This video is first and originally published by The RO Show on Youtube.
The world of business is constantly evolving, and with it comes the need for smarter, more informed decision-making. In this era of rapid change, one company stands out for its pioneering approach to data-driven decisions – Complete Intelligence. Founded in 2014 by Tony Nash, Complete Intelligence is an AI firm that utilizes machine learning to revolutionize the way businesses plan for finance, supply chain, procurement, and sales. In this video episode, The RO Show get into the mission and focus of Complete Intelligence, explore the benefits of automation and AI in decision-making, and highlight how this innovative approach is transforming the corporate landscape.
1. The Power of Data-Driven Decisions:
Complete Intelligence places great emphasis on data-driven decision-making. By removing emotion and bias from the equation, they enable businesses to make objective choices based on accurate and reliable information. Traditional forecasting models often suffer from human intervention, leading to adjustments and inconsistencies. Complete Intelligence’s 100% data-driven approach ensures that decisions are based on comprehensive and reliable data, minimizing errors and increasing accountability.
2. Augmenting Existing Capabilities:
Resistance to change is a common trait among humans, especially when faced with the rapid advancements of AI. However, Tony Nash emphasizes that AI is not meant to replace human capabilities but to augment them. Complete Intelligence’s AI technology complements and enhances existing company capabilities, bringing efficiency, accuracy, and innovation to decision-making processes. By automating routine tasks, employees can focus on higher-value activities such as strategy, operations, and creativity.
3. Complete Intelligence: Making Smarter Decisions:
Complete Intelligence’s suite of products, including CI Markets, offers comprehensive forecasting and planning for a wide range of industries.
By leveraging machine learning algorithms, they provide accurate predictions for global economics, currencies, commodities, equity markets, and more. This allows businesses to make smarter decisions based on up-to-date and reliable data. With Complete Intelligence’s AI technology, companies gain a competitive edge by staying ahead of market trends and making informed choices.
4. The Benefits of Automation and AI:
Automation and AI bring numerous benefits to decision-making processes. By analyzing vast amounts of data in real-time, AI algorithms can identify patterns and trends that humans may miss. This enables businesses to make proactive decisions and seize opportunities before their competitors. Automation also improves efficiency by reducing manual work and streamlining processes. With AI handling routine tasks, employees can focus on strategic thinking and value-added activities, driving innovation and growth.
5. Transforming the Corporate Landscape:
The adoption of AI and automation is transforming the corporate landscape. Companies that embrace these technologies gain a competitive advantage by making faster, more accurate decisions. They can optimize their operations, improve customer experiences, and drive business growth. Moreover, AI enables businesses to adapt to rapidly changing market conditions, navigate uncertainties, and identify emerging opportunities. With Complete Intelligence’s innovative approach, companies can navigate the complexities of today’s business environment with confidence.
6. Ethical Considerations in AI Decision-Making:
While AI offers tremendous benefits, it is important to address ethical considerations. Complete Intelligence prioritizes transparency, accountability, and fairness in its AI algorithms. The company ensures that decisions are based on unbiased and objective data, avoiding any potential biases or discriminatory practices. By adhering to ethical standards, Complete Intelligence ensures that AI decision-making is reliable, trustworthy, and aligned with societal values.
7. Embracing the Future of Decision-Making:
In conclusion, Complete Intelligence’s AI technology is revolutionizing decision-making by empowering businesses to make data-driven choices. By leveraging automation and machine learning, companies can gain valuable insights, make faster decisions, and drive innovation. With the power of Complete Intelligence, businesses can navigate the complex and ever-changing corporate landscape with confidence and achieve sustainable growth. Embracing AI and automation is the key to unlocking the full potential of data-driven decision-making in the future.
Transcript
Tony
I think humans don’t like change, right? And AI can be rapid change. And so in, say, companies, there can be resistance to AI and fear around AI. But I think it is really, at least with current technology, it’s an augmentation of existing capabilities that companies have today.
Rosanna
Welcome to the Ro Show podcast. Thank you so much for joining us today. I’m here with Tony Nash. He is founder and CEO of an AI firm, Complete Intelligence. But he started it back in 2014, so it’s been quite some time. We’re going to hear all about it. I’m very excited. You use machine learning for digitalization, automation, and planning for finance, supply chain, procurement, and sales. You have a revenue, a cost, you have something for the markets. You seem to cover it all. We’re so excited to talk about it. And you also have an amazing podcast. So that’s really awesome what you put out there. Appreciate all the value you contribute. How are you doing today, Tony?
Tony
Thanks, Rosanna. I’m good. I’m hot. I’m in Texas, so it’s a little warm here. So I keep telling myself, Only two more weeks left of summer in Texas. It’s not true, but it just helps me get through. But yeah, doing great. Thank you very much.
Rosanna
You’re welcome. It’s always about mindset. And so, yeah, by telling yourself that, it seems more bearable. We’re at 87 degrees today here in New York, so I feel your pain, and we have that humidity as well, which makes it more unbearable. Well, thank you so much for being here today. And Tony, I want to start talking about your complete intelligence. I looked through your website and your business seems fantastic. You started it back in 2014 before all this AI talk that we have nowadays. And it’s focused on making smarter, better decisions. And it’s data driven decisions, which is really important that people understand the importance of that. You’re a disrupter, in my opinion, a total disrupter for making smarter decisions, which is so important. We don’t realize all the biases and heuristics that we have. And so making more objective decisions is key. Could you share with us your focus and your mission with complete intelligence?
Tony
Sure. First of all, Rosanna, thanks for having me. I really appreciate the invitation. When I started Complete Intelligence, I had led research businesses, one for the economist and the other for a company called IHS, which is now part of Standard and Poor’s. And I observed in those businesses and in our clients and other information businesses that did forecasting, that it didn’t matter how complex someone’s forecasting model was, at the end of that process, there was always someone who changed the number. It always just felt a little bit too low or a little bit too high or whatever. At the time, my question was, why have a complex forecasting model if you’re just going to manually change it at the end? When I left to start Complete Intelligence, I wanted to start a company that was 100 % data driven. You take out the emotion and the friction and all that stuff with, say, market forecasting and, say, company forecasting, that thing, and really build up 100 % data driven forecast. I did not start Complete Intelligence to be an artificial intelligence company. I started it really with the idea that the world is a number problem, and we can figure out that number problem within tolerances.
Tony
And so how do we build from the ground up a way to take in as much data as we can process it and then put out a numerical answer that makes sense? So we initially started as a consulting firm. I started the company in Singapore. We really did consulting to keep us going for the first few years. And that was interesting. It really helped me better understandwhat companies and markets would want. And then we really put together our first productand launched in December of 2019, which was absolutely terrible timing. And so we came out with our first product, which is now called CI Markets for Complete Intelligence markets, where we forecast about 1,500 items weekly and monthly. That includes global economics, that includes currencies or forex, commodities, equity market indices, and individual stocks. So we do Nasdaq, S&P 500, FTSE, Nikkei, other stuff, right? Top 50 ETFs. It’s a 100 % machine driven approach, and we track our error rates. So from the time we download data to the time that we publish it, it’s 100 % machine driven. There is no special markets analysts that’s biasing up or down the numbers. And we’re accountable because we publish our error rates.
Tony
So if someone were to come in and subscribe to CI Markets, they would be able to tell our error rates for gold or the S&P 500 or 3M stock or whatever for the last, I don’t know, 24 months or 36 months or something. So we keep that on our website so people can tell. Why is that important? Because we want people to understand the risk associated with using our data to make a decision. So we don’t just, like a magician, pull out a number and say, Okay, stock X is going to be at Y dollars next month. We give you that number, but we also tell you the error rate for the last several years so that you can then make that decision on your own. We do the same in a corporate environment. So we take the budgeting process for a company and we can use use our machine learning platform to augment the corporate budgeting process, whether it’s revenue or cost, whether it’s supply chain cost, volumes, that thing. We take data directly out of corporate ERP systems or directly out of supply chain systems, and we use that to help people understand their ordering costs, their ordering volumes, their sales volumes, their total revenues.
Tony
We even do budget forecasts very deep within a general ledger. So we’ll do it, say, three to six layers deep within a general ledger. So that really gets down to, say, the team level within a company, very deep, much deeper than the way, say, corporate financial planning does budgeting within most companies today. So we’re helping corporate finance and business leaders understand much deeper within the organization what those revenues or costs should be at a very specific level. And again, we do this on an automated basis. Well, sorry, we do it on an Augmented basis. Multi nd basis, but it’s machine driven. And so here’s what that looks like. We have a customer with about $12 billion in revenue. They have about 400 people who work on their budgets every year for their annual budgeting process, and it takes them three months to do that. Not dedicated full-time, but they’re doing it off and on over that three months. So it cost them $5 to $6 million to do their annual budgeting process. So we take that budgeting process, it actually takes us about three days to process that, very detailed. And the first time we did it, our forecast was 0.3 % off of what those 400 people took three months to do.
Tony
Okay? And we were much more detailed than them. And then once we do that initial budget forecast, we transition a company to a continuous budget forecasting. So every month when accounting closes, we redo a 12 month horizon forecast or 18 months, whatever company wants. And so they don’t have to have that dramatic corporate budgeting process anymore. They’re then on this incremental monthly budgeting where leadership always has a 12 to 18 month view of their business. And so that’s what we’re trying to do with artificial intelligence is really reduce the stress, reduce the drama, and the uncertainty within companies. Again, give them our accuracy and error rates at every line that we do, so we’re accountable. And then let the people within those companies focus on their real jobs, which is strategy, operations, making decisions about the business. It’s not building Excel macros. It’s not maintaining Excel models. It’s taking our accountable forecast and letting them operate their own business.
Rosanna
Love that. Oh, my God. As you were talking, that’s what came to me right away was just that they don’t need to focus on these remedial repetitive road tasks. They should be focused on generating creativity, having vision, and elevating the company to new levels. And you’ve taken… It’s only through automation that this is possible. You’re reducing costs, you’re reducing the time. And we’re in an era of declining productivity across the board. And it’s been declining significantly, especially with COVID accelerated. We need new industries. And through these challenges that we’ve had, there are plenty of opportunities. And this is the opportunity. Automation is much needed across all business levels. And being in business myself, I see that. And we’re utilizing automation as key. Going deeper into the numbers, it’s only possible with this machine learning. I mean, like you said, for a human to go through, it’ll take a month and so many costs. And we need to lighten the load on these businesses, and their margins are being compressed as well. So we do need to reduce the cost, increase those margins, and we need efficiency and innovation. And that’s exactly what you’re providing. I love this.
Rosanna
And when you said something that was key, it started with demand. It’s always about what the customer wants and needs, and you’re fulfilling that. And you’ve evolved through the time, and now it seems that you’re utilizing the AI, and it’s just amazing. Having data, information is golden to make these decisions. We live in an era of information overload. And so at some point, there’s just too much information. And so we need the use of an AI to help sort this data and make sense of it. And so I see how you’re utilizing that, and it’s just amazing. Could you add to that? And that whole process?
Tony
Yeah. Rosanna, one of the key things that we try to really drive home with our prospects and our customers is a part of our forecasting process is automating the audit process for a company. So we talk to a lot of companies who say, Hey, we just need to get our data in order, and then we’ll engage you to do our stuff. And we say, Wait, that’s a little bit like making your bed in the hotel room before you leave for the day. You don’t let the Maid do it. You want to do it yourself. So we automate that auditing process. Then we go back back to our customers and say, Hey, these are some things you really need to look at. And so they don’t have to hire a big accounting firm to do it. They don’t have to do it internally. We’ll tell them exactly what needs attention, and we’ll work that out with them before we do our forecast. So we’ve taken that whole pipeline and really made it very straightforward for people. Whether we’re doing the audit or the forecast, we’re doing trillions of calculations every time we process data. So it’s not possible for a human finance team to do that in Excel.
Tony
It’s just not possible. And so, again, we want to take that… There’s always this drive to fudge the numbers a little bit to make them look right. And so what we do is we’re removed from that process. So if people don’t agree with it, they can say, I don’t agree with this because of X. Great. But if that’s implied to the budgeting process and it’s not communicated, then it’s really a risk for FPNA, for the CFO, and ultimately for the CEO. So these are the things that we’re doing on the auditing process and in terms of computation that really drive more accountability and ultimately better decisions for companies.
Rosanna
Love that. That’s awesome. You talked about reducing error, and then also this point that you’ve mentioned twice now about how we like to fudge the numbers. I mean, it’s in human nature to just do that. And then when you remove that element and you make it more objective, it’s because the numbers are the numbers and that’s the data and we can’t fudge that. That’s much, much needed. I think you’re a pioneer, and I think this is just the beginning of this whole movement. And Tony, I have to say you are really part of the revolution in technology that’s driving businesses and elevating humanity. And I’m so honored to be here with you today. This is awesome. I want to ask you because Herb Simon, he’s a Nobel prize winner in the behavioral economics field, and I’m sure you’ve heard of him. He says a wealth of information creates a poverty of attention. And so we know we have bandwidth constraints, we have opaqueness, we have all these issues with banded rationality. When you create these data models and this AI, this machine learning, how do you distinguish and decipher the signal from the noise? I’m sure you’re using feedback loops, but could you tell us about that process and how you decipher that difference?
Tony
Yeah, sure. So we’re using a lot of different approaches to understand what actually is the signal and what actually the noise, as you put it. And so part of it starts with anomaly detection early on because it doesn’t matter what corporate data we’re looking at, there is always some noise. We’ve had companies that have had to go back and restate their previous years of revenues because of, let’s say, they were double counting information, these sorts of things. Because if we hadn’t taken that company s data through our audit process, they would not have known that they were double counting that information. And so we would then think that that was actual historical data, and that would have been huge noise. So we’re going through as much as possible in taking that historical noise out of the process. That’s an important first step that is, I think, often underappreciated. Yes, I agree. Because, again, if we have problematic historical data, it’s going to be problematic forecast data. So we put a fair bit of attention on that and really pulling that out. Going forward, we’re doing multiple iterations of potential futures when we do a forecast.
Tony
And so we take our customers through the process. We have an onboarding process with all of our customers. The first phase is auditing their data. The second phase is doing what we call in samples or backcasting or something where we take an actual historical period and forecast in that actual historical period to understand how we would have performed. That is another phase of understanding where data could be problematic and noisy. So we’ll typically do a few different historical periods for a customer so they can, first of all, become accustomed to the error rates that they would see or the accuracy they would see from our output, but also so that we can see if a number is veering off somewhere, why is that happening? And then finally, we’re doing what are called out of sample or actual forecasts where the customer starts to see live in the wild forecasts. And from then on, we’re doing live in the wild forecasts unless they want to add, let’s say they want to add a different business or they want to add a different, say, vendor or something like that, then we take it through that same process and then add it into whatever we’re doing live for them.
Tony
I would say there are multiple layers and multiple processes to separate those two things to understand what is real information and what is just, as you say, noisy.
Rosanna
Absolutely. Wow. This is very exciting because this is much needed in businesses. I have to say, when I was reading about your company and and how you started, it said that you built the business from the ground up. And that’s so impressive, including the data science, the software development, with the operations sales market, all that. Tony, please tell us about your background and how you came to this.
Tony
Yeah, it’s interesting that you say that. We are not as a business, and this is not a knock on AWS or Azure or anything, but we are not using AWS data science tools or Azure Data Science tools. They’re all our own data science tools. Because if we’re using AWS or Azure data science tools, then basically they’re the business. And so we’re just the front end of that business. So we’ve developed all of this stuff, preprocess, the actual process, post process, all of this is our own. And what is that like? Well, it’s iterative. You have to have a period of time over which you learn what a good baseline of that process is. And then also, we had to spend some time understanding what the most important elements of that process are. And we continue to iterate that process, whether it’s on the… Just last week, I was working with our data science team on our post process to understand, okay, what is good data coming out of that process? And how do we continue to refine that process so it makes sense? And then we’ll go back into the prep process, and then we’ll go back into some of our forecasting methodology.
Tony
We regularly go back and review all of this stuff because data changes, the economy changes, the data science changes, customer awareness changes, all of this stuff. And so we have to understand, we can’t look at data as if it’s not a changing and nonresponsive element. Data always responds to the environment. And so if we don’t change, then the data will get away from us. And so we always have to be checking out new forecasting methodologies, looking at what’s efficacious, what isn’t efficacious, what’s not always additive, we’re not always adding things to our process. Pardon me. In some cases, we’re removing things from our process because maybe they’re no longer valid. And you’ll hear, for example, every so often people are very excited about a new, say, data science methodology. And we’ll try it out and we’ll look at it and we’ll run it alongside our existing methodology at times and find out, Yep, it’s efficacious, let’s fold it in, or No, it’s really not what all the buzz is about and we don’t fold it in. So I would say for people who are reading data science literature or data science media, there’s a lot of hype about different data science approaches at times.
Tony
I would caution people to look at whether the person writing about that is actually a practitioner, or whether there may be someone who does an occasional video, or maybe they’re a Python programmer who doesn’t really do it at scale or something like that. A lot of these things are really cool in theory, but they may not necessarily work in practice at scale.
Rosanna
Exactly right. We live in a state of flux, constantly moving, so many moving parts and all elements of business, macro economics, micro economics, and things are always changing in technology and everything. And that’s the state we’re in. Part of life is always moving. And they say you’re either growing or dying. And I had died up that with businesses as well. You’re either improving or you’re declining. And we always choose to improve and be our best. And at the same time, not everything works out and you got to keep modifying, tweaking, testing things out. And if they don’t work, you put them aside, you move on. And it’s about being humble. And I read your six company culture points, and I love that. I had to say that it reflects in everything you say, Tony, it’s about being the best you can be and being the best at what you do and not complaining and always pushing yourself. And that’s exactly what you said. You keep testing and you want to provide the best. And if something’s not working, you just say it’s not working and you move on. It’s about being humble. And I love that culture that you create at complete intelligence.
Rosanna
And you have to be that way in order to be successful because that’s how information is. And one thing one day could be the right way, and then next day you realize something new came in and you reserve the right to change your mind. And that’s very important. It’s not flip flopping. It’s about being the best and improving. So I’m all for that. I want to know about your background. I know you lived in Singapore and you have a strong macroeconomic background. And please tell us about all that and how it led you to starting this company.
Tony
Sure. Yeah, thank you. I started my career in global logistics. And starting there really helped me understand how world trade works, how systems work, how cost buildups work, and really how global data flows together in the international system. That, when I was 24, led me to my first overseas job opportunity. I lived in Amsterdam, then the company moved me to London, then they moved me to Florence, Italy. And so early on, I was involved in international discussions, global discussions, going to markets I never thought I would be in, and say, North Africa, the Middle East, Eastern Europe. This was back in the 90s. So early on, I was involved in these data heavy, customer centric global discussions where things like cost, things like workforce consideration, things like geopolitics had to do with what I was doing. So I’ve had to develop this multi tier view of the world from very early on. I then went into media. I worked in Silicon Valley for some media companies, and then I went to grad school. I studied diplomacy and International Relations. Again, that added more layers on to my global view of the world. I’m not trained as a…
Tony
I’m not formally trained as a coder or a programmer. I’m more formally trained as a, I guess, information and, I guess, power politics, power dynamics type of theory person. So in 2003, I was asked to move to Singapore to help turn around a telecom firm, a privately funded telecom firm. And after I was there for three years, I then was asked to get involved in a new telecom company in Sri Lanka. During the Civil War. That was a very complex problem to solve. We sold that company after two years. Then I joined The economist. All of this while I was living in Asia, I ended up overseeing global research for The economist. Again, all of this has to do with geopolitics, economics, company information, global trade, all of these different things. And that’s what ultimately led to complete intelligence. Singapore was a very interesting place to live. We moved there at the end of SARS. This was this big pandemic they had in 2002, 2003. And then we saw Singapore make its way through the financial crisis and then become this very expensive global city, very glitzy and that thing. And then we moved back here, back to the States.
Tony
But it was a very interesting time. Time to be in Singapore. It was a very interesting time to be in Asia. We saw China go through a lot of changes over that time period. We saw India go through a lot of changes in that time period. And so I feel like I had a front row seat to a lot of the changes in Asia. When I moved there, business process outsourcing, BPO’s were booming in India, and it was still a relatively new concept, a relatively new concept. And now that’s matured and we’re two generations beyond that. China was very much a low level cost arbitrage manufacturing location, and we’ve seen China really come up. I don’t know if your viewers remember, but when I moved to Asia, there was an issue where a Chinese telecom engineer was taking photos of a Cisco router so that they could potentially use those plans for their company. And so the technology in China had a long way to go to catch up with US technology at the time. And since then, they’ve really closed the gap. And it’s really been amazing to see a lot of that progress in China.
Tony
And so we saw a lot of that happen in Asia. And we just felt like we had seen enough and wanted to come back to Texas. And so we came back and I moved complete intelligence with me.
Rosanna
Awesome. Well, we’re so grateful that you did. And we’re speaking to today because I have to say you bring such a wealth of diverse background to this company and to all of our discussions. And this is the geopolitical, you mentioned that. And I have to add that’s another big one where anything is possible. And it just seems to be so much change going on. We have multiple players that are all interconnected. There’s no longer… We have transnational borders and with this all information and so social media. And we live in a very interesting age and we call it the age of rising disorder, randomness, entropy. And I recently spoke with an international relations scholar on this. It’s just mind blowing just how much is going on in the world. And you spoke about China and we’re going to talk about China. They’re geo economic competitors with the US. And like you said, you saw the transformation, how much they’ve advanced in technology. It’s just amazing how far they’ve come. And there’s so much change. So I’m so excited to talk more about that. And I know that you are a speaker and leader of closed door dialogs, and you talk about markets, economics, risk, and technology.
Rosanna
So I’d like to go into each of those areas and go behind closed doors and get what’s going on in those areas. But first, I want to say Tony N ash nerd. That’s how you’re known as on Twitter. What does that mean? Did you give yourself that name? Or are you calling a nerd?
Tony
I absolutely did. I mean, I might as well embrace it, right? So it’s who I am. It’s what I’ve been my whole life, so I might as well embrace it. And it’s good. I think that’s my Twitter handle, and it just helps me to on really talk about anything nerdy. So it could be tech, it could be geopolitics, it could be coffee, it could be really anything. I dig into a lot of different things there.
Rosanna
That’s great. I love that. I always tell my kids, if the nerds rule the world, they’re the ones driving technology and innovation and effectiveness and efficiency, and they’re the ones improving and changing the world. So we love that. We call ourselves a family of nerds as well, always learning and growing. And that’s what we’re lifelong learners. So let’s learn more about… Let’s see, let’s begin with technology. I think that’d be a great place to start. I say we talk AI, and I think it’s a much needed solution for humanity. Like I said before, challenges bring opportunities. We have declining productivity, we need automation. What do you foresee as challenges and drawbacks with AI? And what do you think about this fear that we have? A lot of people are very fearful of it. It’s the availability heuristic. They think of Terminator, or they think of some movie or something where AI takes over the world. Please tell us your thoughts in that.
Tony
Yeah, I think humans don’t like change, right? And AI can be rapid change. And so in, say, companies, there can be resistance to AI and fear around AI. But I think it is really, at least with current technology, it’s an augmentation of existing capabilities that companies have today. I don’t necessarily see it as a full substitution of what companies have today. And so it’s similar to… 50 years ago, companies had typing pools. Corporates had typing pools where a bunch of people would type up letters and memos and reports and all that stuff before we had PCs. But what happened to those guys when typing pools moved away? Well, those guys got different jobs. Those skills didn’t just disappear. They had different jobs. And so that’s what I see happening with artificial intelligence is AI is there to augment existing capabilities and enhance existing capabilities. And so when I talk to business leaders and companies about AI, my main point is, being afraid of AI is not going to be constructive to anybody. It’s not going to help anybody out. Now, having way too heavy expectations on AI is also not constructive. These technologies are relatively new.
Tony
They have to be introduced gradually, and there has to be change management around their introduction. And so I think when we think about it with that respect, I think all of those processes or all of those activities give workers today the opportunity to, first of all, understand how AI will impact their jobs. And if they think it’s going to impact their jobs negatively, then it gives them time to use their skills and apply them in a different way. So I don’t necessarily think we are in a fully disruptive AI environment because there’s a difference between artificial intelligence and artificial general intelligence. Artificial general intelligence is a, say, fully autonomous decision machine. We’re not there yet, and we’re a long way from being there. People look at something like chat GPT and say, Oh, it’s just like me talking to something, and it’s not. The way I describe chat GPT is it is a way to summarize Google searches and make it readable. So instead of a bag of links, you’re getting what appears to be some synthesized answer. But in fact, it’s the most frequent responses to that type of Google search within a readable narrative context.
Tony
That’s all it is. So it’s not magic. It’s not going to take huge number of jobs away. It’s going to make jobs easier, actually. And it is already making job easier. So when you look at what we’re doing, it’s not magic. It’s not going to take jobs away. It’s going to add to people’s jobs. So AI is simply math and code. That’s all it is. It’s statistics and code. And so, anything that can be done with statistics today can be done with AI now and over the next, say, 5 to 10 years. Things will get really sophisticated in probably 10 years’ time. But right now, AI generally is pattern recognition. That’s what it is. And you look at almost any AI application, and it’s simply pattern recognition and re presence presentation of patterns in a way that is understandable to the person who’s reading it. That’s a really boring way of saying what AI is, but in general, that’s what it is. When you look at a lot of, say, it administrative work today done by humans, some of that is pattern recognition. In the same way we had a typing pool 50 years ago, we need to take that pattern recognition activity and turn it over to the machines because they don’t get bored, they don’t get distracted, they don’t feel political pressure to change numbers in a certain way.
Tony
So we turn that pattern recognition over to machines so we can do our individual jobs better. Does that make sense?
Rosanna
Absolutely. I love that. Pattern recognition is key. And I talk about that with expertise based, intuitive decision making. And that’s something that experts have with vast experience. And they’re actually pattern matching and they’re recognizing… They’re basically comparing patterns against recognizable prototypes in their heads. And you do that with all of your vast experience that you have with all of this. And this is a computer. This is actually the AI is doing that with all these different fields and decisions. And I think that it automates, it makes things seamless, and it reduces error, like you say. And that’s the simple… I love that definition. Now I’m going to write about that. It’s pattern recognition at its core. And that’s when I like to use Bing, and I go on there and actually, our middle son, he’s in computer science, and he’s like, You know, B ing is better than chat DBT with a lot of these things. So I’ve been using B ing and I love it. It’s basically like, instead of me asking Google, I’m going to check to B ing and it’s faster. It summarizes everything for me. And as you said, it’s pattern matching, and it’s just amazing.
Rosanna
And I just love it. And it’s so efficient. It makes my time easier, faster, and it minimizes my time. I was writing something, I had to write something, I need some information about something. And usually it takes hours. You have to go through different sources. Imagine back in the 90s and the 80s, I had to go to the library and research things. Now I can just ask, for example, a question and I phrase it properly and I’m very specific and I get everything I could want. It’s just amazing. I think another point that you said to your point about change, yes, we have fear of change as humans, and that’s just a natural fear we have. But when we understand that life is change, we’re always changing. And whether we like it or not, change is constant. But just embrace the change. I think we should all embrace this AI, and I think it’s making our jobs easier and I think we need to think different. Even though maybe some jobs are being, they say, eliminated or something, it’s for the best because now we’re able to focus on greater things, creating, elevating our businesses and humanity to new levels instead of being bogged down with auditing and data and all kinds of compiling of data and information.
Rosanna
So Excellent points there. Thank you so much. Now, I want to ask you, are we just at the beginning of this AI revolution? I call it a revolution because I think it’s like an industrial revolution. This is just a new level. Are we just at the beginning? And you said 10 years before we see major changes. What do you foresee in this timeline of AI for, let’s say, the next 10, 20 years?
Tony
Yeah. I think, yes, we’re at the beginning. I would say advanced beginning, but we’re at the beginning. I think what we’re going to have is probably another 2-3 years of excitement over AI. I think inevitably, we’ll have some very high visibility projects that will fail, and it will cause corporate skepticism toward AI generally. This is probably 2-3 years out. And you’ll probably have a few years of real skepticism now. I think generally, a lot of that hype is done by consulting firms who are really looking to build out long term projects with big corporates. And so I think in 2-3 years, as you have some colossal flops with corporates, I think corporates will then realize that they shouldn’t necessarily go to consulting firms to develop their AI. They need to go to technology firms to develop their AI and have consulting firms manage the change management process. So there is a role for consulting firms. It’s just not in developing technology. So there will be, again, I think a series of colossal flops where companies have spent tens of millions of dollars on AI when it’s not really AI. And so there will be a pullback for a few years.
Tony
And then companies will recalibrate toward technology firms to deliver that. And then I think in the 2030s, we’ll see a rapid acceleration of the acceptance of AI across, say, enterprise activities. So but I think inevitably there’s always a hype cycle where there’s hype over acceptance, over expectations, pulling back, and then things come back in a more straightforward way where there’s no longer mystery behind what AI or whatever the technology is. It’s much better understood, and then it’s implemented much more, say, rationally.
Rosanna
I love that. You are brilliant. I have this down. I just didn’t tell you right now. I love this. This is amazing how you apply the cycles of human nature, human emotion. These are the same cycles that play out with the markets, with the crypto markets, equities, and just businesses. The way adoption occurs is that there’s that initial euphoria and hype. Everyone thinks it’s the greatest thing ever, and everyone just so excited. And they get a little ahead of themselves because it’s human emotion and they’re in that euphoria state. And then they get a little ahead of themselves, so they get some failures. And then all of a sudden they think, oh, this isn’t good. Oh, no. And then they start to realize, maybe we went too far. And then they get not exactly what you said. Those failures lead to negative sentiment. So you go through maybe of a darker period, or you could say a bear market, or some type of just negative cycle, and then you get that realism comes in and it’s like, we didn’t have to be so high, we didn’t have to be so low, and now we’re more neutral, and our risk perception versus risk reality is more of a neutral neutrality, and we’re basically where we need to be.
Rosanna
So I love that. That’s excellent.
Tony
This is where I think, Rosanna, where I think the guys who are looking at the low level, very discreet AI activities today are the ones who are ultimately going to be successful. The ones who are looking at the very high level, say, visionary AI projects at a corporate level, those are the ones that will inevitably get bogged down or underdeliver or something. And so if I had any advice for your viewers is look at the very specific, discreet AI projects right now. Be sure that you can carefully identify how you’ll measure their success and make sure that there are high frequency milestones as you deploy that. Please don’t look at the high level supervisionary AI stuff right now because we’re just not ready for that stuff right now. It’s a lot of promises and the delivery may be there, but it’s easier to get the discreet low level wins right now. It’s much better spent money than it is the high level visionary AI stuff.
Rosanna
Excellent points. That’s actually my next question was how can people go into AI, get more accustomed to it? And what’s this lower level that you’re speaking about? Could you please give us some examples?
Tony
Yeah, think of a discrete problem. What problem do you have? Is it my inventory levels are out of control, or I don’t know how to forecast sales, or we have bottlenecks in a certain part of our organization, or something like that. With AI, it has to be information that you use. Look at an information problem that you have where information isn’t really being used well, and then pursue that path. I’ll talk about something that we don’t do, so it doesn’t sound like I’m selling AI. I was talking to somebody last week, like I’m selling my company. I was talking to somebody last week who said, I need to use AI to monitor what my workers are doing in the warehouse because I want to make sure that they’re meeting the productivity that they need to be. So that’s more computer vision really than it is what we’re doing. And so there are companies out there who do that, and they can identify individual workers and see what they’re doing and make sure that they’re meeting productivity needs and so on and so forth. So I said, you may want to check out this company or that company to pursue that.
Tony
So this executive is not looking at a huge visionary AI deployment. He has a very discreet problem that he wants to solve. He knows what success looks like. He just needs to find the vehicle to bring him that success. So for us, with respect to complete intelligence, is… So for example, many of the manufacturing companies that we talk to, their error rates to forecast their materials for manufacturing are often 40 % or more.
Rosanna
Agreed.
Tony
Now, people who aren’t in manufacturing will hear that and go, That’s crazy.
Rosanna
But we are a manufacturing company.
Tony
I agree. Right. And so people who understand manufacturing know that that’s true. And we have a customer made major company in health care, hundreds of billions of dollars a year. Their average error rate for forecasting their materials is around 20 %. Now, again, this is a huge company. 20 % is better than average, but it’s still 20 %. When we applied our process to their problem, the average error was around 2 %. Wow. So using our process, it’s tens of billions of dollars of difference for them in using our process. So you take the average in manufacturing and say 40 % error, this company had 20 % error, and then they work with us and they have a 2 % error. And so it’s massive orders of magnitude difference when people look at… They had a problem with cost of their ordered goods. It was all over the place. They knew it was bad. And so we helped them solve that discrete problem, and it’s very successful.
Rosanna
Wow, I love that. I agree with you completely. In the manufacturing process with supply chain management, there are a lot of mismatches, a lot of inefficiencies which cause that higher error rate. I want to ask you two questions, but let’s start with what causes from what you’re seeing from CI, complete intelligence, what are you seeing causes these high error rates in manufacturing that exceed 40 %?
Tony
I think a lot of it is… Well, part of it is the approach they take to forecasting their cost expectations. So I think there are a lot of, say, legacy ways of doing that that are maybe a moving average or something fairly straightforward, which intuitively makes sense. Or oftentimes we have people who say the price of X is linked to a certain index. There is an assumption that the cost of that item is always linked to a certain index. So in that assumption, there are two points of potential failure. The first one is that X is somehow correlated to that index. The second is that their forecast for that index is correct. And so we pull all of that apart for people and help them better understand what is happening there. So that’s one point of failure is the oversimplification of those cost expectations. I think another potential factor is the political pressures for people to budget a certain way. Let’s say costs are going to go up 15 % this year, just hypothetically. But your boss only believes it should be 6 %. You’re going to put 6 %. There are multiple layers of political pressure on things.
Tony
Let’s say it gets up to the finance team and they say, Hey, look, we know it’s you feel strongly it’s going to be 2 %. So you go from a 50 % expectation that your person on the line level understands all the way to a 2 % expectation because that’s what finance needs to meet their budget. Okay? So there are multiple layers of political pressure to to define their forecasts in a certain way. And we can go into a lot of other things, but there are many layers and many causes of those numbers being 40 % or higher.
Rosanna
Absolutely. Thank you for mentioning those points. I agree, the oversimplification is key. With manufacturing, there’s just so many different elements vertically. And for one of our businesses, we start with the raw material and we go all the way to the end user. Sometimes we do the lifefo method, first in, first out, we do that as well. And it’s hard to attach those costs properly to each of the items. And we find that a challenge. And they’re changing. We always say, things are changing every day to attach it to an index that’s always changing. Let’s say you have a raw material that’s been hanging around in your warehouse for three, six months. Wow, what a difference in prices we’ve had this past year. So in order to have these margins margins match accurately to what the real margin is, forget about that. We’re at the point where we’re just ballparking things, and it’s just very challenging with this data management. So there has to be a better way, and I think you can provide that, Tony. So very exciting to see what else you do in manufacturing because we need that improvement significantly. Now, you mentioned the lower frequency, the baseline of the AI.
Rosanna
I want to talk about this high frequency AI that should be avoided so we don’t aim too high. Could you give us some examples of something that’s really ahead of where we should be with AI right now?
Tony
Oh, okay. So I’m sorry. Instead of high frequency, I should have said visionary high level.
Rosanna
Visionary high level. Okay, perfect.
Tony
So a lot of these are, let’s say, people want enterprise wide projects or something, huge high level. You see a lot of a lot of this stuff, say, come out at a board level, where someone saw a speaker, a TED speaker, or somebody at a conference or something where they’re saying companies should be able to do X with AI within their company. I’m not really thinking of an example right now, but often these things come from the board level down. And so then a consultant is hired and that plan is developed and then the methodology is developed after that. So the problem with those types of enterprise wide visionary AI deployments is the problem is not discreet enough and defineable enough to where the company can define success. So again, if I’m, say, on a board on, say, the technology team or the revenue team or something on a board, or if I’m a CFO or whatever, whenever an AI plan comes across my desk, I would always want to be careful to understand, first of all, how do you know when you’re done? What are the milestones for success? And what experience does this person have doing this stuff?
Tony
And how scalable will the result be? Meaning, if I hire that consultant and this is a long term contract for them to manage the AI, then it’s just a bespoke software project. So you have to really look behind what those high level projects are doing and say, Okay, what tools are they using? Are they open source tools? Are they tools that are being developed specifically for us? These sorts of things. Or have they been deployed many times before? This thing. So whoever is asking, whoever on the corporate team is looking into these projects, or if you’re on a board, you really have to look a couple of layers below to understand not just a global consultancy firm and whatever. You have to really understand layers beneath of how efficient will this and how effective will this deployment be? And again, what problem are we solving? That is a big question that people have to ask about AI for every decision they make.
Rosanna
Excellent points. The key to entrepreneurship is problem solving, and that’s what I find that I’m doing every day. It’s always solving new problems. And part of this expertise, intuitive decision making is being able to see patterns before those problems arise. And that’s exactly what this AI seems to be accomplishing is this pattern recognition, pattern matching. And it’s always about catching the signs of a problem emerging before it does, because you always want to get those problems before they get out of hand. So excellent points. Definable, I love that. I think that’s key. And it’s about scalability and repeatability. And those are very important points with using AI to maximize the benefits available to us today in 2023. So I want to talk about AGI. You mentioned that AI versus AGI. What is AGI? And please tell us about that. And when can we expect that to actually emerge as something that’s going to be more commonplace?
Tony
Agi is artificial general intelligence. So when a lot of people think of AI, they think of robots who can act on their own and replace people and all this stuff. I think that is probably at least 15 or 20 years away because, again, a lot of what we’re doing now is reactive, say, machine learning. It’s not necessarily independent. Even when people use methodologies that they say are independent, they’re not really independent. I think we’re probably 15 years away, and that is… Do you ever see that Will Smith movie? I robot, right? Yes. The robot is helping the old lady or whatever and making decisions and making suggestions and all that thing. That’s probably 15, 20 years away, maybe further. I’m not exactly sure, but that’s not right around the corner. And a lot of what we hear about… I saw something over the weekend saying that an AI wrote a computer program all on its own. Well, okay, yes, it did that, but it didn’t necessarily think of the idea to do that on its own. So So there is a prompt by an external actor to make that happen. We’re not there yet. We’re a ways away from it.
Tony
The idea that robots are going to rule the world, I don’t really think is going to happen in my lifetime, again, because we see what the capabilities are of AI today, and it’s not what many people fear. It’s good, successful AI is pretty mundane, actually, and it’s about productivity. It’s not necessarily about robots ruling the world.
Rosanna
Well said. It’s not about control and power. It’s about assisting us with these very important tasks and productivity. That sounds great to me.
Tony
One thing I’ll add here, though is, since we’ve had computers in the last 30, 40 years, widespread, we’ve always had viruses. Viruses are effectively bad actors and bad lines of code, malicious lines of code. When you hear stories about this AI did something bad, when I see that, I think that’s a virus. It’s the same thing as a virus. It’s not that the AI is doing something bad on its own. It’s effectively a virus that’s introduced into that code to make it do something bad. We need to be really careful as we read news about AI that when bad stuff happens, it’s not necessarily what was originally intended for that AI. And so, again, I consider that a virus.
Rosanna
Very nice. I like that intention versus the virus itself, and we need to not confuse that. Very important point there. Thank you so much for for for detailling that, Tony. I want to talk about risk because that’s another topic of interest here. We have AI, but now risk. Your cost flow, you have three parts to your amazing, complete intelligence. We’re going to talk about the CI market, but you have the cost flow and the revenue flow. So important because I always talk about margins are key. And it’s always about increasing those margins, we want to raise those revenues, decrease those costs to widen that margin. So you work on both sides of that, which is important you tackle each. And so with your cost flow, you talk about accurately assessing risk. Please tell us about that.
Tony
Risk is generally the probability of an unexpected outcome. When we work with customer data, part of what we’re trying to help them understand is the likelihood of a negative outcome for their business. One example, at the end of 2021, we have a customer that’s a mining company, and we were helping them understand what their calendar year 22 would look like. And we said, Hey, you need to be really careful because you’re likely to see a 30 % decline in revenues in Q2 of 22. And they said, No, we’re just coming off of a record year. It’s going to be fine. You guys don’t know what you’re talking about. And that’s fine. We see that on a regular basis where people doubt our outcome, and that’s okay. And so we kept working with them. And lo and behold, end of Q2, their revenue declined by 40 %. So we flagged that six months ahead of time for them. And that was a very transparent risk from our part, given what we were seeing in their market. And not what we as people were seeing, it’s what our machines were seeing in their market. And so that same customer in September received an acquisition offer, and they initially rebuffed it and said, No, not enough money.
Tony
We’ve got a growing business. It’s not going to work for us. As we were working with them and reiterating their forecast each month, we said, Hey guys, I’m not sure if you have seen this, but we expect 2023 to be a worse year for you than 2022 was. And they took it to their executive committee and their board, and then they accepted a second offer from that potential buyer because they realized that we were really accurate in terms of the risk associated with their business. And they were worried that 23 was going to be worse than 22. So they took that buyout offer based on partly, not fully, but partly based on things that we were telling them. So it’s possible to identify those risks. Although we didn’t predict a 40 % decline, we predicted a large magnitude of decline for their business, and they ignored it. And businesses can choose to ignore risks. That they do it every day, right? Some they escalate and they wrap into their plans. Some they ignore. And so this one they ignored and it bit them. And then ultimately they believed what we had said and they sold.
Tony
So that’s just one example of how people can identify risks with our process. We do cost, we do volumes, we do revenues and transactions, all that stuff. So there have been times where we worked with a chemical company, and through our work, we were forecasting the unit price for them. And we had discovered that they were undercharging by probably 80 % for the unit price of their good. They ended up raising their price by 50 %, and none of their clients complained at all. Their clients knew they were getting a heck of a deal. And so they raised their prices by 50 %, and the market completely absorbed it. This was three years ago. So this was before all of the inflation right now. And that company, partly because they successfully raised their company by 50 %, they sold as well. They were a publicly traded company as well, and they sold as well. Again, partly because of the things that we spot in their market, some of its risk and some of it’s opportunity.
Rosanna
Wow. Excellent examples there. Risk management is always number one, whether it’s in business, investing, in life. We’re always calculating our risks, and we’re trying to make the smart decisions based on properly calculating those risks. And we have that issue I mentioned earlier was there’s a disparity between the perceived risk and the actual risk. And we try to neutralize that and try to bring those together. So we need to properly calibrate. And has to do with framing as well. When we come from a position of fear or losses, we tend to take more risk. We’re risk seeking. While if we come from a position of strength, we tend to be more risk averse. So in order to make better, smarter decisions, it’s so important to have that proper calibration of risk. And so you pointed out some very important points there. We want to reduce that margin of error, that standard deviation, and just to I love how you explained risk. It’s about the other things that can happen. So what are some factors that you use to identify risk? Let’s say in that example you gave us, what are some signals that you receive that identify higher risk for that company?
Tony
Yeah, it’s interesting. Part of it is the volatility within the data itself. So how volatile are those numbers and how do they react with other factors within their market or even outside of their market? And then we look at the cyclical nature of those numbers. How do they act in the short term? How do they act in the long term? So again, we’re looking at, say, univariate activities, meaning the data on its own. We’re looking at multivariate activities, meaning how does it interact with other data? Okay. And then we’re looking at other types of long and short term overlaps and other things in that process. So there are multiple layers of, and I keep saying that word layers, but there are multiple phases and layers within what we’re looking at to understand how data should behave. And another example, one of our customers at the end of 2019, we were looking at some of their costs from goods that they bought in Asia. And at the end of December 2019, we told them that their cost for a certain good would rise by five times by May of 2019. Sorry, of 2020. By May of 2020, the price of those goods would rise by five times.
Tony
They saw it. They thought it was crazy. They ignored it. By April or May, I can’t remember which month, the price of those goods rose by seven times. That’s a very strange multiple rise for any number. But with the process that we have, we looked that over through numerous lenses to understand whether that was accurate or not. They chose not to prioritize that risk, and that’s fine. Again, back this up with a lot of different statistics and processes and that thing. We can say, Look, this is the likely outcome, and here’s the probability of it happening, that thing. When you’re predicting risk, you have to identify a time frame. You can’t just say, Hey, that price is going to go up at some point. You have to say, That price is going to go up within this time frame. It has to be something that’s actionable for the customer. And so I see a lot of people on social media and in, say, industry analysis and geopolitical analysis who say X is going to happen. Okay, fine. X is going to happen. But when is it going to happen? Next month or 10 years from now?
Tony
And there are so many people out there who will say X is going to happen without a time frame on it. But when we go to customers, we have a very precise time frame when we believe things are going to happen. And again, they can choose not to accept that, and that’s fine. They know their business better than we do. But they should at least be prepared when we warn them that something is going to happen. They may think it only is going to happen with, say, a 20 % likelihood, and that’s fine. But at least they should have a contingency plan in place if that’s going to happen.
Rosanna
Absolutely. It’s always having that backup plan. And I love your transparency and how you share that data with them. And they should listen more. But I guess everyone thinks they know their company best. And it’s always about risk adjusted returns. And I think as novice traders and investors, like 2020 brought a lot of new investors and they seem to not account for that. But I always tell people it’s always risk management first and knowing your risk. So we have to properly assess that risk. And it’s just amazing how you’re able to forecast these expenses. And like you said, time frame, time frame is key. Absolutely. It’s always about time frame and risk adjusted returns. So I think we can talk about another favorite topic here, economics, macro economics. And we talk about there’s so much change, and there’s so much change in the economic world globally, domest. And I sometimes think anything is possible. And we say inflation, with core inflation seems to be entrenched. It’s very sticky. It’s hanging around the 4 % to 5 % range. I haven’t looked recently, but it’s probably always changing. But it seems to be stuck in that range.
Rosanna
We know people are talking about stagflation. Then others are saying deflation. And there’s a credit crunch. We know that there’s a lower supply and demand of credit on both ends there. Then recession. And the recession is coming into 2024. What are your thoughts? I know that’s a very wide spectrum there. Tony, what are your thoughts going forward?
Tony
Yeah, it’s out of there. So we’re at a place right now where I could probably put together a plausible scenario for any one of those things happening. Because we’re in a place in economics and markets where really anything can happen. And the reason I say that is, through COVID and after COVID, we had so many stimulative government programs underway that I don’t think we’ve ever seen this magnitude of stimulus in markets. And I don’t think we’ve ever seen it withdrawn this quickly either. And by withdrawn, sorry, I don’t mean withdrawn, I mean halted. So of course, it’s still in the market, right? But the benefits of that stimulus have already largely been seen. So what are we going to see over the next, I think you said, in 2024? Well, we’re likely to see the Fed continue to raise rates at least a couple more times. That would put us around 6 % for a Fed funds rate, which would be pretty high given where we’ve been for the past, say, 15, 20 years. The cost of credit, you mentioned credit crunch, what we saw with regional banks, Silicon Valley Bank and the other banks, and what is happening with the credit crunch will impact small and medium sized businesses more than it will impact large businesses.
Tony
So that is a real danger for things like job creation, for company creation, for other factors that we just take for granted. So high interest rates, coupled with the credit crunch, I think we will not necessarily see the results of that for probably four or five months, something like that. But ultimately, it will have an impact on the US economy and other economies, of course. If credit is not available in the US, then transactions by US companies are not possible internationally, especially small and midsize companies. So as we see those regional banks, not all of them, of course, but some of them seize up and get more conservative about their loans, it will impact a large part of the US economy. And I find that really worrying. I do know that we are headed into an election year, so we will likely see immense pressure on the Fed to loosen monetary policy going into an election year. So we may see the Fed raise one or two more times, but I think the pressure on the Fed to loosen going into ’24 will be immense. And again, all that really does is prolong, say, some really bad habits we’ve had in place for about 15 years since the global financial crisis.
Tony
So we could see a sharp reaction in Q3 or Q4 of this year, a negative reaction. But I would say going into Q1 or Q2 of next year, we’ll see a huge pressure for the Fed to accommodate because of the election, both presidential and legislative and governor elections. And so I would say the Fed would probably be neutral from, say, June onward. They’re probably not going to talk Hawkish or Dovish. They’re probably going to try to make as many changes as they can before the election. All the election rumblies really start to hit. And then from, say, Q2 onward next year, they want to probably be a benign force unless something dramatic is happening in the economy. Well said.
Rosanna
I think we’re on the same page here. I love the way you outlined everything, Tony. I think you covered it all. We had the fastest money printing, very fast during COVID, this mass stimulus. And then at the same time now, we have the fastest shrinking of the money supply since I think the 1930s. So it’s like we had a whiplash. And then we have higher for longer, higher rates. I think it’s like the fastest in a very long time. How about that one? And maybe since the… I don’t know. Whatever the fastest raising in rates, we went from like… I think we went up 5 %, 50 %. I mean, insane. 500 basis points in what, a year or so? And it’s just crazy. And then you have the unrealized losses for banks and you have that banking crisis, which I don’t think it’s over yet. It’s about liquidity issues. We don’t have really the credit issues of 2008, but it could lead to that. Commercial real estate is a big challenge. The shorter term loans, we have vacancies, offices are way vacant because of COVID. It’s exponentially sped up that process. And I always talk about even if you’re locked in at low rates, if you don’t have that cash flow to meet that debt service, how are you going to hold on to those properties?
Rosanna
Then you have Airbnb homes that are becoming vacant now. How long can they sustain those homes until they start flooding the market as well? So even though people are locked in and they say the Fed trapped people in their homes, we have lower inventory, but that can change quickly. Data changes quickly.
Tony
Things kick in. It will refinance for cash flow issues, I think. So that 3, 4 % loan they have, that’s great as long as everything is stable. But I think for cash flow issues, people will refinance and they’ll have to refinance into higher rates. So whether they want to or not, I think that will become fairly common over the next, say, 2, 3, 4 years. The thing about commercial real estate that you mentioned that’s very important is, a lot of pension funds have a lot of commercial real estate holdings. So as we see commercial real estate funds and companies mark to market, you will see loss in pension funds in a way that we haven’t seen for a long time. The question is, what will happen there? What will that really impact people’s retirement? Or will the government just cover it? I think that’s, do we socialize that risk outcome? I hope we don’t because it’s the pension fund managers fault that they made that investment and didn’t sell earlier. The other part about commercial real estate is a lot of the risk with commercial real estate loans is held with regional banks. So not only did we see the issues that we saw in March with regional banks, we’re likely to see more regional bank issues associated with commercial real estate loans marking to market.
Tony
At the same time, we have a lot of commercial loans, not commercial real estate, but corporate loans that come due next year. I can’t remember the number. It’s trillions of dollars. They will reprice in new interest rates. If they don’t pay them off in the lower interest rates, then the carrying costs of those loans for companies rises dramatically. There are so many factors associated with higher interest rates. Oh, yes. T’s good to have the use of money cost money. That’s what interest rates are. So if using money is free or if interest rates are on a real basis negative, that’s really problematic. And we’ve seen that through economic history everywhere, largely everywhere where it happens, it inevitably becomes a problem. There have been some moments where it wasn’t, but they were brief. So having real interest rates be positive is a good thing for the economy. It will help raise savings rates because people will be incentivized to save rather than speculate. And so all of that is good. And the normalization of the economy with higher rates is a good thing in the long term. It’s just likely to be pretty painful in the short term.
Rosanna
Absolutely. It’s the digestion of all that. It’s going to be very painful and it will take time to work through. The Zerp, in my opinion, was not a good idea. We’re in the banking crisis is a symptom of all the negative rates and all that free money that was given out. Now, excellent point about pension funds. You don’t know what’s going to happen there, but it just shows the vast, widespread things that can occur. It’s not just centralized with commercial real estate. So people say, Oh, I’m not a commercial real estate investor, so it’s not a big deal. But it is a big deal because it’s widespread across many different funds and many different industries. Regional banks, excellent point. I think that the process continues with less banks, more branches. Smaller banks, they’re more sensitive, they have liquidity issues, and these commercial real estate loans are centered around these regional banks. I remember when I was in commercial real estate, I was a broker and owner of a firm. I used to always go to the regional banks for my customers loans. It was always the local banks. It wasn’t a Bank of America or Chase.
Rosanna
And so, yes, it’s always the regional banks that have those loans. So that’s another concern. And you also mentioned excellent point. And I talk about this often, small businesses are more sensitive to all these margins being compressed, higher cost of debt and capital, and it takes time to work through the system, and we’re getting new refis coming through. So I think these higher rates are probably going to last much longer. I’m not certain we’re going to go much higher, but I think we’re going to last longer. And like you said, I think it’s healthier. I mean, we want to have some return on savings. It’s important that we have some type of return. At that point where it was just growth, we had to keep investing the money in order to make some return. Now it’s a different mindset. So I hope people don’t fall for the recency bias. These are different times. I think this is a new regime that we’re in. We’re actually just returning back to a different time period because we live in cycles and these cycles repeat and it’s human emotion based. So I think we are in a different time than we’ve been the past 15 years.
Rosanna
And so we need to think differently. We also have mass government spending. It’s still continuing. So this contraction that we’re experiencing is really going to be felt mostly on the private sector and it’s going to be painful. So it’s going to be challenging times. And inflation works both ways. We had margins compressed. We had higher cost of goods sold, higher operating expenses, new fees. We have new fees here in New York, the IIS surcharge, and that’s something in the employment fee that we had. But the top number for corporate profitability was held up. The revenues were held up by inflation, by higher prices. But if we get a deflationary plan, like it appears we’re going to have prices coming down, then… And plus, if we go into this recession that everyone talks about because of this fast reduction in the money supply and 85 % of the money supply comes from commercial banks and there’s a credit crunch. So we get that that top number is going to come down as the demand rolls in and then we’re going to have serious problems. And it could be a severe recession because we have markets is still compressed and then we have higher cost of debt and capital.
Rosanna
And as you said, it’s going to work through the system. It’s going to take time. Top number coming down. So there’s a lot of scenarios that can play out here in the next year or two. So there’s a lot of issues and I’m by no means an expert in all this monetary system and economics. But from what I’m seeing as a business owner, it’s very challenging and it’s very challenging challenging for the small business, and we’re small business owners. So we’re noticing that, and it’s not letting up, and the demand has come down, and it seems that people are looking for better pricing. And it’s a different dynamic now. And we still have higher pricing with the parts and supplies, and we have inverted yield curves, and so we need to prepare accordingly. Regarding the markets, and I think we can go right into the markets. How are you preparing? What are you seeing with your CI Markets and going forward? Apparently, there’s a lot of scenarios which means there’s potential volatility coming up, a lot of different things coming up. How are you planning and what are you seeing with your CI Markets?
Tony
I think at least for the next couple of months, we’ll continue to see equity markets grind higher. It’s really hard to see the incremental benefit that investors will get given the risks with markets at this level and where interest rates are going. But we expect markets to continue to grind higher despite what many people are thinking about markets. Commodity prices, we expect to continue to decline. Generally, crude prices, there’s a is a belief that I see in markets where crude prices are bound to hit $90 any day. We’re just not necessarily seeing that. We don’t see a dramatic fall in crude prices. We see an ongoing, lumbering slow fall in crude prices for the next few months. The reason crude is so important is because there are so many secondary tertiary impacts of crude price that the crude price itself is very, very important. Again, we continue to see equity prices grind higher despite fears about things, a big rug pull. We don’t see them grinding a lot higher. We see them grinding marginally higher for the next couple of months. And then commodities we see falling. So a lot of the goods inflation that we saw in ’21 and early ’22, that’s largely played out.
Tony
Now, what we’re waiting to see fall is services prices because services prices track with wages and wages are up pretty dramatically. And so the really interesting part, I was looking at a chart earlier today and I tweeted this out. Actually, there’s a guy named Bob Elliott who initially tweeted it out and I recent his. Bob’s pretty amazing, smart guy. As wages have continued to rise, productivity has fallen dramatically, one, two % per year. So Americans now are actually contributing a lot less to make a lot more. And so as wages continue to rise, that hits services prices in a big way. So when you call that service person out to your house, or when you have some service done, those prices just are absolutely not going down, at least for now. And the productivity of those workers is actually declining. So why are the employment numbers so strong? Well, companies have to actually hire more people to get the same work done.
Rosanna
They.
Tony
Have to hire more people at higher prices to get the same work done. That’s the environment we’re in right now. And it’s a really strange environment. So until we start to see productivity kick up, we’re going to be in this cycle where the employment numbers themselves look good and wages continue to rise because nominal prices are continuing to rise, but productivity will continue to crater because we’re workers just are not incentivized to really do all that much or work all that hard. Their workers are incentivized to do just what they have to. What we have seen with the, I guess, permancy or semi permanently of work from home is, a lot of people are taking on two jobs. So they’ll have an official job with a company, and then they’ll have an unofficial job doing something else. And this is very, very widespread. And so that primary job that they have where, let’s say, they get their health benefits, they’re not necessarily putting in 100 %, which is what they would have done, say, four or five years ago before COVID. Now they’ve got their side hustle, which is not an insignificant amount of their time, where they’re doing their salary job at an inflated wage, and then they’re doing their side job, which is still pretty lucrative.
Tony
So this is a problem that we’re seeing that spurred from work from home where companies can’t really observe people because they’re not in the office and they feel awkward about observing them at home. And so workers, because of the opportunity, they’ve taken on more than one job.
Rosanna
I love this discussion. You expose the reality and the truth of what’s going on. And I’ve been talking about this for a while. There’s just a declining productivity across the board. There’s a mismatch between the employer and the employee and the expectations on both sides. And we’ve seen that for a while. Covid really changed the worker and their expectations. And I’m in the camp that because I have stay at home workers. I still have them. They don’t want to come back to the office. One of them is actually moved away from here. And I just have seen over time as they stay away from the office, their productivity declines. They’re out of touch and they’re just not producing the same. We’ve had to limit some of their hours. I hope they’re not watching right now, but it’s okay. They know. It’s just changed. And so our expectations have changed too. And we had to pay them higher. There were higher wages. And now, as prices come down, we want to lower the prices. But how do you lower their wages? It’s a very challenging dynamic. And I call it a dilution of value. And we just have less value across the board.
Rosanna
For our inputs, we’re getting less output. And it’s very concerning. And I often say, Is AI the solution? Is AI the solution? Is automation what’s going to save us at this point? And you mentioned Bob Elliott, and he was on the show recently. He actually was on twice. Excellent. I love his input. He’s a great guy. Excellent guy, so smart. And he said that we are in income driven growth cycle. And I agree completely. Services are a function of labor and employment. And as we have this wage growth and occurring, it keeps fueling that demand. And it’s not necessarily a spiral, a price spiral. It’s more of it maintains it. And so we have these services inflation maintained because of these wages. And people have multiple jobs now. And it’s just like I said, a dilution of value and something needs to change. And I’m hoping that automation is the answer. And I want to ask about blockchain. I want to talk about different asset classes and Bitcoin and crypto. What are your thoughts about that being a global reserve asset and helping with automation with the blockchain?
Tony
Yeah, it’s really hard for me to take, and I mean, knock on anybody, but because crypto specifically has been such a speculative asset, it’s really hard for me to take it seriously. Some of that is price discovery, which is normal, but a lot of that is opportunism. I don’t think we’re at a point with crypto yet to where it’s a currency. Crypto is an asset. It’s a speculative asset. So when we get to the point where the transaction costs are low enough to where we can use it every day, then it becomes a currency. But if people want to use an alternate currency, there are 80 other currencies in the world that people can use. You don’t have to make one up. So until we really start to see the transaction costs of that currency decline, then we won’t see it used as a currency, a analogy I use is the Euro area. The Euro was created because every country in Europe had its own currency, and it was created largely, at least at the time they said, because of the transaction costs of doing cross border commercial transactions within the Euro zone. It added, I don’t know, 5 % to the cost or something like that, of doing, say, a transaction between Germany and France, something like that.
Tony
So they created a single currency zone to reduce those transaction costs. And was it worthwhile? Probably. It seems like at least there were short term gains based on reducing those transaction costs. And it’s a long discussion as to whether or not the Euro is well governed. But with crypto itself, we have so many different cryptocurrencies out there. And I’d say currency is not necessarily seriously, but the transaction costs of those currencies are very high. So it’s really hard for me to take them seriously as a currency. It’s easy to see anything as an asset. Anything that can be traded is an asset. So do people see crypto as a store of value? Yeah, sure. That’s fine. So is Beanie Baby or whatever, right? And so people will put money there in hopes that it appreciates. And so that’s an asset. So I don’t know that we’re at a point where crypto is really all that usable. And I know people are probably going to hate me for saying that, but I just don’t think we’re there yet. We may be in five or 10 years. We may never be there. I don’t know. But I can’t really take that seriously until we have lower transaction costs and more predictability around what that current currency is valued at.
Tony
So if you look at currencies like the Turkish Lira, there’s almost no predictability around what the Turkish Lira is going to do. So who transacts in the Turkish Lira? Nobody who doesn’t have to. It’s just not a good store of value. And so I think looking at crypto, unless you want to go on a ride, a hugely volatile ride, it’s probably, at least from my perspective, it’s not something I would put money in. I did put money in Dogecoin. I made, I don’t know, 15 times my money in Dogecoin. It wasn’t a lot of money, but I got in and then I got out. And I think I still have $20 worth of Dogecoin or something like that. But I just wanted to see what that was all about. And I got in and I got out and I haven’t acquired it anymore and I have just a minimis amount in there just so that it makes me pay attention to where that price is. And what is a blockchain? It’s a register of stuff. I’m trying to see, and I don’t mean this to sound cynical, but I’m trying to see value in the blockchain.
Tony
Let’s say I buy a car and that car is governed on the blockchain, I really don’t care who owned that car two times before me. All I care about is does the car work. I can get information about that car, but who owned it doesn’t matter to me. What they did with it, as long as they didn’t damage it, I don’t care. And so I’m having a lot of trouble understanding value on the blockchain, but I’m sure there are plenty of people who have concerns. And I’ll be honest, Rosanna, I really don’t want to hear them. I’ve had a lot of people tell me about them over the years. And it’s interesting and novel, but I’m not sure it’s all that valuable right now.
Rosanna
I love your valuable input, Tony. That thank you so much for sharing that. It provides a great perspective. I love that. Do we really need all this information? It’s like they’re selling you the point that you can have all this info, you can know where the person lived, where the car was stored, and how it was stored. At some point, it’s information overload. And do I really need all that information? I think we’re going to have to use the complete intelligence system and decipher which is needed to help us solve that problem. Because I think that… I don’t need to know if the car was previously stored and who owned it and where it was and everything, but excellent points there. And you talk about the currency and the US dollar is the global reserve currency. And I don’t think it’s going to be dethroned anytime soon. That’s my opinion for stability and liquidity, I mean, the only contenders are the Yuan and the euro. And I don’t think that they’re presenting much competition for the US dollar. What are your thoughts on all that?
Tony
China can’t let their currency float. Anybody who understands the PBOC, the Central Bank in China, the People’s Bank of China, understands that it’s questionable at best the way they govern monetary policy in China. Monetary policy, occasionally in China, is still based on numerology. And so whenever I hear people say, oh, the Chinese CNY is definitely the next global currency, tells me they really don’t understand how the PBOC operates. The Euro, because they have centralized monetary policy, but they have decentralized fiscal policy, there’s too much of a risk with the Euro because every country decides their own fiscal policy, but monetary policy is decided centrally. It just is not workable for a global currency. So the people who transact in euros are either transacting with Europe or say European either actual or proxy colonies, or they’re just looking for an alternative currency besides, say, US dollar, Swiss franc, Japanese yen, or something like that. It’s not to say that there are not a lot of transactions done in euros. It’s a very large economy. But that divergence between monetary policy and fiscal policy is a risk for people who hold it.
Rosanna
Exactly right. There’s a lot of noise out there and a lot of different stories and people come up with their own theories. But you actually provide data driven opinions here. And I love that point that you made. I agree with you completely. I think that for the same scenarios that you mentioned, I think the US dollar remains king and I think it shall until further notice. I want to know your thoughts about different asset classes. When we talked about commodities, you touched upon the oil, crude, and all that. What are your thoughts on the metals, gold and silver, especially if we go into a stagflationary environment? Are you bullish on all those going into the end of this year, into next?
Tony
Not necessarily because when we look at other currencies, it’s all relative. So we could see, say, money printing in more supply of dollars, for example. But it doesn’t necessarily mean that there’s a perfect inverse correlation between the dollar and the gold price. So I would be really careful there. I know there are a lot of people who really want gold to rally, but we’re not not necessarily bullish on gold right now. Again, we’re very much in a short termist market. I would look at markets, say, three months at a time because Fed activity, US treasury activity, ECB activity, PBOC, BOJ activity, any of these could change the economic output globally at any time. So I I would be really careful with precious metals. Now, industrial metals, if we are really going to have fully electric vehicles by, say, 2035, something like that, it doesn’t really matter what’s going to happen over the next 2-3 years. But those metals, whether it’s cobalt or copper or whatever, any of those, say, green metals, there’s going to be more demand for them over the next decade. I think if you are horizon for investing is very near term, really in just about any asset class, you’ve got to be ready for volatility.
Tony
But if it’s over the longer term, you know things like battery metals are going to likely appreciate over the long term.
Rosanna
Agree, exactly. I played that. Lithium before. And I’m looking at that for long term as well as these other metals that you talk about. And I think aluminum is another one that is used for EVs. There’s quite a bit, and there’s so much more than just Lithium. So excellent points there. I want to talk about emerging markets. There’s a lot of talk about India being the next super growth story. And then we have Vietnam, Southeast Asia, and then they’re always talking about South America. What are your thoughts on diversifying with emerging markets?
Tony
The base of the enthusiasm about emerging markets generally is around developed economies diversifying out of China, I think. And so we really have to look at what can play a substitutional role to China’s supply chain. And a place like Vietnam, no brainer, easy, and it’s growing by leaps and bounds. Malaysia, Thailand, same. It’s slower growth, but still reliable growth in those places as they take off that substitutional manufacturing from China. India, we’ve seen some announcements in India. I love India. I’ve been going there for 20 plus years. I’m not convinced that India has the supply chain infrastructure in place yet to make it a reliable supply chain source. So I need to observe some successful transitions of manufacturing to India, and I need to see that again and again and again before I see India as a reliable location for global manufacturing. I want India to succeed. I think they have the workforce to succeed. I just worry about the physical infrastructure in India being able to take a large amount of, say, global manufacturing. So we have to observe India for the next couple of years to see if they can take some of that on and really see what’s happening.
Tony
Places like Indonesia who are very interesting and most of the people I talk to who are manufacturing in Indonesia are very happy. I think that’s one place that isn’t talked about much in this special in the US, but I think Indonesia has as much opportunity to take on manufacturing as India. If you look at the Americas, Mexico, I know that in terms of the automotive and electronics manufacturing supply chain, Mexico is taking a much larger share of that from China. It’s quietly doing that, especially because automotive and electronics, they don’t necessarily want to upset the supply chain they have in China right now. So they’re building that stuff in parallel, over time, ready to off take manufacturing from their Chinese locations to Mexico, at least for their North American markets. Mexico, very interesting. Brazil, of course, Brazil has been in manufacturing for a long time. And of course, say, ag goods and other things, raw materials, metals. Brazil, I think Brazil presents political risks and potentially some trade risks, depending on what they do around nationalization and, say, documentary requirements into and out of Brazil. But I think certainly they have the capacity and the know-how.
Tony
I think Brazil’s worst enemy is Brazil. So if they can get out of their own way, they can succeed in a big way.
Rosanna
Excellent points. Thank you so much. I like that Brazil’s worst enemy is themselves. I think I say that about myself sometimes, right? All of us can be our own worst enemy. When we see the risk free rate over 5 %, I always talk about T bills being a great return, unlike what we’ve had the past 15 years. What are your thoughts on that with the market? I know you said we’re going to have this run. Maybe it’s driven by the AI euphoria or whatever it is going on, but people seem to be overlooking that fear has subsided a little bit. But we have T bills, over 5 %. Why is it in their best interest to go in the markets with and take and invest in riskier assets when you have this return of a 5 %?
Tony
Honestly, Rosanna, I’m not sure. I think people have been so conditioned, especially over the last few years, to yolo and try to figure out how they can make 15 % in a day or something like that. And so I think that’s a great question and I really don’t know the answer to it. I think a short answer is investors may not necessarily be rational. That incremental investor may not be rational. So they’re going to put it in some tech stock or something instead of into treasuries because treasuries are boring. But a 5 % return, it’s not boring. It’s actually really interesting. So I think that’s a great question. And I think that’s one that people are going to look back on and go, I don’t understand why I didn’t do that.
Rosanna
I love your answer because I feel the same way. I think Warren Buffett is the one who said investing is supposed to be boring. And I think many people have said that. And so T bills seem to fit the bill. But it’s always about risk adjusted returns. And when I see a risk free rate over 5 %, to me, it just makes sense. But I think it’s a recency bias. People are like, Oh, I got to put my money in the market. I think they’re just conditioned.
Tony
Yeah. Well, if you look at the first half of the year, the returns on equities have been very good in the first half of this year. So there is a reason they haven’t transitioned, or many people haven’t transitioned to say treasuries yet. My real question is, what’s the return in the second half of the year? And will you be able to do better than treasuries in the second half of the year? That’s a real question. And again, I’m not necessarily seeing the incremental benefit of putting that extra dollar into the markets versus the risk of potential downsides given where we’ve come in the first half of this year.
Rosanna
Excellent points. Thank you so much for that. So I think you like coffee, right? Is that something you like? Tell us about your coffee passion.
Tony
I’ve liked coffee since I was about 4 feet tall, since I was really young. I had actually a Twitter follower at the end of 2020 sent me a direct message. I’d never met this guy. He said, Hey, what’s your address? I thought that was a little bit weird. I thought, I’m not sure. Why do you want to know my address? He said, I want to send you something. And so I thought, Okay, that’s weird. I asked him a few questions. Ultimately, I said, Sure, here’s my address. I decided to just take a risk. So he sent me an old 1980s style air popper with some small bags of green coffee. It was a very generous thing for him to do. And so I started posting coffee in December of 2020 with this dumb air popper on my back porch. And I quickly realized that I loved to roast my own coffee. And in August of 2020 or 2021, I was watching college football one Saturday and decided, I’m going to do it. I’m going to just… If I don’t have people pay me for what I’m doing, I’m just not going to improve my capability.
Tony
So I built a website over about two days and put a note out to my Twitter followers and said, Hey, I’m launching a coffee brand. If I can get 20 people to subscribe, I’ll do it. Within 48 hours, I had my 20 subscribers and I’ve been there ever since. So the name of the company is nerd roaster. I really do own the nerd.
Rosanna
You do. You’re the nerd.
Tony
So it’s nerd roaster. If you don’t mind, you can find it at nerdroaster. Co. And we do a monthly subscription model. I source beans from different individual farms every month and roast them. And I send my subscribers a note saying, here’s the farm that you got it from, here’s the process it went through, here’s how we roasted it, here are the tasting notes you should have. And so my subscribers really like that. Again, they’re nerdy about coffee and we’ll start at different… We’ll look at different locations. A few months ago, I did an African roast. Last month, I did a Hond roast. This month, we do a Mexican Chiapas roast. And so each month, I change locations and roast it and tell my customers what it should taste like. I don’t understand why coffee isn’t more appreciated like wine. You get your mass box wine drinkers in the same way you get your mass coffee drinkers. But there are so many people who drink wine and just really love the taste. With coffee, that’s how I appreciate coffee, and that’s how I taste coffee. I really want people to come to a point where they appreciate the origin of the coffee, how the coffee is roasted, what the tasting notes are, even things like how do you grind your coffee before you brew it?
Tony
How do you brew your coffee? So I talk with my subscribers about those sorts of things so that they can have the best experience with their coffee.
Rosanna
I love that. I mean, I love how you’re using that model of the wine for coffee because I love coffee. And my family is from Napa Lee and Naples, Italy is a big place for coffee, espresso. And you can go to the beach over there and they have the finest cup of espresso. And you get it right from a little h ut over there in the beach. And it’s like in a beautiful always give it to you in a beautiful porcelain little cup and you drink it. So it’s been part of my family and growing up. And my husband and I every morning have our espresso. And so we feel the same way as you that there’s so many different… It’s an experience and there’s different notes to it. And I love that you break it down that way. How do you drink your coffee? Do you vary it?
Tony
I vary it. I’ll have it black or I’ll have it with some heavy cream, just a little bit of heavy cream. But I don’t put any sugar in my coffee that destroys the flavor too much. But if people want to do that, that’s fine. But I don’t really do milk or half and a half. If I’m going to put some liquid in it, it’s going to be the good stuff. I’m going to put heavy cream in it.
Rosanna
I love that.
Tony
But whenever I do a new roast, the first time I drink it, I’ll drink it black because I want to make sure I fully understand the body and what those tasting notes are and how drinkable it is, how acidic it is, all that stuff. So it’s really, really important for me to experience the coffee that way. And then occasionally, I’ll throw some cream in there, depending on how I feel.
Rosanna
I love that. When you sample it and you first drink it, do you drink it with the four ounces or eight ounces, or do you drink it in a little shot like an espresso?
Tony
No, I drink it as a cup of coffee. I don’t drink it as a little shot because I want to enjoy it. It’s like holding a wine glass and seeing it sticks to the side and.
Rosanna
Smelling it. Oh, my gosh.
Tony
This is amazing.
Rosanna
Oh, my God. You’re a Renaissance man. You’re the nerd who roasts coffee. And I’m very passionate about coffee as well. I like that crema when I have my espresso and I never put sugar. And sometimes to make a cappuccino, I don’t know if you’ve heard of this, my husband, we travel and we went to Spain and they drink a tiger nut milk. It’s actually a plant based milk. And so we soak them. My husband actually does that. He soaks it and so he makes a plant based… It’s like a milk substitute, but it has a nice flavor. And so we put that in making a cappuccino with that. But yeah, so it just varies it a little bit. But I’m with you on that heavy cream. I’m licking my chops just thinking about that. I’m trying to limit it, though, by having the dairy, but it’s that’s so good. So yeah, so definitely going to put that in the notes to come visit you on your posting page. I think that’s awesome. I want to end with a very important note, social responsibility. And it’s about our causes and what we believe. And you truly strike me as a humanitarian.
Rosanna
I read on your LinkedIn, and I urge everyone to come to your LinkedIn, you were a foster parent and your causes are children, economic empowerment, education, human rights. Please tell us about all of that and what drives you.
Tony
When I was in Asia, I was asked to be on the board of a microfinance bank called CREDIT. Microfinancing can Cambodia. That brought me to Cambodia every three months to sit. The management team was fantastic. They knew how to run their business. But just to bring other ideas to them, to be a sounding board for them to help them understand things like, what does their leadership team look like? What’s the composition? What are some of their practices? How are they being fair to their borrowers? What security risks do they have? Those sorts of things. So even when we moved back to Texas, I was still going to Cambodia every three months to take part in those board meetings with credit. And so I through a lot of my career, not all of it, but through a lot of it, even back in the early 2000s, I was working in places like Sri Lanka. I think I told you when I went there to help set up a business while there was a civil war, we were there. We weren’t making huge money. We were there because it was very interesting. And we saw a market that really desperately needed some of the things we were building out.
Tony
And so you can find need everywhere. You can find disparity everywhere. I can find it in my town here in Houston. I can find it on the other side of the world. So I would encourage anybody who’s watching, you don’t have to take an exotic trip to do humanitarian work. You can find it in your neighborhood. You can find it in your town. So don’t look at people overseas as the only ones who are in need. You really have to look in your own backyard first. And if you can do it in your own backyard, then you can do it. Should be able to do it anywhere.
Rosanna
That’s so beautiful. It’s that you care and you’re so passionate and you provide a wealth of information. I think you should almost write an autobiography. You’ve experienced so much and you contribute and you add value. And this has been so amazing. I’m just wanting you to know, so many people told me, You got to meet Tony N ash. He’s amazing. Really? Yeah. Two people who recently been on the podcast but they’re like, Tony’s amazing. And I agree completely. I think you’re absolutely amazing, brilliant. And this has been amazing speaking with you. And thank you for sharing with all the listeners your brilliance. And I like to wrap up with you, please telling us about your website and where people can find you and visit you and read more about all the value you contribute.
Tony
Great. Thank you, Rosanna. And again, thank you for the opportunity. This has been fantastic. Our website for Complete Intelligence, our AI firm, is completeintel. Com. I can be found on Twitter @ Tony N ash nerd. The website for my coffee company Nerd Roaster is nerdroaster. Co. And if you can’t find me any of those places, just do a Google search and I’m sure you’ll find me.
Rosanna
Love that. It’s about doing the right thing. And it appears that you are. And we thank you so much for everything you do and for your coffee roasting. I’m going to have to try it out myself. So thank you so much, Tony.
Tony
Thank you.
Rosanna
Thank you for listening to the Roost Show podcast. Please visit RosannaPrestia. Com for more episodes. See you soon. All investment, real estate, financial, legal, and tax opinions expressed by Rosanna Prestia or on the Roast Show should not be relied upon as professional advice and are intended to be used for informational purposes only.