even better and we can re evaluate our options. And there's implications for that, which I want to get to in just a second, but for the Deep Wealth Nation, can you, hey Jeffrey, on base, off base, with that, I mean, where are we on these open weight, is that more or less, it's not necessarily from the goodness of their hearts, but they have an ulterior business motive here?

James Wang: Yeah, that's completely right. And I think not much to add on that, that specific point, but one thing I will add, it does tell you a little bit about whether or not you want to actually be playing within the investment landscape with these kinds of giants who are willing to basically burn away billions of dollars in order to just keep someone else from, having as much of a lead.

So a question in terms of business opportunities. There's a question in terms of what you want to jump into for the entrepreneurs out there. Do you want to make your own foundational model company and try to compete with this? you want to basically go somewhere else? Where you're able to take advantage of these different trends, and no matter what [00:17:00] happens, as AI gets better, you get better, and you have a bigger advantage as well.

And that's just something to think about, whether you're literally looking at an AI business, or if you have a more traditional or whatever business that needs to evaluate how you're actually going to incorporate this into your existing Whether it's thinking about, you know, your manufacturing business, whether it's thinking about your SaaS business that needs to integrate these things, that's a question to ask yourself.

If the AI gets better, in a more advantageous position or am I in a worse one? And one thing here is you want to position yourself on the side of being in a more advantageous position, because I was also talking with a Again, an ex colleague from Bridgewater trades public markets and other things.

And one of his ideas was that, you know, during these big transition periods in history, what you see is actually, you know, stock markets do well, but there's a big bifurcation. Certain companies do really well, and they [00:18:00] basically Help make up the boom. And certain companies essentially whittle away at their previously huge market caps and potentially even go to zero and become extinct. 

and averages hide these bifurcations, but it's something that does actually crop up over time and this is a time where you have a big kind of transition in terms of this and it's something that you should pay attention to because it it doesn't happen all at once it gradually goes and then you kind of wake up and suddenly you're in this new paradigm.

One example that I give is during the dot com boom Google and Amazon existed, you know, before the dot com bust. But they only really took center stage afterwards. And early on during the internet era, all the newspapers were like, this is great. With the internet, we now have readers from anywhere in the world. We're going to be in a great position and our businesses are going to thrive. And instead, what you saw is after the internet bust and then after the Google's and Facebook's and whatnot rose up. [00:19:00] That ad revenue got sucked away and their businesses were then the newspaper businesses and traditional media businesses were destroyed.

So you're probably going to see a similar kind of shift as new models crop up and as new ways of doing this kind of business start to come to fruition.

Jeffrey Feldberg: Now, it's interesting from an investment side of things, I'd imagine that it's now, okay, I could not necessarily bet the farm, but I could invest in a particular company such as OpenAI, or as these new indexes come up, these new ETFs come up, maybe I'm just going to go for the entire industry and perhaps hedge some of the risk that goes with that.

So it's interesting to see where that goes. But from the business world perspective, when we look at closed source versus open weight, James, I would love your thoughts on one of the things you hear a lot out there for businesses that have proprietary data and information, and hey, who doesn't have that?

Hey, I don't want to put that out there for OpenAI to learn all of my secrets. It's going to be used by who knows who and accessible by whomever, whenever, [00:20:00] that I can't really do that. And for the closed weight, well, maybe that's an option. I can now bring that in house. I'm the only one that's accessing it and no one else is going to see it.

Thoughts on that and how that's going to play out in the business world?

James Wang: Yeah. And there's a few points to sort of tease out there, because you can have a, for example, enterprise contract with one of these closed weights. So, you could have your model companies and basically have them host a thing for you. You could also, yourself, self host an open weight model internally, and that would be the most secure.

Your data never leaves your possession. You can always have it within your particular company. And OpenAI does technically say that they don't train on the data that people put into it, but that's always hard to say. And with companies like DeepSeek the Chinese model company that I mentioned, they explicitly say, yeah, we basically take all of it and train on it.

So anything you put in there that you're not hosting yourself is definitely taken and used. So it's really [00:21:00] something to think deeply about because one of the Distinctions that you will have over time. And I mentioned open weight and closed source models have been getting closer and closer. The reasoning models, I think, are a little bit different in terms of the dimension that they operate on, but just putting that one aside.

They've been getting closer and closer. In a way, for your business application, it could be just fine to use one of these open weight models, handle it yourself, and be able to run it. NVIDIA even had come out with their Some of their new products that are essentially designed for either businesses or even home users to do that.

And you can protect yourself that way because one of the old data is the new oil was a big like saying or whatever at some point or another. But it is actually kind of true in this case. If you think about all the models, ultimately my reason for why I say, I don't know how well open AI or whatever, we'll be able to realize its valuation and everything.

It's because many of the models are interchangeable. The existing data that they trained on were the corpus of the internet. All [00:22:00] of them had access to the same thing. All of them did the same thing. It's the same models. It's the same data. It's all of the, and they have all had massive computed resources.

So what's going to differentiate things going down the line? It's going to be if you have different data. And you have different data that matters for your specific industry and you're able to keep that in a proprietary way.

Jeffrey Feldberg: Yeah. It's huge. And again, Deep Wealth Nation, what I love about what James is doing, and again, go to the weightythoughts. com, sign up. You'll also get updates about his upcoming book, What You Need to Know About AI. These are decisions. These are enterprise decisions, business level decisions. That can really positively or negatively impact our business.

So as an example, if I don't know what's really going on and I'm, okay, yeah, whatever, we'll just put our information out there on an AI system and it's closed source. Well, maybe they're looking at it or, you know what, I want to do an open weight. It's , pretty good, maybe it'll be even better at one point, but it's good enough for today that we're not going to have a risk of [00:23:00] having our information out there.

Would you have known that? And this is what I love about what you're doing, James, you're bringing these issues to the forefront of what the typical entrepreneur, business owner, founder may not have ever thought about, particularly if they're not in technology. We'd love some more of your thoughts on that in terms of what you're bringing out to us in the book.

James Wang: Yeah, totally. And in the book, I do go through specifically mental models and ways of understanding the history of where all this stuff came from, understanding some of the technical guts, but not in a super technical way. It's basically trying to walk you through and build intuition on What these things can or can't do is one of the ways that I've described many of these models is they are glorified autocompletes, sounds like it diminishes what they do, but it's actually a pretty useful mental model for what this can or can't do.

If you have a really, really smart autocomplete, you can actually write an essay with the thing. It could actually give you really good suggestions. Is it ever going to write anything completely novel and creative for you, though? No if you take your phone and keep pressing space [00:24:00] or whatever it is to keep autocompleting, it's never gonna get there.

But why? a lot of the mental models that I try to help folks understand and build is around that. How do these models actually specifically work under the hood? Again, not, I don't go through the math, I don't go through all these things, but one thing that's really important is is that with all of this hype and with all of these things that people are saying about what AI can or can't do, what can it actually do, and what can't it actually do, and what is it actually really, really good at, and what is it bad at?

For example, just talking about something important to understand for business owners. There's been multiple cases where people have Done court cases lawyers have done court cases, or expert witnesses have done court cases and used ChatGPT to write it. ChatGPT has made up court cases, there have been lawyers that have been getting into trouble.

One of the most The funniest, most ironic, it shouldn't have happened, but there was a misinformation expert from Stanford who was giving [00:25:00] testimony on how ChatGPT and these other models could potentially have misinformation or not as an expert witness. They found that some of the data and some of the cases were made up in his testimony because he himself had used ChatGPT and he had, and you know, it's almost like direct irony. Specific things like this that Don't work, because ultimately, again, they're fancy auto completes. They make things that sound like they are real conversations or whatnot, but that's because they're drawing from a statistical distribution. If you're asking it, when did, was George Washington born? Most websites probably say the same thing, so you're probably okay there.

But if you're asking it to do a specific thing for you, with specific facts and knowledge that is needed, you're going to get into trouble, and I don't think people really make the distinction between these things.

Jeffrey Feldberg: It's important, and so on that, let me ask, and perhaps we can differentiate, you hear a lot about an AI model having a hallucination, and perhaps you can explain that to the audience what a hallucination is. With the court cases, though, [00:26:00] was it a hallucination, or just simply made it up because it thought, well, this is really what's being asked of me, so I'm just going to put it out there in your autocomplete example, which I love that analogy.

James Wang: Yeah, it's an extensive section in the book. And my way of describing it is everything is hallucination, actually. And the reason why I put it this way is if you contrast this with the AI that came before, why did AI in the 80s and 90s fail? And it's important to have this kind of historical context.

It failed because it was never flexible enough. Like the expert systems at the time were very rigid in terms of the rules and everything that's required. And if you think about the real world, it's just too messy. You can't have a perfect logical proof to get to the right business decision, to get to the right diagnosis for everything that you do.

That's why they failed. That takes us to today. Why are these models so powerful and so useful? That's because they generalize really well. And their way that they do that is they actually don't know anything. [00:27:00] And the thing is, they have no true knowledge embedded in them. And when they actually do, that's actually considered a mistake.

So if you're able to, for example, accidentally extract someone's phone number from chat GPT or something. That is a mistake. Something wasn't trained right in terms of it. The way that these models work and why they're generalizable is something called the information bottleneck principle.

I go through this more extensively in the book, but the idea is just learn everything and then forget all the specifics. It's like two different students. One student is a book memorizer and has memorized every single fact in the textbook and answers things that way. And then you have another student who doesn't remember a single fact, a single year, whatever, but just knows the general outlines and the general principles.

The LLMs are more like that. And if you think about a lot of life, that's actually super useful, that's how actually most human beings are. We don't sit around and perfectly calculate things and perfectly memorize everything. But you also have to know the limitations of that particular [00:28:00] way of working and what it can, again, what it can or can't do.

And one of the things that it cannot do is it never has true knowledge. The only reason why a lot of the questions that you throw into it, and I know a lot of people who now use it as Google, essentially, where they just ask ChatGPT, what's the answer to this question? The only reason why those come out correct is is mostly you're asking a fairly general question that most people have said the same things about, and thus it's the center of the statistical distribution.

If you start to move out into more specifics where precision is important, it will completely fall apart because it does not actually have any true knowledge inside of it.

Jeffrey Feldberg: And it's so interesting, and James, so you've been walking us through, so you explained to us the difference between a closed source versus an open weight model and the business implications about that. And now you're sharing with us that really all AI, it's a hallucination, it's just to what degree of a hallucination, and just be very careful what it prescribes versus what you take face value and potentially execute on.[00:29:00] 

But I want to ask you something, because I know what I love with what you shared at Creative Ventures, with what you're doing through technology, you're really going towards a utopian world. How can we leverage investment, put capital into up and coming technologies that can forever change the world in a very positive way?

And From an AI perspective, in the early days, let me say early days, I'm talking the past few years, not back when it first came on the scene, in the past few years, you had this very utopian view, well, AI is not going to replace any human jobs, it's going to simply supplement that, and I would say that narrative has begun to change, so from where you are, whether it's with your VC hat on at Creative Ventures, or as a thought leader, and coming out with this book, what you need to know about AI, Where is that narrative today, and where do you see it heading in terms of the business implications, society implications of how we're living, and jobs, and economics, all those fun things.

Where are we on that?

James Wang: Yeah, totally. And the thing here is it would be nice and easy if I [00:30:00] could just say, yeah, AI is going to be great for everyone, it's going to enhance productivity, and there's no jobs that are going away. The fact of the matter is that's not true, in terms of every single tech transition, whether it was the industrial revolution or green revolution, certain jobs went away.

The thing that I'd argue is a lot of the jobs that are going away In a way, and you know, I'll say this in terms of this audience, I say it in a more sort of polite way, or a politique way, or whatever in the book, some of those jobs should probably go away, so what, kind of jobs are most vulnerable to this?

Well, like say, writers are very worried about what these LLMs will do, because yeah, it's a, it's the literal modality that you operate in. It's words that it's spitting out. Is it gonna take away my business? Writing job. Well, if your writing job is something that, again, if mental sort of intuition here, hitting the center of the distribution and doesn't actually require anything that [00:31:00] reaches out in terms of creativity or precision.

Like an article mill. Like if you write for one of these, like Cosmo, Glamour, whatever it is, and you write like 10 best things for summer or something like that, that job's probably going away because there's nothing inherent in terms of deep creativity or whatnot in there. If you're writing something, again, with more precision, with more creativity, with like reaching out in terms of it, that is actually something that the AI can't do because inherently you are working with a Statistical model.

It's going to try to pull things from the center of the distribution. Inherently, it lacks creativity. So one of the big arguments I make in the book is if you are worried about your job and everything, what you need to do is actually position yourself where those human characteristics are much more important and can make a far bigger differentiation than something where, again, It was really just rote work that we couldn't automate away because our [00:32:00] systems weren't quite smart enough.

Now, that's not an issue anymore with some of the AI technologies that are coming up. What AI technologies can't do is they can't add the human touch, they can't be creative, and they can't work in those dimensions in such a way that they can actually, take one of these, take a job that, Like fiction writer, for example, you can't really write a fiction book, and I know there's been news articles or whatever in terms of, people have done this or that, but in terms of true creativity, true humanness for these things those are still very, very safe, because fundamentally these models don't do that.

And we don't know how to get to that kind of human nature kind of thing quite yet.

Jeffrey Feldberg: James, what's interesting with what you're saying, if we took an entrepreneurial view on that, it really isn't much different because in the entrepreneurial world, you better keep on changing to where the world is going. Otherwise, not AI, but your competition is going to come along and erase you from the map.

So you better keep on [00:33:00] finding new problems to solve, be world class at it. And it really sounds like the same thing here with whatever you're doing, whatever your skill set is, whatever your vocation or career is, make sure you continue to hone your craft, be irreplaceable, be very different and unique that maybe AI can supplement you, but it's not going to replace you.

How am I doing with that?

James Wang: Yeah, that's completely right. And, you know, when I was bringing up that example and everything, part of the reason why I was sort of thinking about how do I place this, because for certain audiences, I think there are more people who fall into the category of, yeah, some of this work is actually somewhat rote work that I need to do to sort of make a living.

I think the, Deep Wealth Nation here tends to have fewer of those people and more people who are more entrepreneurial and really trying to go out there on the frontier anyway. So, yeah, it's exactly like everything else. I would encourage people to actually just play around because it's so easy with these, especially with the LLMs, just play around with it and get a sense of what it can or can't do.

[00:34:00] Fun tests that I always like to use is basically any time a new model comes out, I will take a chapter of a, fiction book and basically ask it, write me the next chapter. You know, do you think it's going to be good? Is it going to be bad? Well, if you think about it from the, again, the perspective of the statistical distribution, it's probably going to be pretty terrible.

It's probably going to be the least interesting filler chapter, whatever, and it does tend to be that, and if you push it and push it and push it, eventually it starts to just pull things from thin air and characters that didn't exist and whatever just start falling all over the place. gives you a real good sense of these models, but it also gives you a good sense of what they can or can't do.

It's kind of like the internet era where, you know, if you're thinking about doing an entrepreneurial business or whatever and launching and things, the internet changed a lot of things and it actually just made it easier for entrepreneurs who have a really great idea and something that they really want to get out there to get out there more easily.

Because people can find you on the internet in terms of marketing, but also for distributing some of these things. You don't need to [00:35:00] send people CDs anymore or floppy disks. It's a similar kind of thing here where Play around with the AI stuff. If you're talking about certain products that need to be coded or whatever, the AI can actually help you move forward on that much faster.

That is actually one of the areas, because coding is words and everything, that it can move you forward really quickly. It can't help you necessarily if you know nothing about it, but in terms of creativity and everything and being able to, come up with the idea, it can't help you there. But if you know already where you're going, it can just get you there that much faster.

Just like internet couldn't come up with a idea for you, but what it could do is it basically help you accelerate an idea that you already have and make it easier to get to market.

Jeffrey Feldberg: And so let me ask you with what you're seeing right now with the generative AI, where it is now, but also where it's heading. One of the things that I've heard bantered around is the ability for it to pick up the phone, speak with a human, answer, let's call it level one or [00:36:00] tier one, Technical support or customer service or booking a reservation.

And I've had some demos of these and they're actually pretty good. Hey, Jeffrey, how can I help you today? Well, I want to book this. I want to do that and it answers those kinds of things. And so where that narrative goes is that is maybe not perfect, but it's getting there that at one point, not so much in the U S if you look to non US countries, primarily English speaking, where you have those call centers, They're going to be decimated.

That AI will replace that and those people will be out of work and they'll have to find other kinds of things. So it's not a doomsday per se, but it's not a positive one either. How close are we to that and is that where we're heading? What are your thoughts?

James Wang: Very close. I mean, it's, in fact, we're probably there. And I have actually been seeing some of this. Certain types of jobs will be eliminated, and to be honest, some of these call centers and some of these support centers already are so scripted that essentially there's no creativity or human touch to [00:37:00] it whatsoever anyway.

Those, especially, will be very easily replaced. In fact, you can probably replace them more and have a better experience from AI chat than some of the humans that are just forced to follow some of these scripts anyway. And it's the same thing, actually, with some of these coding farms and whatnot that you have in, again, some of these English speaking or historically English speaking countries in the developing world.

If you need fewer programmers But you just need a few that really know what they're doing, and they can basically very quickly leverage AI to get out the kind of rote, cruft code that they need to write or whatever. Suddenly, you don't need a ton of these very junior kind of programmers. That just are churning out rote boilerplate.

A lot of these kinds of jobs are probably going to go away because, ultimately, they never really had that much human requirement to begin with. It was just that we didn't have automation that could handle even the [00:38:00] slightest bit of variation. Now that it can handle the slightest bit of variation, if you look at it, it was never actually that human to begin with again. If you're talking about these customer service things, I'm sure all of your listeners have had the experience of oh my god, I'm talking with a human, I think, but can I even get, can I get a little bit of human, can I get something out of this person who's just telling me the same answer over and over again?

Yeah, totally. those are already not very human and it's completely possible to completely automate them already.

Jeffrey Feldberg: Well, absolutely. I mean, my own data point of one, when I'm dealing with some of these call centers, it's not the best experience because it's scripted and at the end, they're taking longer than the actual call. If you liked what I did, maybe you can answer a survey and how did I do? Do you have any more questions?

I'm really here to help you and on they go with that. But there's implications here because if my business is in call centers, providing call centers, or if my business is, well, I'm using a lot of call centers. What does this mean? Am I going to be hiring people or am I now going to be going the [00:39:00] AI route?

There's implications here and I really need to know as an entrepreneur, well, what does that mean for me and how do I not become a victim but I become a victor? And that's one specific example, but if we zoom out for just a moment, James, are there specific strategies that we can deploy or maybe even best practices as it continues to change with AI?

How do I move along with it where I'm not going to be, Brilliant like you on the AI side and understand all the technicalities of it, but I have these best practices or these strategies that keep me in the know and for the most part will keep me out of trouble and in the right area. How would you address something like that?

James Wang: Yeah, and I think the real thing here is there's both a lot of opportunity, but a lot of potential pitfalls I actually talked with a few instances of people who did sort of jump the gun and basically go, oh, this is great, AI means that I can replace all my employees.

They did it, and suddenly it's oops, there were certain things and functions and some of the creativity, some of the, human judgment or whatever, that it doesn't replace at all, [00:40:00] that suddenly they encounter, it's oops, I need to hire a bunch of different people back, because I didn't actually understand how it works.

So, the thing that I'd say is, again, similar to the Internet and everything, you don't need to know, The pipes and how a request works. The famous Google interview, technical interview that I went through as well was, tell me the life of a request, and you go through all the technicalities of HTTP, how does this work, and where does this go, and do these things work?

You don't need to know any of that. To use the internet. You don't need to know any of that to make a business on the internet. You don't need to know anything about loss functions, regularization, or any of those words that are coming out of my mouth right now in terms of AI. But you do need to know where their limits are, and where they can actually be enhancing, and what they can do. For example, if we're talking about, again, the call center thing, you may not actually want to be a call center that dismisses everyone and just tries to, buy some of these AI, because guess what? If you think about it a few levels [00:41:00] deeper, I am relying on someone else's API.

To do this thing that now everyone else also has access to if it is actually true that I could literally plug in an AI thing and basically get rid of everything and I'm just charging for that. What's stopping the customer from doing that? What's stopping OpenAI from coming out with a service itself?

Maybe even using your data as you go along to replace you as a middleman in terms of providing this if all you're doing is wrapping up your thing with one of these models. it's something to think about where your value add is and how you actually enhance things, just to give the call center example, we were talking about bad call center examples, but I also had ones that are shockingly good. It's you know, the person is empathetic. They are able to actually help you work through things, whatever. Imagine that with the couple of people within your, say, 300 person call centers, the 10 people who are best at that.

But suddenly, you just give them, essentially, the [00:42:00] ability to handle hundreds of conversations at once. That's actually quite an interesting thing, your distinction is not the model necessarily. That's right. The model getting better is just even better for you, because maybe it just enhances and increases the number of conversations they can take on.

The distinction is the level of care and service that your people have, that you are retaining, that you're able to pay more, that you're able to do these things. It's a way of thinking about it, and it's beautiful. Basically, going through those steps of knowing what are the limits of these things and also being careful about I'm not just wrapping someone else's product and calling it my own and thus actually providing any value other than being a middle layer, which I think a lot of companies are at risk of right now, and some of them are actually falling into this trap very quickly.

Jeffrey Feldberg: And James, so what you're talking about is actually really important. And let me ask you this, back in your days at Google X, and even to perhaps a lesser degree at Bridgewater Associates, I mean, two sterling companies, we know about them. They're doing some [00:43:00] incredible things. To your point of not being another me too, and just wrapping yourself around what's already out there.

If you look to Google X, now most businesses aren't going to have both the thought power and the capital that they have. But if you zoom out for just a moment, at Google X, as an example, how do they approach this whole AI area of, okay, we're going to be a creator, we're going to do something really unique here that the world needs, but they just don't know it yet, how could we take from a multi gazillion dollar entity like that and replicate, parody that thinking process on a much smaller scale, but in our own way for our own business, our own companies to really make a difference out there.

Any thoughts on that?

James Wang: Yeah, totally. I mean, there's parts that you definitely can't replicate in the sense of, yeah, like tons and tons of money being thrown off by a separate business that has nothing to do with that, right, and just tons of free cash flow. But one of the mindsets that you can take, though, right, and one of the things that actually has been pretty critical for some of these players that have advanced the most, so in terms [00:44:00] of, specifically some of the Google DeepMind or OpenAI or some of these other labs, it's an experimental mentality.

A lot of people look at the ability to throw a lot of money at something, or throw a lot of compute at something, or build up a lot of these different things as really the key to doing whatever it is. But if you actually talk to the people pushing the boundaries Of these models, of AI, of all these different things, it's actually the ability to experiment quickly.

That's the true thing that makes a difference. The fact that you have a lot of money and the fact that you have a lot of compute just means that you can do experiments more easily. That's the actual thing that really distinguishes these these labs and everything from everyone else. If you in your own business are able to take some of that encapsulation, maybe you can't throw billions at the experiments and have it just go bad and not care or something, but you can take that mentality and go, I'm going to do a lot of small experiments to see [00:45:00] whether or not this makes sense and works.

It's keeping that kind of curious mind yourself. And being willing to jump into these different things, play with the LLM stuff yourself. Maybe you have a pilot with some customers, like try this out in this way or whatever and see if it actually enhances the experience and makes it better. Be curious and willing to learn about and move forward with some of these things, which again, I think there are a lot of the principles that you've emphasized before, Jeffrey, in a lot of your different podcasts and in a lot of your different frameworks.

Is the kind of thing that ultimately I think you can take away from these huge organizations of what makes them so successful.

Jeffrey Feldberg: And so James, not to confuse simple with simplicity, because you've taken what could be entertained as a very complex question, and you've really distilled it down to the essence. And what I'm hearing you say, and you can share with me if I'm on base or off base with this, time, resources, money aside, have a mindset, have a culture of let's do experiments.

Let's be curious. Let's give our team members some time off where they can just think about [00:46:00] whatever they want to do, even if it's outside of their day in, day out areas. To see where it goes, and I would also suspect, as a company, as a leader, I would need to have the patience and the understanding that, hey, you know what, nine of these ten experiments are going to fail, but I just need one to work, and that's my ROI for everything else.

Thoughts about that?

James Wang: Yeah, completely right. And different, look, different companies will have different models of how to do this. I mean a great book that had recently come out is the NVIDIA Way which talks about NVIDIA and its unique culture, which looks a little bit more like Apple and just all these experiments are actually just proposed to Jensen Huang, the CEO, and he kind of makes the top down decision where to go.

But, you know, it's still a culture where everyone is willing to bring this stuff up, even if it boils up to a single decision maker that then, goes forward or not. Key here is the open mindedness of that decision maker, even from a very top down perspective, of being willing to entertain and do those things, and always [00:47:00] being willing to sort of seize the next opportunity.

And, again, different cultures. If you have a much more dispersed one, a much flatter organization from the perspective of innovation comes from all over the place. That's great, too. And being open and willing to do that also will generate a lot of value. So, you can take different modes to it, because I know different businesses actually do have different kinds of whether it's command or control or more dispersed models that work.

Jeffrey Feldberg: Yeah, that is very interesting, and you know what, that could be not just an episode, a whole other series on NVIDIA, and How they've really championed AI with their chips. And are they going to be able to continue that lead or not? That's a whole other topic, whole other discussion, But, it is interesting. but to double click on that point, using an old internet analogy now, or an old computer analogy now, just to double click on that, if you read in the book that Jensen Huang basically saw some of this deep learning stuff starting to come up and go, hey, we I want a lot of resources thrown at this and I want to see what happens in terms of [00:48:00] it.

James Wang: And this has happened plenty of times and some of these things don't come up with any sort of value or whatever, but he basically said, no, I'm going to actually throw a lot of resources at this because I heard about it. I think it's really interesting. Other people have mentioned it and we're going to take that bet.

We're going to make this experiment and if it works, then great. And as it turns out, it worked. worked and has made NVIDIA very much one of the most valuable companies in the world. And yeah, it's that kind of mentality that generates that kind of value.

Jeffrey Feldberg: It's interesting. I mean, in many ways of what they do, they've created the industry and now you see other companies that are trying to emulate that and can they do it? Will they do it? As always, time will tell on that, but it's interesting, but to your point, they took experiments, they took the risk, and in their case, anyways, it paid off.

Fascinating. Well, before we go into wrap up mode, and my goodness, James, I could go in so many different directions of questions that I didn't have a chance to ask, would have loved to. But let's focus on your book for just a moment. So what you need [00:49:00] to know about AI in terms of big picture wise, if you will, if I can use a fancy word, the gestalt of the book, when you zoom back out as it would pertain to an entrepreneur, business owner.

What can you share with us of ways that it's going to benefit us? And by the way, Deep Wealth Nation, in the show notes, go there, click on the links. You'll sign up for the sub stack. You'll be automatically getting updates on that. You'll learn all about through the weighty thoughts, James's book and what's going on with that.

But what would you want us to know with what's upcoming in that book that can really help us as an entrepreneur, as a business owner, founder?

James Wang: There's all these different things that people talk about AI, but at the end of the day, what you need is knowledge. And not knowledge about, okay, here's how I set up a home server and do this thing.

Not knowledge about how do I train my own or whatever. But knowledge about, from a conceptual level. What is this thing actually? And what can it do and not do? Because you, as a business owner, need to make these kinds of high level decisions and really [00:50:00] understand hey, what's around the corner? What kind of experiments should I do to going back to that point?

How should I actually run my business and think about future proofing things and try to go for it? You can't do any of that. If all you have is headline information about AI that talks about how hyped it is, like how much it can do these things, like either Doomerism in terms of it's terrible, or just Boosterism in terms of how it can do everything.

What you actually need to know is from a fundamental perspective in my intuition, how does it, Actually work. And the thing is, that's a pretty involved question, because again, AI is a really deep topic in terms of, which is why it takes a book and not just like a, unfortunately, a 000 or 2, 500 word post or something, but you need to know the context of it, the history of it, the specifically, how it's working right now, where people have deployed it, and successful examples, and also what some of the things that are coming on the horizon that might change some of [00:51:00] the outline of what this is.

But the idea is, you're still able to take this book and still have this kind of intuition three years from now, and still have it be useful, even though AI would have changed a huge amount since then, because the fundamentals and foundation of how it works have not changed Pretty much forever in terms of like the way that deep learning fundamentally is structured.

But if you have the intuition, you'll have the power to make these better informed decisions going forward.

Jeffrey Feldberg: And so James, what I'm hearing you say, and it's incredibly important, you're getting beyond the geeky, techy things that we don't really need to know. So as an example, myself included, I go into a car, I push the button, I don't know how the engine works, I just know I push a button, the car turns on, and I drive it.

And you're saying, well, if you know the best practices, if you know how to approach it, if you can get beyond the shiny new object syndrome, and big picture wise, you know what to think, what to do, what not to do, you'll be successful. And so as I think [00:52:00] about that, imagine if we had, essentially what you have is an AI playbook is what you're coming out with for the entrepreneur, for the business person.

Imagine, go back now to the late 1990s, and there's a playbook all about this new thing called the World Wide Web, what we now call the internet and e commerce and everything else. Imagine how powerful that would have been back in the day. Okay, I don't understand exactly what's all going on, but I know how to approach it and how I'm going to position my business.

I mean, wow, talk about empires being built from knowing both what to do and what not to do. And so again, you can tell me if I'm on base or off base with what you're doing, what you've Compiled, what you've simplified for us without taking away the essence of it in what you need to know about AI is how, as a citizen, but also as an entrepreneur and as a person, I should be looking at AI, both today, but also down the road, with a systematic approach that transcends the changes in the technology, that I'll be okay with that because I'll know how to approach this and how to think about [00:53:00] that.

How am I doing with that?

James Wang: Completely right, and extremely well put. And yeah, with that internet analogy, again, not gonna talk to you about how the, how all the pipes work or whatever, and what, how, how all the requests work. What is important, for example, is that suddenly distribution is extremely cheap. Start up costs are extremely cheap in terms of it, and there's zero marginal costs once you actually get up to a very high level of, distribution to people, and there's fundamentally Trends that happen with that that basically add, there's aggregators, there's other things that crop up, but that's the kind of framework that I'm trying to give.

And yeah, sure, I'll talk a little bit, again, going back to the analogy of the internet, of similar sort of things, it's okay, here's how some of these things work, but on a high level, just so that you can get the intuition around, this is specifically what the implications are for me, my business, and my life.

Jeffrey Feldberg: Love what you're saying with that, and again, Deep Wealth Nation, when you go to the show notes, click on the links, but specifically, weightythoughts. com, And also in the show notes, James has given us [00:54:00] an early access link to his book, What You Need to Know About AI: arm yourself with the knowledge to thrive in the age of AI. So click on the show notes, go to the link, it's a bit. ly link, and there you'll get early access to the book so you can get ahead of the curve with the strategies and the insights that James has been sharing. 

From your VC hat, from your business, from your entrepreneurial side of things, from just being out there and in the right circles with the right people, you're bringing this to us, and I know when I'm reading your Weighty Thoughts absolutely love what you're doing. You got to keep that up. And James, let me ask you this.

We're gonna go into wrap up mode. There are so many questions that I didn't ask. Is there one particular question or topic that you want to put out there that we haven't covered yet?

James Wang: Yeah, there's also so many different specific points that I can think of that might be important or not. I'll just go back again to the fundamental of you have to have a good mental model at the end of the day of what this, thing does because it's going to touch every aspect [00:55:00] of our lives.

So in terms of if we're going through a lot of thinking about how should I actually handle this, how should I, whatever it is, it's really understanding that it's also beyond LLMs. We're going to actually move very In certain cases, very quickly, beyond just literally these chatbots or whatever, they're going to start being agents in terms of being able to affect things.

Like you're talking about customer service and whatnot. We're not that far from having AI just book your flights and hotel and everything for you and just giving it the tools and the credit card to actually do that. Go do that. All of these different, trends and everything else that are going to come along are going to happen at lightning speed but what you need to really understand is the foundation of, well, okay, I get how this technology works, I get its constraints, and I actually get its weaknesses, too. This is how I prepare for the world, and that's really what you should really focus on, as a business owner, not follow every single, twist and turn of what [00:56:00] OpenAI is doing, their drama with Elon Musk, or they've come out with this new shiny model, or this thing, or whatever.

things that are more fundamental are what you really need to zone in on.

Jeffrey Feldberg: It's interesting. And again, we can go down so many rabbit holes. I would imagine though, that the world as we know it, Looking back, it's going to change dramatically, very quickly. So as an example, what we now call a smartphone. Down the road, it'll be a dumb phone. There'll be some device that hasn't even been invented yet, but to your point, we'll talk to some kind of device.

I don't know what that's going to be or how that's going to look or where that's going to be. Okay, I want to fly from San Francisco to New York. Here's my budget. Here's my timelines. Find me the best ticket. Boom, you're done. You're not going on your phone. You're not going on a computer. You're not going in a web browser.

It's just doing it for you. And to your point, okay, Jeffrey, this is done. Here's the information, and it's leaving at this time. I've sent it to Whatever email or text or whatever the case may be at that time. Well, how do you handle that as a person? How do you handle that as a business? What does that mean [00:57:00] for us to be able to change that?

What gives me comfort is with what you're sharing, what you're bringing out there, and what you need to know about AI and your book and your thoughts with that is okay, well, hey, you don't have to jump on the bandwagon and have it be the wrong bandwagon.

Here's how to think about this, here's how to analyze it, here's how to make sense of something that's right for you or maybe not right for you and a lot to be said for that. So again, thank you so much for sharing that and for doing what you're doing and really making a difference out there. So James, so much.

In the wrap up mode, it's going to be a little bit of a repeat for you. It is a tradition though, and there's a lot to be said about tradition. And here on the Deep Wealth Podcast, it's my privilege and honor to ask the same question to every guest. So this will be version two or round two for you. Let me remind you, it's a fun question.

When you think of the movie Back to the Future, Which is actually interesting, we've been talking a lot about Back to the Future today in different ways. But when you think of the movie Back to the Future, you have that magical DeLorean car which will take you to any point in time. So James says, tomorrow morning, this is the fun part, you look outside your window, not only is the DeLorean car there, it's [00:58:00] curbside.

The door's open, it's waiting for you to hop on in what you do, and you're now gonna go to any point in time. James as a young child, a teenager, whatever point in time it would be, What would you tell your younger self in terms of life lessons, life wisdom, hey, James, do this, but don't do that. What would that sound like?

James Wang: I gave the answer last time that essentially I wouldn't change anything because again, it's the struggles. It's all the different experiences that you go through that make you who you are ultimately. One of the things I have actually told a lot of young folks who come up for, in terms of mentorship, I also a guest lecture sometimes at Berkeley in terms of entrepreneurship there and things like that.

Is try to tell them that ultimately it's you go out there and you basically find your opportunities, go do things, even if it sits outside of the norm not just follow your passions , but go out there and really put your effort into trying hard, into going after some of these things, and don't be afraid to fail, because even those failures will take you [00:59:00] somewhere great.

I always try to tell people, but to be completely honest, I'm pretty sure if I rewinded back it probably wouldn't change things in terms of, reassuring young people that that's the case, I think usually people still have to, you know, fall, skin their knee and everything before they actually realize that later in life.

So, you know, I'd probably tell myself that, thinking about it. And also pretty sure I wouldn't completely take that until too late, I guess, in terms of finally getting here and really understanding that lesson by hard experience.

Jeffrey Feldberg: Well, it's great advice of, hey, try hard, go after your dreams, and don't be afraid to fail. And to your point, hey, you're going to scrape your knee, it's going to be painful sometimes, but big picture wise, it's making you better. And it's, you know what, when we look to social media or even the movies, They make it look so easy and as we know, well sometimes it can be, a lot of times it actually takes a different path and what you're saying really rings true to that.

If you can just show up, stick with it long enough and keep on trying, pick yourself up, go back out there, that's what can make [01:00:00] all the difference. And so, James, I know for Deep Wealth Nation, if they'd like to speak with you, they want to have a conversation, maybe they even have some ideas for you to look into or research for them, or maybe even for their companies, there's some kind of synergies that's there.

Weightythoughts. com is the best place to reach you, correct?

James Wang: Yes, and there's a DM function on Substack once you subscribe and everything. I've usually found that to be the easiest because at this point email is so saturated. so much stuff that comes through. Technically, I'm on X's A. James Wang as well as Blue Sky and whatnot. But those are also pretty saturated channels.

is probably actually the best way at this point to, you know, DM and have a conversation.

Jeffrey Feldberg: So there you have it, Deep Wealth Nation. Weighty Thoughts. Again, W E I G H T Y Thoughts, T H O U G H T S dot com. That's your golden door to get through to James, have those conversations with him, ask him some questions, get some insights, actually get ahead of the curve. And with that said, James, it's official.

Congratulations. This is [01:01:00] a wrap. And as we love to say here at Deep Wealth, may you continue to thrive and prosper while you remain healthy and safe. Thank you so much.

James Wang: Thanks so much, Jeffrey. I'm glad to be back and talking with the Deep Wealth Nation again. 

Jeffrey Feldberg: So there you have it, Deep Wealth Nation. What did you think? 

So with all that said and as we wrap it up, I have another question for you.

Actually, it's more of a personal favor. 

Did you find this episode helpful? 

Have you found other episodes of the Deep Wealth Podcast empowering and a game changer for your journey? 

And if you said yes, and I really hope you did, I have a small but really meaningful way that you can actually help us out and keep these episodes coming to you.

Are you ready for it? 

The dramatic pause. I'll just wait a moment. Drumroll, please. Subscribe. Please subscribe to the Deep Wealth podcast on your favorite podcast channel. When you subscribe to the Deep Wealth Podcast, you're saving yourself time. Every episode automatically comes to you, and I want you to know that we meticulously craft Every one of our episodes to have impactful strategies, stories, expert insights that are designed to help you grow your profits, increase the value of your business, [01:02:00] and yes, even optimize your post exit life and your life right now, whatever you want that to look like.

And every time you subscribe and a fellow entrepreneur subscribe, it's a testament to how together, Yes, we are. We are changing the social fabric of society. One business owner at a time, one liquidity event at a time. So don't let the momentum stop here. Subscribe now on your favorite podcast channel.

You'll never miss an episode. You'll be the first to hear from the top industry leaders, the innovators, the disruptors that are really changing and shaping the business world, and maybe you're commuting, maybe you're at the gym, maybe you're taking a well deserved break that we spoke all about on this episode.

The Deep Wealth Podcast, it's your reliable source for the next big idea that could literally revolutionize your business. So once again, please hit that subscribe button, stay connected, inspired, and ahead of the curve. And again, your next big breakthrough moment, it might just be one episode away. Maybe it was even this episode.

So all that said. Thank you so much for listening. And remember your wealth isn't just about the money in the [01:03:00] bank. It's about the depth of your journey and the impact that you're creating. So let's continue this journey together. And from the bottom of my heart, thank you so much for listening to this episode.

And as we love to say here at Deep Wealth, may you continue to thrive and prosper while you remain healthy and safe. 

Thank you so much. 

God bless.