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The Unbeatable Artificial Stock Market

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MG: The Lead Lag Report joining us for the hour here is Tony Nash of Complete Intelligence has found a lot of people that I respect following. Tony, I saw a few people saying they were excited to hear what Tony has to say. So hopefully we’ll have a good conversation here.

Tony for those who aren’t familiar with your background talk about who you are how’d you get involved in the data side of markets and forecasting in general. And what you’re doing with Complete Intelligence.

TN: Sure, Michael. First of all, thanks for having me. I have followed you for probably 10 or 15 years.

MG: I am very sorry for that I am very very sorry for that.

TN: But yeah so, I got involved in data way back in the late 90s when I was in Silicon Valley and I built a couple of research firms focused on technology businesses. I then took about probably eight years to become an operator. I did a turnaround in Asia of a telecom firm. I built a firm in Sri Lanka during the Civil War and then I started down the research front again. I was the Global Head of Research for the Economist and I was the Asia Head of Consulting for a company called IHS Markit which is now owned by S&P and then after that I started Complete Intelligence.

So, you know my background is really all about data but it’s also all about understanding the operational context of that data. And I think it’s very hard for people to really understand what data means without understanding how people use it.

MG: Okay. So that’s maybe a good direction to start with that point about context with data because I think part of that context is understanding what domains data is more appropriate for forecasting and others. Right? So, I always made this argument that there are certain domains in particular when it comes to, I would argue investing that have sort of a chaotic system element to them. Right? Where small changes can have ripple effects. So, it’s hard to necessarily to sort of make a direct link between a strong set of variables and the actual outcome because there’s always a degree of randomness. Whereas, something that’s more scientific right that doesn’t have that kind of chaos theory element is it’s clearer.

So, talk about that point about context when it comes to looking at data. And again, the kind of domains where data is more appropriate to really have more conviction in than others.

TN: Yeah. Okay. So, that’s a great place to start. So, the first thing I would say is take every macro variable that you know of and throw it out the window. It’s all garbage data 100 of it. Okay? I would never trade based on macro data.

We’ve tested macro data over the years and it’s just garbage. It doesn’t matter the country. You know we hear people saying that China makes up their data. Well, that may be true you can kind of fill in the blank on almost any country because I don’t know how much you guys understand about macro data. But it is not market clearing data. Okay? Like an equity price or a commodity price.

Macroeconomic data is purely academic made-up data that is a proxy for activity. It’s a second or third derivative of actual activity by the time you see, say, a CPI print which is coming out tomorrow. Right? And it’s late and it’s really all not all that meaningful. So, I wouldn’t really make a trade or put a strategy together based on macro data even historical macro data. Every OECD country revises their data by what four times or something.

So, you see, a print for CPI data tomorrow that’s a preliminary print and that’s revised several times before it’s put on quote-unquote actual. And so, you know, you really can’t make decisions using macroeconomic data beyond a directional decision. Okay? So, if you follow me on Twitter, you see I’m very critical macro data all the time. I’m very sarcastic about it.

I think the more specific you can get… You know if you have to look at say national data or macroeconomic data, I would look at very low-level data the more specific you can get the better. Things like household surveys or you know communist and socialist countries. Chinese data at the very specific level can be very interesting. Okay? Government data the high-level data in every country I consider it garbage data in every country. So, you’re looking at very low-level very specific government or multilateral data, that’s interesting.

The closer you get to market clearing data the better because that’s a real price. Right? A real price history on stuff is better and company data is the best. And of course, company data is revised at times but that really helps you understand what’s happening at the kind of firm level. And what’s happening at the transaction level. So, you know, those are the kind of hierarchies of data that I would look at.

MG: So, okay this is a great. That’s a great point you mentioned that it’s you said very these variables is macro variables they’re proxies for activity. Right? They’re really more proxies for narratives. Right? Because and that’s where I think… You mentioned sarcasm almost 99 of my tweets at this point are sarcasm because when Rome is burning, what else I’m not going to do except joke about it. Right? Because I can’t change anything. Right?

So, and to that point I share a lot of that cynicism around data that people will often reference in the financial media that sounds really interesting, sounds like it’s predictive but when you actually test it to your point, you throw it out because it doesn’t work. Right? There’s no real predictive element to it.

So, we’ll get into some of the predictive stuff that you talk about but I want to hit a little bit on this market clearing phrase you kept on using. Explain what you mean by market clearing.

TN: Data is where there is a buyer and a seller.

MG: To actual prices of some asset class or something like that.

TN: Yep. That’s right.

MG: Okay. So, that makes sense. Okay. Now again I go back to the certain domains that data is more clear in terms of cause and effect and getting a sense of probabilities the challenge with markets. As we know is that the probabilities change second by second because not only does that mean meaningless data change second by second but the market clearing data changes second by second. Right? Going back to that point.

So, with what you do with Complete Intelligence, talk us through a little bit. What are some of the variables that you tend to find have some predictive power? And how do you think about confidence when it comes to any kind of decision made based on those variables?

TN: Sure. Okay. So, before I do that let me get into why I started Complete Intelligence because if none of you have started a firm before don’t do it. It’s really really hard so…

MG: From the people in the back because I got to tell you I’m an entrepreneur, I’m going through. And all you got is people on Twitter kicking you when you’re down when it’s the small sample anyway.

TN: Absolutely. So, I was where I had worked for two very large research firms The Economist and IHS Markit. And I saw that both of them claimed to have very detailed and intricate models. Okay? Of the global economy industries, whatever. Okay? For all of the interior models. And I have never spoken with a global research firm a data firm that is different from this. And if I’m wrong then somebody please correct me. But at the end of that whole model pipeline is somebody who says “no that’s a little bit too high” or “a little bit too low” and they change the number. Okay? To whatever they wanted it to be in the first place. So, and I tell you 100% of research firms out there with forecasts today have a manual process at the end of their quote-unquote model. A 100% of them. Again, if there’s somebody else that doesn’t do that, I am happy to be corrected. Okay? But I had done that for a decade and I felt like a hypocrite when I would talk to clients.

So, I started Complete Intelligence because I wanted to build a 100% machine driven forecasts across economics, across market, across equities, across commodities, across currencies. Okay? And we’ve done that. So, we have a multi-phase, multi-layer machine learning process that takes in billions of data items. We’re running trillions of calculations every week when we reforecast our data. Right? Now the interval of our forecast is monthly interval forecast. So, if people looking at daily prices that’s not what we’re doing now. Okay? We will be launching daily interval forecasts. I would say probably before the end of the year to be conservative but we’re doing monthly interval forecasts now.

Why is everything I’ve said is meaningless unless we measure our error. Okay? So, for every forecast that we do. And if you log into our website, you can see whether it’s the gold price, the S&P 500, USD, JPY, molybdenum or whatever. We track our error for every month, for everything that we do. Okay? So, if you want to understand your risk associated with using our data it’s there right in front of you with the error calculations. Okay? It’s only fair, If I’m gonna say sell you a forecast, you should be able to understand how wrong we’ve been in the past, before you use that as a decision-making input.

MG: Well, maybe just add some framework on that because I think that’s interesting. So, what you call error I call luck. Right? Because luck is both good or bad. I always make that point that with any equation any set of variables you’re going to have that error is the luck component that you can’t control. And that doesn’t necessarily mean that the equation is wrong. Right? It’s just means that for whatever reason that error in that moment in time was higher or lower than you might otherwise want. Okay?

TN: There is no such thing as zero error. And anybody who tells you that they have zero error is obviously they’re an economist and they don’t understand how markets work. So, there is always error in every calculation.

So, the reason we track error is because that serves as a feedback loop into our machine learning process. Okay? And we have feedback loops every week as we and what we’re doing right now is every Friday end of day. We will download global data process over the weekend have a new forecast on Monday morning. Okay? And so all of that error whether it’s near-term error, short-term error or say medium-term error, we feed that all back in to help correct and understand what’s going on within our process. And we have like I said, we have a multi-phase process in our machine learning platform. So, error is simply understanding the risk associated with using with using our platform.

MG: Right, which is basically how apt is a thing that you’re forecasting to that error which is again luck good or bad. I’m trying to put in sort of a qualitative framework also because I think… Yeah, there’s errors in life obviously, too. Right? And so, when they’re good or bad. But you know those elements.

TN: Right. But here’s what I would and I don’t know, I don’t want to dispute this too much but I think there is. So, you use the word luck and that’s fine but I think luck has a bit to do with the human element of a decision. Okay? We’re using math and code there’s zero human interaction with the data and with the process. And so, I wouldn’t necessarily call it luck. I mean, it literally is error like our algorithms got it wrong. So, if you want to call luck that’s absolutely fine but I would say luck is more of a human say an outcome associated with a human decision. More than something that’s machine driven that’s iterating. Again, we’re doing trillions of calculations every week to get our forecasts out there.

MG: Yeah, no that’s fair and maybe for the audience, Tony. Explain what machine learning is now.

TN: Sure.

MG: I once developed an app called “How Edition”. I was having dinner with the head developer once and he said he just came back from a conference about machine learning and he was just basically well, having drinks with me laughing and joking saying everybody use this term machine learning but it’s really just regression analysis. Right? So, talk about machine learning what is actual machine learning? How important is recent data to changes in the regression? Because I assume that’s part of the sort of dynamic nature of what you do just kind of riff on that for a bit.

TN: Okay. So, when I first started Complete Intelligence, I was really cynical about AI. And I spoke to somebody in Silicon Valley and asked the same question: what is AI? And this person said “Well AI is everything from a basic I say, quadratic equation upward.” I’m not necessarily sure that I agree that something that simple would be considered artificial intelligence. What we’re really doing with machine learning is there are really three basic phases. Okay? You have a preprocess which is looking at your data to understand things like anomalies, missing data, weird behavior, these sorts of things. Okay? So, that’s the first phase that we look at to be honest that’s the hardest one to get right. Okay?

A lot of people want to talk about the forecasting methodologies and the forecasting algorithms. That’s great and that’s the sexy part of ML. But really the conditioning and the pre-process is the is the hardest part and it’s the most necessary part. Okay? When we then go into the forecasting aspect of it, we’re using what’s called an ensemble approach. So, we have a number of algorithms that we use and let’s say they’re 15 algorithms. Okay? That we use we’re looking at a potential combinatorial approach of any individual or combination of those algorithms based on the time horizon that we’re forecasting. Okay?

So, we’re not saying a simple regression is the way to go we’re saying there may be a neural network approach, there may be a neural network approach in combination with some sort of arima approach. We’re saying something like that. Right? And so, we test all of those permutations for every historical period that we’re looking at.

So, I think traditionally when I look back at kind of quote-unquote building models in excel, we would build a formula and that formula was fairly static. Okay? And every time you did say a crude oil forecast you had this static formula that you set your data against and a number came out. We don’t have static formulas at all.

To forecast crude oil every single week we start at obviously understanding what we did in the past but also re-testing and re-weighting every single algorithmic approach that we have and then recombining them based upon the activity that happened on a daily basis in that previous week. And in the history. Okay?

So, that’s phase two the forecasting approach and then phase three is the post process. Right? And so, the post process is understanding the forecast output. Is it a flat line? Right? If it’s a flat line then there’s something wrong. Is it a straight line up? Then that there’s something you know… those are to use some extremes. Right? But you know we have to test the output to understand if it’s reasonable. Right? So, it’s really an automated gut check on the reasonableness of the outcome and then we’ll go back and correct outliers potentially reforecast and then we’ll publish. Okay?

So, there are really three phases to what we do and I would think three phases to most machine learning approaches. And so, when we talk about machine learning that’s really what we’re talking about is that that really generally three-phase process and then the feedback loop that always goes back into that.

MG: Yeah. No that makes sense. Let’s get…

TN: That’s really boring after a while.

MG: No, no, no but I think that’s it’s part of what I want to do with these spaces is try to get people to understand you know beyond sort of just the headline or the thing that is thrown out there. As a term to what does that actually mean in practice you don’t have to know it fully in depth the way the that you do. But I think having that context is important.

TN: I would say on the idea generation side and on the risk management side right now. Okay? Now the other thing that I didn’t cover is obviously we’re doing markets but we also do… we use our platform to automate the budgeting process within enterprises. Okay? So, we work with very large organizations and the budget process within these large organizations can take anywhere from say four to six months. And they take hundreds of people. And so, we take that down to really interacting with one person in that organization and we do it in say less than 24 hours. And we build them a continuous budget every month.

Once accounting close happens we get their new data and then we send them a new say 18-month forward-looking forecast for them. So, their FPA team doesn’t have to dig around and beg people for information and all that stuff. So, some of this is on the firm event could be on the firm evaluation side, as well. Right? How will the firm perform? Nobody’s using us for that but the firms themselves are using that to help them automate their budgeting process. So, some of that could be on this a filtering side and the idea generation side, as well.

So, we do not force our own GL structure onto the clients. We integrate directly with their SAP or Oracle or other ERP database. We take on their GL structure at whatever levels they want. We have found that there is very little deterioration from say, the second or third level GL to say the sixth or seventh level GL, in terms of the accuracy of our forecast. And when we started doing this it really surprised me. We do a say a team level forecast for 10, 12 billion organizations, six layers down within their GL. And we see very little deterioration when we go down six levels than when we do it at say two levels. Which is you know it really to me it speaks to the robustness of our process but would we consider Anaplan a competitor not really, they’re not necessarily doing the kind of a budget automation that we’re doing at least, that I’m aware of. I know that there are guys like Hyperion who do what we’re doing but again their sophistication isn’t necessarily. What we’re doing and they do a great job and Hyperion is a great organization. I think Oracle gave them a new name now but they’re not necessarily using the same machine learning approaches that we’re using. And our clients have told us that they don’t get the same result with using that type of say ERP originated or ERP add-on budgeting process.

Yep. So, I would say we can’t we can do company-specific information for a customer if that’s what they want. Okay? We don’t necessarily have that on our platform today aside from say individual ticker symbols. Okay? But we’re not forecasting say the P&L of Apple or something like that or the balance sheet of Apple. Something we could do in a pretty straightforward manner but we do that on a customer-by-customer basis.

So, what we’re forecasting right now are currency pairs, commodities about 120 commodities and global equity indices. Okay? We are Beta testing individual equity tickers and we probably won’t introduce those fully on the platform until we have our daily interval forecast ready to go to market. But those are still we’re still working some kinks out of those and we’ll have those ready probably within a few months.

MG: Okay. So, let’s talk about commodities here for a bit tonight. Obviously, this is where a lot of people’s attention has gone to. What kind of variables and I know you said you have a whole bunch of variables that are being incorporated here but are there certain variables in particular when it comes to oil and other commodities that have a higher predictive power than others.

TN: There are I think one of the stories that I tell pretty often and this really shocks people is when we look at things like gold. Okay? I’m not trying to deflect from your oral question but just to you know we’ve spoken with the number of sugar traders over the years. Okay? And so, we tell them that say the gold price and the sugar price there may not necessarily be a say short term say correlation there but there is a lot of predictive capability there and we talk them through why. And I think the thing that we get out of the machine learning approach and we cast a wide net. We’re not forcing correlations is that we’ll find some unexpected say drivers. Although drivers implies a causal nature and we’re not trying to imply causality anywhere. Okay?

We’re looking at kind of co-movement in markets over time and understanding how things work in a lead lag basis with some sort of indirect causality as well as say a T0 or current state movement. So, with crude oil you know there are so many supply side factors that are impacting that price right now, that I can’t necessarily point to say another commodity that is having an impact on that. It really is a lot of the supply side and sentimental factors that are impacting those prices right now.

MG: That makes a lot of sense. And I’m curious how did you mention it’s I think the intervals once a month. Right? So, given the speed with which inflation has moved and yields have moved how does a machine learning process adapt to sudden spikes or massive deltas in in variable movement. Right? Because there’s always a degree of randomness going back to error. Right? And you can make an argument that the larger move is the that may actually be more error but I think that’s an interesting discussion.

TN: So, I’ll tell you where we were say two years ago when 2020 hit versus today. Okay? So, in March of 2020, April 2020 everything fell apart. I don’t think there were any models that caught what was going to happen. It was an exogenous event that hit markets and it happened very quickly. So, in June, I was talking with someone who is with one of the largest software companies in the world and they said “Hey has your AI caught up to markets yet because ours is still lost” And you guys would be shocked if I told you who this was because you would expect them to know exactly what’s going to happen before it happened. Okay? I’ll be honest I think it was all of them but the reality is you know Michael you where you were saying that ML is just regression analysis.

I think a lot of the large firms that are doing time series forecasting really are looking at regression and derivatives of regression as kind of their only approaches because it works a lot of the time. Right? So, we had about a two-month delay at that point and part of it was because… So, by June we had caught up to the market. And we had started in February to iterate twice a month, we were doing once a month; I hope you guys can understand with machine learning two factors are we’re always adjusting our algorithms. Okay? We’re always incorporating new algorithms. We’re always you know making sure that we can keep up with markets because you cannot be static in machine learning. Okay? The other thing is we’re always adding capacity why? Because we have to iterate again and again and again to make sure that we understand the changes in markets. Okay?

So, at that time we were only iterating twice a month and so it took us a while to catch up. Guys like this major technology firm and other major technology firms they just couldn’t figure it out. And I suspect that some of them probably manually intervened to ensure that their models caught up with markets. I don’t want to accuse any individual company but that temptation is always there. Especially, for people who don’t report their error. The temptation is always there for people to manually intervene in their forecast process. Okay?

So, now, today if we look for example at how are we catching changes in markets. Okay? So, if I look at the S&P 500 for April for example, our error rate for the S&P 500 for April I think was 0.6 percent. Okay? Now in May it changed it deteriorated a little bit to I think four or six percent, I’m sorry I don’t remember the exact number offhand but it deteriorated. Right? But you know when there are dramatic changes because we’re iterating at least once a week, if not twice a week we’re catching those inflections much much faster. And what we’re having to do, and this is a function of the liquidity adjustments, is where in the past you could have a trend and adjust for that trend and account for that trend. We’re really having to our algorithms are having to select more methodologies with recency bias because we’re seeing kind of micro volatility in markets. And so again…

MG: So, kind of like the difference between a simple moving average versus like an exponential moving average. Right? Where you’re waiting the more recent data sooner.

TN: It could be. Yeah.

MG: Right.

TN: Yeah. That’s a very very simple approach but yeah it would be something like that, that’s right. Yeah. What so when we work with enterprise customers that level of engagement is very tight because when we’re getting kind of the full set of financial data from a client obviously, they’re very vested in that process. So, that’s different from say a small portfolio manager subscribing to RCF futures product where we’re doing forecasts and they have their own risk process in place. And they can do whatever they want with it. Right? But again, with our enterprise clients we are measuring our error so they can see the result of our continuous budgeting process. Okay?

So, if we’re doing let’s say, we launch with a customer in May, they close their mate books in June get them over to us redo our forecast and send it over to them and let them know what our error rate was in May. Okay? So, they can decide how we’re doing by department, by team, by product, by whatever based upon the error rates that we’re giving at every line item. Okay? So, they can select and we’re not doing kind of capital projects budgets we’re doing business as usual budgets so they can decide what they want to take and what they don’t want to take. It’s really up to them but we do talk through that with them and then over time they just start to understand how we work and take it on within their own internal process.

MG: So, back a little bit Tony. So, you mentioned you do this machine learning forecasting work when it comes to broad economics, markets and currency; of those three which has the most variability and randomness in other words which tends to have a higher error? Whenever you do any kind of machine learning to try to forecast what comes next?

TN: I would say it depends on the equity market but probably equity markets when there are exogenous shocks. So, our error for April of 2020 again, we don’t hide this from anybody it was not good but it wasn’t good for anybody. Right? And so, but in general it depends on the equity market but some of the emerging equity markets, EM equity markets are pretty volatile.

We do have some commodities like say rhodium for example. Okay? Pretty illiquid market, pretty small base of people who trade it and highly volatile. So, something like rhodium over the years our air rates there have not necessarily been something that we’re telling people to use that as a basis to trade but obviously, it’s a hard problem. Right? And so, we’re iterating that through our ML process and looking at highly volatile commodities is something that we focus on and work to improve those error rates.

MG: Here, I hope you find this to be an interesting conversation because I think it’s a part of the of the way of looking at markets, which not too many people are themselves maybe using but is worth sort of considering. Because I always make a point that nobody can predict the future but we all have to take actions based on that unknowable future. So, to the extent that there might be some data or some conclusions that at least are looking at variables that historically have some degree of predictive power. It doesn’t guarantee that you’re going to necessarily be better off but at least you have something to hang your hat on. Right? I think that’s kind of an aspect to investing here.

Now, I want to go a little bit Tony to what you mentioned earlier you had lived abroad for a while in Europe. And when I was starting to record these spaces to put up on my YouTube channel the first one, I did that on was with Dan Arvis and the topic of that space was around this sort of new world order that seemed to be shaping up. I want you to just talk from a geopolitical perspective how you’re viewing perhaps changing alliances because of Russia, Ukraine. And maybe even dovetail that a little bit into the machine learning side because geopolitics is a variable. Which is probably quite vault in some periods.

TN: Yeah, absolutely. Okay. So, with the evolving geopolitical order I would say rather than kind of picking countries and saying it’s lining up against x country or lining up with x country or what country. I would say we’ve entered an era of opportunistic geopolitics. Okay? We had the cold war where we had a fairly static order where people were with either red team or blue team. That changed in the 90s of course, where you kind of had the kind of the superpower and that’s been changing over the last say 15 years with say, China allegedly becoming kind of stronger and so on and so forth. So, but we’ve entered a fairly chaotic era with say opportunistic macroeconomic relation or sorry, geopolitical relationships and I think one of the kinds of top relationships that is purely opportunistic today is the China-Russia relationship.

And so, there’s a lot of talk about China and Russia having this amazing new relationship and they’re deep. And they’re gonna go to war together or whatever. We’ve seen over the past say three, four months that’s just not the case. And I’ve been saying this for years just for a kind of people’s background. Actually, advised the Chinese government the NDRC which is the economic planning unit of the central government on a product or on an initiative called the belt and road initiative. Okay? I did that for two years. I was in and out of Beijing. I never took a dime for it. I never took expense reimbursement just to be clear, I’m not a CCP kind of pawn. But my view was, if the Chinese Government is spending a trillion dollars, I want to see if I can impact kind of good spend for that. So, I have seen the inside of the Chinese Government and how it works and I also in the 80s and 90s spoke Russian and studied a lot on the Russian Government and have a good idea about how totalitarian governments work.

So, I think in general if we thought America first was offensive in the last administration then you really don’t want to learn about Chinese politics and you really don’t want to learn about Russian politics because they make America first look like kindergarten. And so, whenever you have ultra-ultra-nationalistic politics, any diplomatic relationship is an opportunistic relationship. And I always ask people who claim to be China experts but say please tell me and name one Chinese ally. Give me one ally of China and you can’t, North Korea, Pakistan. I mean, who is an ally of China there isn’t an ally of China.  There is a transactional opportunistic relationship with China but there is not an ally with China.

And so, from a geopolitical perspective if you take that backdrop looking at what’s happening in the world today it makes a whole lot more sense. And a lot of the doomsayers out there saying China is going to fall and it’s going to have this catastrophic impact. And all this other stuff, the opportunism that we see at the nation-state level pervades into the bureaucracy. So, the bureaucracy we hear about Xi Jinping. And Xi Jinping is almost a fictional character. I hate to be that extreme on it but there is the aura of Xi Jinping and there is the reality of Xi Jinping, just a guy, he’s not Mao Zedong. He doesn’t have the power that supposed western Chinese experts claim that he has. He’s just a guy. Okay?

And so, the relationships within the Chinese bureaucracy are purely transactional and they are purely opportunistic. So again, if you take that perspective and you look at what’s happening in geopolitics, hopefully you can see things through a different lens.

MG: Now, I’m glad you’re framing that in those terms because I think it’s very hard for people to really understand some of these dynamics when it’s almost presented like a like the story for a movie. Right? For what could be a conflict to come by the media because and it’s almost overly simplified. Right? When you hear this type of talk. So again, I want to go back into how does that dovetail into actual data. Right? Maybe it doesn’t at all. When you have some of these dynamics and you talk about market clearing data, you’re going to probably see mark movement somewhat respond off of geopolitical changes. Talk about anything that you’ve kind of seen as far as that goes and how should investors consider geopolitical risk or maybe not consider geopolitical risk?

TN: Yeah, I think, well when you see geopolitical adjustments today all that really is… I don’t mean overly simplified but it’s a risk calibration. Right? So, you know Russia invades Ukraine, that’s really a risk calibration. How much risk do we want to accept and then what opportunities are there? Right?

So, when you hear about China, you have to look at what risk is China willing to accept for actions that it takes? Keeping in mind that China has a very complicated domestic political environment with COVID shutdown, lockdowns and all of this stuff. So, having worked with and known some really smart Chinese bureaucrats over the years, these guys are very concerned with the domestic environment. And I don’t although there are idiot you know generals and economists here and there who say really stupid stuff about China should take over TSMC and China should invade Taiwan, these sorts of things. My conversations over the years have been with very pragmatic and professional individuals within the bureaucracy.

So, do I agree with their policies? Not a lot of them but they are well thought out in general. So, I think just because we hear talk from some journalist in Beijing who lives a very sheltered life about some potential thing that may happen. I don’t think we necessarily need to calibrate our risk based on the day-to-day story flow. I think we need to look at like… so there’s a… I’m sure you all know who Leland Miller is in China beige book like?

MG: Yeah, he’s not too long ago.

TN: Yeah. He has a proxy of the Chinese economy and that’s a very interesting way to look at an interesting lens to look through China or through to look at China or whatever. But so, I think that the day-to-day headlines, if you follow those, you’re really just going to get a lot of volatility but if you try to understand what’s actually happening, you’ll get a clearer picture. It’s not necessarily a connection of a collection of names in China and the political musical chairs, it’s really asking questions about how does China serve China first. What will China do to serve China first and are some of these geopolitical radical things that are said do they fit within that context of China serving China first? So, that’s what I try to look at would I be freaked out if China invaded Taiwan? Absolutely. I think everybody would right but is that my main scenario? No, it’s not.

MG: In terms of the data inputs on the machine learning side how granular is the data meaning? Are you looking at where geographically demand might be picking up or is it simply this is what the price is and who cares the source? Because again with hindsight if you knew that the source of China and kind of had a rough sense of the history of Russia-Ukraine maybe that could have been an interesting tell that war was coming.

TN: Yes or No. To be honest it had more to do with the value of the CNY. Okay? And I’ll tell you a little bit about history with the CNY. We were as far as I know, the only ones who called the CNY hitting 6.7 in August of 2019 with a six-month lead time. And so, we have a very good track record with USD-CNY and I would argue that China’s buying early in 2022 had a lot more to do with them from a monetary policy perspective needing to devalue CNY. So, they were hoard buying before they could devalue the CNY and I think that had a lot more to do with their activity than Russia-Ukraine. Okay? And if you notice they’ve made many of their buys by mid-April and once that happened you saw CNY, go to 6.8. Right? It’s recovered a little bit since then but China has needed to devalue the CNY for probably at least nine months. So, it’s long overdue but they’ve been working very hard to keep it strong so that they could get the commodities they needed to last a period of time. Once they had those commodities, they just let the parachute go and they let it do value to 6.8 and actually slightly weaker than 6.8.

MG: The point of the devaluation is interesting. I feel if I had enough space but we were talking about the Yen and what’s happened there. And this observation that usually China will start to devalue when they see the end as itself going through its own devaluation.

How does some of those cross correlations play out with some of the work that on machine learning you’re doing? Because there’s a human element to the decision to devalue a currency. Right? So, the historical data may not be valid I would think because you might have kind of a more humanistic element that causes the data to look very different.

TN: Well, they’re both export lab economies. Right? And we’ve seen a number of other factors dollar strength and we’ve seen changing consumption patterns. And so, yes when Japan devalues you generally see China devalue as well but also, we’ve seen a lot of other activities in on the demand-pull side and on the currency side especially with the US dollar in… I would say over the last two quarters. So, yes, that I would say that the correlation there is probably pretty high but there are literally thousands of factors that contribute to the movement of those of those currencies.

MG: Is there anything recently Tony in the output that machine learning is spitting out that really surprises you? That you know… And again, I understand that there’s a subjective element which is our own views on the world and of course then the pure data. But I got to imagine it’s fascinating sometimes if you’re sitting there and seeing what’s being spit out if it’s surprising. Is there anything that’s been kind of an outlier in in the output versus what you would think would likely happen going forward?

TN: Yeah. You know, what was really surprising to me after we saw just to stick on CNY for a minute because it’s the first thing that comes to mind, when we saw CNY do value to 6.8. I was looking at our forecast for the next six months. And it showed that after we devalued pretty strong it would moderate and reappreciate just a bit. And that was not necessarily what I was hearing say in the chatter. It was kind of “okay, here we go we’re going to go to seven or whatever” but our data was telling us that that wasn’t necessarily going to happen that we were going to hit a certain point in May. And then we were going to moderate through the end of the year. So, you know we do see these bursty trends and then we see you know in some cases those bursty trends continue for say an integer period. But with CNY while I would have on my own expected them. I expected the machines to say they need to keep devaluing because they’ve been shut down and they need to do everything they can to generate CNY fun tickets. The machines were telling me that we would you know we’d see this peak and then we would we would moderate again and it would kind of re-appreciate again.

So, those are the kind of things that we’re seeing that when I talk about this it’s… Oh! the other thing is this: So, in early April we had a we have people come back to us on our forecast regularly who don’t agree with what we’re saying and they complain pretty loudly.

MG: So, what do you say I talk when I hear that because whenever somebody doesn’t agree with the forecast, they are themselves making a fork.

TN: Of course. Yeah. Exactly. Right? Yeah, and so this person was telling us in early April that we’re way wrong that the S&P was going to continue to rally and you know they wanted to cancel their subscription and they hated us and all this other stuff. And we said okay but the month’s not over yet so let’s see what happens this was probably a week and a half in April. And what happened by the end of April things came in line with our forecast and like I said earlier we were like 0.4 and 0.6 percent off for the month. And so that person had they listened to us at the beginning of the month they would have been in a much better position than they obviously ended up being in. Right? And so, these are the kind of things that we see on a… I mean, we’ve got hundreds of stories about this stuff but these are the kind of things that we see on a regular basis. And we mess up guys I’m not saying we’re perfect and but the thing that we when we do mess up, we’re very open about it. Everything that we do is posted on our on our website. Every call we make, every error we have is their wars and all. Okay? And so, we’re not hiding our performance because if you’re using our data to make a trade, we want you to understand the risk associated with using our data. That’s really what it comes down to.

MG: It reminds me of back in 2011 and in some other periods I’ve had similar situations, where I was writing and I was very adamant in saying the conditions favored a summer crash. Right? I was saying that for the summer and the market should be going up and people would say oh where’s your summer crash and I would say this summer hasn’t started. Like it’s amazing how people, I don’t know, what it is, I don’t know if it’s just short-termism or just this kind of culture of constantly reacting as opposed to thinking but it is it is remarkably frustrating.

Going back to your point at the very beginning being entrepreneur don’t do it, that you have to build a business with people and customers who in some cases are just flat out naïve.

TN: That’s all right though. That’s a part of the risk that we accept. Right?

MG: Yeah, the other thing right now that happens with every industry but from the entrepreneur’s standpoint. It’s what you’re doing the likely outcome of your product of your service. You’re trying to communicate that to end clients but then in the single role of the die the guy the end client who comes to you exactly for that simply because they disagree with you know the output, now says I want out.

TN: Oh! Yeah! Well, your where is your summer call from 2011 the analogy today is where is your recession call. Right? So, that’s become the how come you’re not one of us calls right now. So, it’s just one of those proof points and if you don’t agree with that then you’re stupid.

So, I would say you never finish with that there is always a consensus and a something you’re you absolutely, must believe in or you don’t know what you’re talking about.

MG: Yeah, well, thankfully. What you’re talking about so appreciate everybody joining this space Tony the first time you and I were talking. I enjoyed the conversation because I think it said on investing and I encourage you to take a look at Tony’s firm and follow him here on twitter. So, thank everybody. Thank you, Tony and enjoy.

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Visual (Videos)

Supply Chain Innovation, Transformation, and Sustainability

How can leaders and finance teams enable business growth, innovation, and resilience through supply chain management (SCM) and digital transformation? And, how does sustainability affect supply chains? To answer these questions, we spoke with Jon Chorley, Chief Sustainability Officer and Group Vice President of Oracle, and Tony Nash, CEO & Founder of Complete Intelligence.

 

This video interview first appeared and originally published at https://www.cxotalk.com/video/supply-chain-innovation-transformation-sustainability on April 17, 2021.

 

💁‍♀️ Check out more of our insights in featured in the CI Newsletter and QuickHit interviews with experts.

🎯 Discover how Complete Intelligence can help your company be more profitable with AI and ML technologies. Book a demo here.

 

The conversation includes these topics:

 

Jon Chorley is group vice president of product strategy for Oracle’s supply chain management (SCM) applications and leads the team responsible for driving the business requirements and product roadmaps for these applications. Chorley is also the chief sustainability officer for Oracle.

 

Tony Nash is the CEO and Founder of Complete Intelligence. Previously, Tony built and led the global research business for The Economist and the Asia consulting business for IHS (now IHS Markit).

 

 

Show Notes

 

Michael Krigsman: We’re discussing supply chain innovation and transformation and sustainability with Jon Chorley of Oracle and Tony Nash of Complete Intelligence. Jon, tell us about your role at Oracle.

 

Jon Chorley: I run the supply chain management strategy group at Oracle, responsible for our overall investment priorities and directions for our supply chain solutions. I also have the chief sustainability officer role at Oracle where I help coordinate all of our sustainability policies and practices for the Oracle Corporation and help drive some of those ideas and thoughts into the products and services we deliver to the market.

 

Michael Krigsman: Tony Nash, tell us about the focus of your work.

 

Tony Nash: Complete Intelligence, we’re a globally integrated and fully automated artificial intelligence platform for cost and revenue proactive planning. We do forecasting for enterprises and markets in areas like continuous cost budgeting, continuous revenue budgeting, automation of certain, say, forecasting tasks. We also offer agile budgeting and forecasting.

 

We measure our error rates, so that’s important that someone is planning, especially around supply chain. We’re trying to help people reduce the risks around their future costs.

 

Supply chains are very complex: time, cost, quality, all sorts of considerations. Our focus is on the cost element of it, and there are many other things and why we’re working with Oracle. They have so many other things to bring to the table that try to complement them on that side.

 

Michael Krigsman: You met Jon through the Oracle startup program. Just briefly tell us about that.

 

Tony Nash: Oracle for Startups program is a fantastic way for early-stage companies to integrate with the Oracle ecosystem. There is the Oracle technology product side of it, but there is also meeting people like Jon, meeting people like his colleagues, and the Oracle marketing team, Salesforce, and product teams. Amazing opportunities to understand how an organization like Oracle works and how a company like Complete Intelligence can come alongside them and enhance Oracle’s end customer experience for the better.

 

 

How did supply chains function during the disruptions of 2020?

 

Michael Krigsman: Jon, during the last year, supply chain became a household topic for pretty much everyone.

 

Jon Chorley: Yes.

 

Michael Krigsman: What did the last year tell us about the nature and the reality of supply chains?

 

Jon Chorley: Well, that they’re central to everything that makes the modern world. When you see an empty shelf and realize it’s an issue with the supply chain. Or you see a run on a product as some shortage or some challenge in some way. People now understand that the complicated infrastructure that brings those products to them is the supply chain.

 

As we’ve gotten into the more recent months where we’re looking at the vaccine distribution, people understand that yes, it’s a technical problem to produce the vaccine, but it’s also a supply chain problem to get it in people’s arms.

 

All of those things, I think, have helped take the supply chain from the back office, from the folks like Tony and I who work in it day-to-day, into the board room, which I think is very important. But also into the dining room. People now understand the importance and centrality to efficient supply chains.

 

Michael Krigsman: Jon, give us some insight into the kinds of weaknesses that this last year exposed in how we handle supply chains.

 

Jon Chorley: I think that there are a couple of areas there that I’d point out. One is we had a very uncharacteristic demand shock. There was a real change in short-term demand.

 

Some of that was upside. A lot of charcoal sold to power the grill. A lot of toilet paper.

 

Some of it was downside. Restaurants challenged, hospitality, and so on.

 

Those demand shocks forced people to look at different ways to look at their traditional forecasts. That is supportable by the kind of technology Tony and I can help deliver, but it does require people to look carefully at how they’re forecasting their demands. That’s one angle.

 

Another angle, I would say, is the overall concern about resiliency. A lot of folks looked at ways of single sourcing, for example. Maybe relying on goods out of Western China, for example.

 

All of those things had a lot of challenges, and that forced people to look at, was the single-sourcing strategy driven by cost only the right answer? Did they need to look at A) maybe simplifying their product lines a little bit, so they had more flexibility, and B) looking at alternate sources of supply? I think resiliency came a lot more to the fore.

 

Tony Nash: We’ve had even companies like semiconductor companies (who have been based in Asia) start to build facilities in the U.S. so that they can regionalize some of those supply chains and de-risk the downturn impacts of future shocks like this. Electronics manufacturers, other people who are assembling goods, or even some primary goods, are regionalizing their supply chains so that they don’t see huge impacts or any future issues like COVID or other shocks.

 

There’s at least a little bit of a buffer by region, which saves. It’s greener in terms of saving on the sea freight fuel and that sort of thing, but it also helps cushion any shocks on the supply side so consumers can get what they need when they need it.

 

 

Challenges associated with overseas manufacturing operations

 

Michael Krigsman: Jon, I’ve heard you talk in the past about the inherent challenge of manufacturing goods overseas (in China, for example) and the timeliness of getting them here in the U.S.

 

Jon Chorley: It has a lot of advantages in terms of costs, scale, and so on. But it does bake into your supply chain a certain fixed amount of time. That is fine if you have predictable demand. But if you have variable demand, it becomes a lot trickier to manage.

 

The same is true really of the innovation cycles. The speed with which you may want to innovate can be constrained by working those things from points of consumption (let’s say Europe, North America) and points of production (let’s say the East, China, Vietnam, and so on). Those are factors folks are considering.

 

I think, in some areas, certainly advances in things like automation and technologies like 3D printing, rapid prototyping, those things are changing the equation a little bit in terms of what constitutes the most cost-effective or the most efficient, or the most responsive approach to manufacturing. I think you’re going to see those factors gradually have more and more of a play as people develop new ways to balance those equations.

 

Tony Nash: Michael, that’s interesting because, as we look at how the history of supply chains have evolved from keeping POs on 3×5 notecards 30 years ago to the digitization of that, it started with EDI (electronic data interchange) from, say, the ocean lines and the airfreight firms so that you knew where your package was, all the way down to today where you have everything kept, let’s say, in a bill of material within an ERP system or a supply chain system.

 

What people have been doing for the past few years is really bill of material versioning, where you’re running scenarios on the same product configuration, of bill of materials for multiple locations, to understand where they should make a certain good. Those considerations are allowing people flexibility. They can make the time and cost tradeoffs to look at when they can have goods in a market, whether it’s seasonality or whether it’s some disruption or whether it’s some demand pop for some reason people may not know. Allowing people to run multiple bills of material or versions of bills of material allows them the flexibility to identify what they should produce where and what it should be made of.

 

Michael Krigsman: It sounds like this is a data and analytics problem.

 

Tony Nash: It is, and the way things have been done typically is, as a manufacturer, you sign a longer-term agreement for your raw materials with a vendor. They provide that for you to a certain point. You make it in factory A somewhere and then ship it out. Of course, there is not necessarily a single factory for any large company, but it’s a well-worn path.

 

We’ve had an atomization of that with mini manufacturing, or regional manufacturing, flexible manufacturing, so people can have localized versions or, like I said, seasonality. These sorts of things. Manufacturing and finance teams can only make those types of decisions with data and with automation. It’s a simpler way on how to make better business decisions.

 

 

Digital tansformation and sustainability in supply chain

 

Michael Krigsman: You need clarity around the goals and the strategy. You need the right kinds of data. Then you need the cultural willingness to innovate and do things differently. Is that an accurate way of summarizing?

 

Jon Chorley: I agree. I think you need to have some idea of where you’re going. Although, that probably is going to change. But you need to have that idea. You need to have the information, as Tony has discussed, that helps you navigate that path.

 

Then you need to be able to course-correct because we live in the real world, and nothing quite goes the way you expect it to. You need to be able to constantly course-correct.

 

Like I say, if you have a great set of headlights, you can see what’s coming. You’re coming to a cliff. If you have no brakes and no steering wheel, it’s a huge problem you’d rather not know.

 

The ability to course correct is like having brakes and a steering wheel. You need to be able to make those adjustments as things change around you. That means flexible systems, flexible processes, a willingness to look at new ways of doing things, cultural changes. All of those things become important.

 

Michael Krigsman: Tony, I have to imagine you spend a lot of time thinking about the sources of data as well as the machine learning models and other types of models that you create.

 

Tony Nash: I get excited about things like data governance, but most people don’t. I get excited about it because I understand that it helps to have much better forecasting applications and tools to make those decisions.

 

Yes, we’re thinking about the granularity, the frequency, the level of detail people have. Are they using the data that they have to make decisions today because it’s not just, let’s say, a cultural change of let’s rely on automation of things like forward-looking views or forecasting or proactive planning? It could also be a cultural change: are we looking at our data to make our decisions? How much of our data are we looking at? Are we looking at maybe the error rates of the way we plan? Are we looking back on that from time to time?

 

Although that may seem mundane and small, it’s actually very big for things like digital transformation because you have to take inventory of what you’re doing today so you can plan where you’re going tomorrow. As Jon said, it’s never going to go exactly to plan – never. I wish it would, but it never does. You have to understand yourself well today so that you can identify what’s possible.

 

Michael Krigsman: Jon, we’ve been talking about the complexities of supply chain. Let’s shift gears slightly and talk about the complexities of sustainability. How does sustainability intersect supply chain?

 

Jon Chorley: Most people would agree that supply chains are about making and moving physical goods around the world. That is a huge part of what’s impacting the environment. It’s a huge impact on sustainability.

 

The way we design those supply chains, historically, has been what I would call a linear supply chain. Which is we make a product, we sell a product, we forget the product. We then make another product, sell that product, and forget that product. It’s a fire and forget mentality, if you like – to some degree.

 

If we want to be sustainable, we need to think about the full lifecycle of those products and how they get recycled back into the forward supply chain. As we progress into the future and start thinking about these things more — and we’re required to by the markets, by regulations (potentially), and by what constitutes good business — we will increasingly move towards adjusting our supply chains to be more circular. That is, looking at the full lifecycle of the product.

 

That begins with how you design it. That’s going to be a fundamental change in the way we think about all supply chains.

 

Advice on supply chain transformation for business leaders

 

Michael Krigsman: As we finish up, Tony, can you offer advice to business leaders and finance teams who are listening to this who say, “Yes, we want to change, transform our supply chain, but where do we even begin? It’s such a daunting challenge.”

 

Tony Nash: I would say, really start with the easy stuff. Get some successes. Do a pilot. Then you can accelerate it very quickly.

 

Data scales very quickly. Technology scales very quickly. But your team may be uncomfortable with digital transformation, especially around supply chains. Help them see some quick wins and then push forward as quickly as possible after that.

 

Michael Krigsman: Jon, you discussed earlier the cultural dimensions of supply chain transformation. It’s really important, so just share some further thoughts on that and advice that you have for folks who are listening.

 

Jon Chorley: I think any change is at least as much cultural as it is technological, and the people who implement those changes are key to its success. I think part of what’s needed is a willingness to understand that the way you did things in the past may not be the way you need to do things in the future.

 

Quite often companies, for example, feel that they have a certain special way of doing a process that’s absolutely required, and they hold onto that even though there is really no business differentiation for them to do it that way. They’ll invest a lot of time and energy to duplicate that on a new platform.

 

We always encourage people to step back a little bit and leave behind some of those preconceptions. Not everything is your secret sauce. Your secret sauce is a little bit on the top. It’s not stuff on the bottom.

 

Leave behind those preconceptions. I think that’s probably the single biggest cultural shift.

 

Then the other point we mentioned earlier is board support. I think that’s top-down. Having that support from the upper levels of the business is critical to any large-scale transformation.

 

I think the great thing, if there is a great thing from 2020, is that boards are aware now of the criticality of supply chains in their business and are probably more open to those kinds of conversations. Those difficult conversations from supply chain professionals with their board. Now is the time. The folks that make the investments now are the folks who are going to benefit from the uptick that we all hope is coming.

 

Michael Krigsman: Jon Chorley and Tony Nash, thank you both for sharing your expertise with us today.

 

Jon Chorley: All right.

 

Tony Nash: Thanks, Michael.

 

Jon Chorley: Thank you so much. Great talking with you all.

 

Tony Nash: Thank you.

Categories
Visual (Videos)

A Mission-Critical Focus to Enable Growth

This article originally published at https://www.admentus.com/podcast/a-mission-critical-focus-to-enable-growth-with-tony-nash-of-complete-intelligence/ on March 26, 2021.

 

 

Every company wishes they have a crystal ball when it comes to making business decisions, and while a physical iteration of that wish is not possible, Tony Nash has developed the next best thing for his clients at his startup, Complete Intelligence.

 

Tony is the CEO and Founder of Complete Intelligence. Before founding Complete Intelligence, Tony was the global head of research for The Economist and the head of Asia consulting for IHS Markit.

 

Complete Intelligence is a fully automated and globally integrated AI platform for smarter cost and revenue proactive planning. Using advanced AI, they provide highly accurate cost and revenue forecasts fueled by billions of enterprise and public data points.

 

Key Takeaway: As a growing, scaling business, you must know what you are good at, what you do, and what you do not do. Maintain your mission-critical focus on the most important aspects of your business and outsource the parts that you are simply not good at or are outside of your mission.

 

Lessons Learned:

 

• Put Significant Thought into Your Senior Hires – hire low first, then hire the upper levels as they will be the ones that have to share your mission and must be the right hire.

• Know what You Do Not Do – Knowing what you don’t do is just as important as knowing what you do do.

• Define Your Culture – Define the culture you are building and continually and intentionally reinforce it.

 

Show Notes

 

JC: Hello everybody, Jeff Chastain here with the building to scale podcast again, where I get the opportunity really to speak with entrepreneurial business leaders growth-minded leaders who are working to grow and scale their own companies. And some of the we’ll discuss some of the challenges. Some of the successes as they’ve had over the years working through that.

 

Today’s guest with me here is Tony Nash with Complete Intelligence out of the Houston, Texas area. So first off Tony welcome to the show and thank you for taking a few minutes out of your busy day to join us here.

 

TN: Thanks, Jeff. I appreciate the opportunity.

 

JC: So give us a little bit about what Complete Intelligence is and what you guys have got going on there?

 

TN: Sure. We run an artificial intelligence platform. We use it to forecast market activity say currencies, commodities, equities for investors. We also help people companies understand their costs and their revenues which are really important on the budgeting side. So we help people de-risk their future business decisions by understanding where their costs are going to go and where their revenues will likely go.

 

JC: Okay, so I’ve got a background in technology and we kind of talked about AI and stuff beforehand but if we were to bring that down. And say okay I put you on the spot here but it was well the networking questions I’ve heard before like. Okay, if you describe that to a five-year-old what do you really do? So I know we kind of talked beforehand that this is typically big enterprise focus but for those that are not into that industry or not dealing with 9 10 figure dollar budgets, kind of a thing. Proactive budget planning. What does that really mean from a obviously from a company your size or your perspective?

 

TN: Sure, if I have to describe it to a five or ten year old. It’d say look, if you run a lemonade stand you have to understand how much the lemons are going to cost. How much the water is going to cost. How much the sugar is going to cost you. Also want to understand how many customers you’re going to have. How much money they’re going to spend. How much money you’re going to take in through the lemonade stand, right?

 

So we work with customers to understand all of those things. Now when companies themselves forecast this stuff and we know this from talking to our clients. They typically have 30 error rates or worse, even for raw materials costs. So their planning is way off, okay? When you look at industry experts investment banks economists, industry experts, these sorts of things. Their error rates are typically 20% off, okay? Our error rates are typically about around 4.6 percent, okay? And that’s on an absolute percent error basis. So we’re not gaming the pluses and minuses, okay?

 

So if you’re buying those lemons and that sugar and that sort of thing you can pay a dollar 20 for it. For us maybe a dollar five or something like that, right? So we’ll help you save 15 cents a lemon, okay? And you’ll understand where those costs are going. And so when you scale that up to very large customers who have you know 2 billion, 5 billion, 20 billion dollars in turnover or more. They’re buying in tens and hundreds of millions of dollars.

 

So let’s say a 17% improvement in their ability to forecast things, those are very large numbers. And so we’re working with enterprise scale data in the cloud and helping them understand where their business is going. And I would say probably better than just about anybody else out there. And so it doesn’t have to be the biggest company in the world doing this stuff. We work with mid-sized companies as well, okay? Because we’ll take data out of their enterprise planning system or something like that. And we’ll use it on our platform to help them make better decisions. We’re not telling them what to do, we’re just telling them where the data tell us that things are going to go.

 

So the real problem we’re solving aside from the obvious of what’s going to happen in their markets and their costs. Every company has a very painful budgeting process, okay? Some companies it takes a month or two or three months. Some companies some of our customers it takes six or seven months. And they’re going through in a very meticulous way of proactive planning their budgets. And there are hundreds of people involved and at the end of the day it goes up to the CFO and the CPO the chief procurement officer or the CFO and the head of sales and it’s a verbal agreement on what’s actually going to happen. This is actually one of the CFO pain points.

 

Not all that data driven, right? And so what we do is we give them a straw man to base it on so they can a very meticulous and detailed straw man. So that seven month process is taken down to a couple days, okay? From data transmission processing to sending back. And they also get a continuous budgeting exercise, okay? Every month we’ll reforecast their budgets for them so if something like Covid happens as it did last March, April. We help them understand what’s likely to happen uh in their business.

 

JC: Now that makes sense and that’s really one of those things that regardless of the business side that it’s like, okay having actual real data not seven month old data actually having it on a monthly basis or even closer kind of a thing. You can actually make real decisions on it at that point rather than just thinking like you said one code would happen. Everybody had their budget set January, February for what 2020 was going to be. And now two months later they’re completely invalidated that either the like you said earlier some some businesses are up, some are down, some are pulling back the the expenses. So it may have turned out okay but all the proactive planning they did initial on is completely out of window at that point.

 

TN: Right and most of those guys their revenue budgets were blown out like they had no idea what was going to happen there. They were saddled with their cost budgets that they had to continue paying for all this stuff. They didn’t know what was coming in on the top line. And so they then had to be very reactive on the on the cost side. And initially it was just a lot of you know arbitrary cost cutting and no disrespect to anybody. They were doing the best they could right but a lot of these big companies initially were just like, we don’t know what what we’re going to be in three months.

 

We were initially told covered was four to six weeks. And you know it’s still going on right and so what we saw is a lot of companies cut costs in the second quarter and the third quarter and by the end of the third quarter the management views looked up and said, well we’ve cut it as much as we can through the first three quarters let’s not release any more budget in Q4. So that just helped them on the income side so that they you know their bottom line looked better than it probably would have if they would have been a status cooperation.

 

JC: Yeah

 

TN: But still what we’re doing is using actual live data to help clients make the actual decisions that they need to make to run their businesses.

 

JC: Yeah and that’s really to me the key whether you’re got the small business that you simply just don’t have that much data to be processing all the way up to the enterprise. It’s still the same thing of saying, okay making those decisions on the numbers rather than, like you said with with Covid where it’s almost an immediate knee-jerk panic reaction of, hey we’ve got to cut things or hey everything’s going to be down. It’s like okay let’s look at the numbers and hopefully by a Q2 Q3 et cetera we’ve got some actual real data that we can start looking at.

 

So but yeah that’s that’s interesting so going back to Complete Intelligence then take us back. And say I think you said it 6 to 7 years old for the company itself. So how did this how did this kind of come about from a entrepreneurial standpoint.

 

TN: Sure, yeah, I used to run global research for a company called The Economist based in the UK, publishing company. And then I moved to a company called IHS Market which was just bought by S&P about six months ago. I was their Asia head of consulting. I was working with clients on a lot of data-driven decisions. And what clients were telling me were two things first the forecast that everyone was doing not just stuff, us were wrong and there was no accountability for that, okay?

 

The second is they could never get a forecast for their exact decisions. Forecasts were always too high level or not the right thing or something. So I rolled out of IHS market saying I want to have a data driven company that actually helps people make real decisions about their business. And so we started as a consulting firm for our first few years we were a consulting firm. And I was trying to understand the types of decisions that people needed to make I knew it from my consulting days with bigger firms but I wanted to understand what we could actually do.

 

About three years in we decided to turn into a product firm. Which is a very different type of business and so you know we built an initial platform that was very customizable but then to productize it out to build it to scale really is a very different skill set. Aside from a little bit math and a little bit of code it’s a very different same marketing and sales operation. It’s a very different you know infrastructure and all that stuff, right?

 

So a couple years ago we decided to productize with some subscription online subscription data products. And then we’ve got more specific with say cost and revenue products. So, I started the company in Asia in Singapore and then in 2017 we moved to Texas. So part of our, my calculation there was the talent in my mind is better here in the US. The customers are much easier to access here in the US and the business environment is pretty friendly. So it was a pretty easy decision for us to decide to come to Texas.

 

JC: Interesting. Okay. So what kind of challenges or what did you face in going from I guess I don’t necessarily know what your role was when you were saying with the economist except I’m assuming you’re you’re managing a team but you’re not necessarily managing a company. At that point to now owning and running your own company here with you said what 10 11 something employees up to now?

 

TN: Yes, that’s right that’s right, I think. So you know first is always the administrative part of it, right. I mean I think every new business owner just isn’t aware of the administrative stuff. And also the fear of missing something, right. What have I not done. what what tax filing have I not done or you know something like that, right? So there’s always that which was not a major issue but it was an additional burden.

 

When I think the biggest part of it was, I was just doing everything. And you come as a as a business owner you come to a point where you’re doing everything and you’re involved in everything. And then you’ll come to a point where you have to delegate stuff. And finding the right balance of when to do that and how to do that is I would say it’s more art than science. And other things like scaling RIT infrastructure that’s never really a decision I’d make before. I’m a math nerd and economics and data nerd, right.

 

So you know those types of decisions were really new but also on the customer side. Although, I had been customer facing when and this is kind of a no-brainer of course but when you don’t have a big brand behind you. Getting to the right people is a much more difficult process. And so we, I knew that coming out of the gate but I underestimated how hard it would be.

 

We started talking with some of our sales partners right away. Knowing that they wouldn’t give us a yes, right away but starting the relationship so guys like oracle guys like Bloomberg, Microsoft, Refinitive Tompson, Reuters these guys are all major partners for us now. Major sales channel partners and it took us four to five years to get those relationships moving and commercialized. So for a small business owner who is looking at channels as a major part of their business strategy. I would recommend you have to start talking to those partners right now like a year or two or three before you intend on getting your first dollar.

 

And so the other part as we’ve grown is we’ve had to think through, what do we do well as a company. And what’s best for us to outsource so things like HR. You know what, we don’t have an HR team. We have an outsourced HR firm, right, that’s a no-brainer but you know I can’t do it all myself. I don’t know the laws and stuff so we have outsourced HR. As I said with our channels we are scaling up our sales force but to have that as a kind of a force multiplier is huge for us, right. And things like marketing we have a marketing team in the Philippines and we have some marketing here but where can we get great skills at the best price really, right. And so we have to look around to find out you know what that stuff looks like.

 

We don’t have any of our data science team or any of our developers offshore. They’re all here in the US and part of that is for our client base. We don’t want things going to Eastern Europe or Asia or whatever but where we can push things off and make sure that we keep our core business. We’re happy to push things off. And so what I mean is we are a technology company, okay. We are not a human resources company we are not a marketing company and we’re not a consulting firm. And so we partner or outsource so that we can stay small and scale but do it very very well.

 

JC: Yeah and really even still that’s giving you the ability to scale because you’re not having to hire in like you said a whole team of HR. It’s a lot more cost effective especially for a smaller business to say hey we’re going to go pay a much smaller fraction of that to an outsourced group still allows you to scale and grow the business but at a much slower cost at that point.

 

TN: Right.

 

JC: So kind of what was that did you just walk into that and say day one we’re just not going to do HR. We’re just not going to do marketing etc. or was that kind of a a transition process because I know a lot of people will try to do some of it before they finally throw up their hands. And say okay, yeah this is not us or how quickly did you make that handoff there.

 

TN: That was immediate. I knew we didn’t want to do that from the start. Just from my corporate experience I knew that that wasn’t something I knew that we would spend a lot of money there not necessarily get good value. And so when somebody is a vendor you can you know you need some output, you need some outcomes. And so we just chose to make some of those guys vendors instead of making them full-time employees.

 

JC: So I’m curious since obviously you’re a numbers driven company accounting stuff like that. What does your relationship with some of these vendors look like how much of a numbers kind of basis relationship are you doing with them or are they is that more free flowing?

 

TN: Well, U think when you say numbers basis what what do you mean by that? I’m sorry.

 

JC: A lot of times. I’ll work with companies to sit here and say okay we’ve still got to measure our return on ROI kind of a thing on everything. So do we have specific numbers do we have specific milestones measurables et cetera tied to outside vendors the same way as we’d have tied to an employee?

 

TN: Oh, yeah absolutely. So like with our HR you know our outside stage our vendor. What we get from them on a monthly basis, I would probably have to hire a couple people to do internally. It just doesn’t make sense for us the the fully loaded FTE costs are just way too much. On the marketing side, unless somebody has absolutely stellar marketing skills, a lot of the direct marketing campaigns, social media marketing all that stuff for a firm our size at least it just doesn’t make sense to hire somebody. We can direct that activity manage it every day that sort of thing but the execution of it is better outsourced because we can do better with an outsourced vendor like dramatically better than we can by hiring those people directly, right. And so and so and we’re not talking a small kind of we’re saving 20% we’re saving a lot more than that by hiring marketing people directly.

 

JC: Yeah, that makes sense.

 

TN: Yeah and so I think again with most of the decisions we make. We really question how core is that to our business does it add to the technology, does it add to the customer relationship? And that’s really what it comes down to so I think we’re you know we’re at a place with things like video calls. And with a lot of the other technology that’s come around over the last 10 years. Where you don’t necessarily need that you don’t need everything in house it’s just not necessary. And if I have a vendor then I don’t necessarily have to pay for them to learn. If somebody is on staff I have to pay for them to learn. And so it’s not necessarily all fully productive time, right. And so again we’re very results oriented company. And so again we think through all that stuff. So for the guys who are watching your podcast. I would say look you know if you’re growing a company you really need to think through what your head count expectations are. What are they doing can you get that outsourced do you absolutely need to hire that person or can you turn it into an invoice.

 

JC: Yeah and that’s that’s really the the key because I see a lot more today of having a lot more availability and options of those outsourcing kind of a thing. That it’s not just necessarily the one big accounting firm that you had to be local face to face meeting somebody with the technology these days. I can have my account on the other side of the country kind of a thing and it’s just no big deal or I can have a marketing firm like you said all the way over the Philippines. It’s no big deal at that point so it’s almost it’s driven competition in those fields for sure. So it’s really almost like you said a no-brainer that okay why would you why would you want to go build your own in-house marketing firm when you’re a technology company or when you’re a financial services company something like that. It’s like that’s not your core business but still really identifying that core business is obviously the key there.

 

TN: Right.

 

JC: So talking about that core business you said you kind of made a an evolutionary change there with within your own company of saying okay consulting to now today being the the 100 product focus. What did that process look like or I guess for that matter? Why did you necessarily say because a lot of people I was that was my own background coming out of corporate America was, okay we’re going to be a consultant kind of thing. So how did you go from the consultant to saying okay we need to do something different or something transitioning towards the product side?

 

TN: Yeah, it’s very simple. As a consultant my upside is limited. I only have so many hours in the week and I can only bill against those hours. And if I hire people the upside is limited for them, right. So and if I want to grow a large revenue base I then have to hire a lot of people and then add x percent on top of their cost. And you know if their time isn’t sold then I can’t hire them anymore, right.

 

So I just got really tired of being the main guy consulting and you know billing against my hours. And so we productized because you know I wanted to make sure we could scale the kind of intellectual property that was in my head. And build that out as much as possible. Now that process was a it took a lot longer than I thought and a lot longer than I had hoped. That transition really took 18 months to two years. So you because you know, I had resources that were helping us on client engagements. I had to take them off of client engagement so they weren’t revenue generating to develop the IP around our product business because they can’t do both, okay. They can’t serve clients and develop IP because the development of  IP always gets put off. And so I had to make as a business owner, I had to make a very hard decision to say we’re going to stop you know selling, right now, okay.

 

And I’m going to pay the cost on these resources to develop this capability so that we can then productize it in 18 months time. And that was a very very hard decision but we did it because we had to otherwise I would have been flying all over working you know 90 hours a week, all that stuff. And we did it we bit the bullet and we came out with some pretty amazing capability.

 

JC: Oh and that’s really the key to me of saying, yes it’s a longer term vision you’re playing the longer game there even like you were talking about with the channel partners. Okay, you gotta start investing in things now looking towards that that longer term goal. And if you’re only looking towards next quarter, next month even next year. You might not necessarily have made that change to go product because you’re just looking at okay how can we get more billable revenues here in the next quarter.

 

So yeah it’s looking at that so kind of going down that direction. What does what does the vision look like for Complete Intelligence? Well how do you define vision from a company perspective and what’s your what’s your bigger picture vision there since it obviously sounds like you’re one to look longer term than just focusing on the immediate short term?

 

TN: Yeah I think so so our focus is really to continue to build out what we’ve started to do which is licensing sales for our core capability and aligning with other products. So how do we get built into core let’s say core erp software or core e-procurement software or you know something like that. So that a client doesn’t even have to think about working with us it’s just all baked into that software, right. And so that’s part of the vision.

 

The other part of the vision is how do we ensure that the results of our efforts are easy for a client to work into their internal processes. So just producing data or just producing something. If it’s an extra step then it’s a hassle for people, right. So how do we make sure and part of this is integration with other software that sort of thing but how do we make sure what we’re doing is really really easy for our customers to use. So that it helps them rather than adds more tasks to their day.

 

JC: Makes sense. So a lot of times I’ll see this where the the company owner. I’m not saying you are but the company owner has the vision there the ideas going forward how do you bring that down or how how do you bring that down in your own company to the team to say okay there. How do you get them bought into that vision or them understanding that vision internally?

 

TN: I think anybody doing that has to be comfortable with a lot of kind of a lot of mistakes and ongoing iteration of processes. I may have a short-term view of things that may not be right my team may be doing stuff that ends up wrong. I have to be okay with that and we have to learn. So and it’s not that’s not a luxury if you’re doing something like we’re doing we have to be a learning organization that is always seeing things that aren’t just right. And say okay that’s not right let’s take a couple days fix it. And then we’ll you know we’ll roll it out again or something like that, right. So as a software company we can do that. If we were making something physical it could, it would be different.

 

JC: Yeah.

 

TN: But as a software company we can iterate as we’re going, right. And so I think delivering that vision is really helping people understand on an ongoing basis. What the original vision is but then adjusting incrementally on a regular basis. And those regular adjustments they may be technology issues where we can’t actually do what I want to do, okay but that’s fine we iterate and we move along toward that path.

 

JC: Makes sense. So running a little long here running out of time. I always like to kind of come back and we we’ve talked about a bunch of different things over time but still what is kind of the best tip the best strategy that hey if I had known this six years ago. When we started the company or if I had this in mind this path in mind things might have been easier? What comes to mind as being your kind of your top idea here that wish I’d known this or thought about this or done this earlier?

 

TN: I think you know the biggest thing that I would have done is really thought through what I needed in a management team. If you’re scaling and you’re building the people who you put in place in a management team are really really critical. So what I would say is higher lower levels first and then make sure that the senior level management team that you’re hiring is somebody that you can really trust and someone who can really manage a team.

 

So put off those senior hires as long as possible. And it’s going to be painful and it’s going to mean you’re going to have to work a lot. And you know that sort of thing but higher low first then higher the upper levels, okay. And that’s almost the opposite of what say a venture capital investor or something would tell you. They want to see a management team but the fact is you need execution and then you need to build into those senior people that you can really trust to execute on the vision.

 

JC: That makes sense that’s interesting since we hadn’t touched on that one yet. I was figuring you’d go different directions but yeah I know a lot of times I’ll see that especially with the small ones if you’re don’t not having to do venture capital or stuff like that because I do agree there but a lot of times it is. Still it’s almost more the challenge that was what I run into of you start building out the lower levels. And you’re still trying to wrap your arms around it for honestly too long before you start introducing that management but yeah it’s doing that lower level and really understanding what’s going on first. And making sure you’ve got to keep handle on it before you can start bringing in people and really focusing at that point on.

 

Okay, what even going back to like what you were saying. Okay, what’s our core focus in the business this turns into. Okay, what’s your core focus as a leader to say. Okay, what are the aspects that I don’t want to do that I don’t enjoy doing that I don’t do well etc to hire on but yeah I like that from the focus on on building out the lower level team first that makes a lot of sense because a lot of times you’ll see startups said hey here’s our full sweet sea level
suite all these people we brought in it’s like. Okay, who’s actually doing the work at this point so yeah very cool, right?

 

TN: That’s right.

 

JC: So the listener wants to learn more about uh your company about Complete Intelligence about yourself where can they go find some more information here?

 

TN: Sure, so you can find us on on the web at completeintel.com. On social media on twitter we’re @complete_intel and you know just look us up online and we have a lot of interviews. A lot of resources on our website to find out more.

 

JC: Okay, we really appreciate it so thank you for taking time out.

 

TN: Thanks Jeff.

 

JC: Thank you.

 

TN: Thanks have a great day.

 

Categories
Podcasts

Forecasting Global Markets with Artificial Intelligence

“Bitcoin Kid” JP Baric is joined by Tony Nash in this premier episode of Digital Gold.

 

Tony Nash is the CEO and Founder of Complete Intelligence. Using advanced AI, Complete Intelligence provides highly accurate market, cost, and revenue forecasts fueled by billions of enterprise and public data points. Previously, Tony built and led the global research business for The Economist in the Asia consulting business for IHS he’s also been a social entrepreneur, media entrepreneur, writer, and consultant.

 

JB: Tony, as I mentioned, you’re the founder of Complete Intelligence. Can you tell me a little bit more about what Complete Intelligence does and how you work with your clients?

 

TN: Sure, yeah. As you mentioned in the intro, I led global research for a British firm called The Economist and I led Asia consulting for an American firm called IHS Markit. In that time, over about a decade, I had a bunch of clients come to me saying, we have two problems. First, forecasts are terrible and that was a comment both on the work of the firms that I worked with as well as just the market generally and they said forecast error rates are terrible. There’s no accountability of the forecasting saas and nobody tracks their historical data, so we have to try to dig it out ourselves.

 

So forecast accuracy is a huge issue. The second issue is the appropriateness of a forecast. So if you make a chemical or a mobile phone or cake mix, there are specific items within that product that you need to know the cost of. But you may not be able to do that internally. Major companies have hundreds of Excel workbooks floating around with their forecast for sales or for costs or whatever and it’s just really confusing. So what ends up happening is people kind of manually estimate costs and revenues. And so, what we wanted to do was automate that entire process company-wide.

 

We wanted to take out the human bias that comes with the forecasting industry and internal forecasts and all that stuff and we really wanted to build products that allowed the machines to learn how markets move so that’s currencies commodities equities and so on as well as how company revenue and spend changes over time.

 

JB: So when doing some of my initial research on Complete Intelligence, basically just to paraphrase, you guys are taking the spot of what an analyst would do. Is that correct?

 

TN: Yeah. But here’s what we don’t do. We don’t put together a report on what’s going to happen in industry x or with commodity y because what we find is when that stuff is put together so when an analyst puts a report together on some aspect of an industry, it’s really loaded with a lot of, let’s say, a house view on something or a personal bias. And so we do have a weekly newsletter and we do kind of video podcast that sort of thing. But we don’t have industry notes because we don’t want our clients to feel like we have bias towards say the oil and gas sector or toward industrial metals or that we’re for or against gold or for or against crypto or something.

 

There’s so much of that loaded into forecasting today and it has been that way for decades, that we just want to let the data and the sophistication of the data… we’re doing billions and billions of calculations every time we run our process. Humans do this but they’re not aware of it. The humans also aren’t aware of the amount of bias that they put into their calculation. So what we do is we track this and we track it based on error rates and we allow the machines to correct based upon how they’ve made error over time. It’s just like an infant learns, right. You touch a hot stove and you learn not to do that again. It’s very similar the way we kind of reinforce the behaviors that we want within our platform.

 

JB: I guess my question to you is when it comes to these machines, they’re learning in the background so you don’t have a team of a thousand analysts. Instead you have a team of a thousand neural networks or machines basically working for you running these calculations 24/7 on all these different commodities and are they just making assumptions and then confirming if those assumptions are right and then the models that do better end up going end up kind of getting weighted more? How does that work, I guess? How do those questions and answers work in those data testing points, those AB testing that you mentioned.

 

TN: It’s a good question. So we’re running tens of thousands of scenarios for everything we forecast, every time we forecast. And then we’re looking at which ones best reflect the market as it stands right now and then we add in the different approaches on a weighted basis to make sure that they reflect where the market is. So it’s a multi-layer analysis. It’s not just a basic kind of regression correlations driver, that sort of thing. We’re also looking at the methodologies themselves.

 

Some of these are very fundamental, traditional statistical methodologies. Some of them are more technically-driven say decision trees, those sorts of things, types of machine learning models and we’re looking at how on a proportional basis those different methodologies best understand the market at this point in time. And so yes. I mean, that’s a long way of saying “yes” to your question.

 

JB: No. I think that was a great answer. So you guys are looking at currencies, equities, and in July you discussed gold and silver being nature’s Bitcoin. Can you explain to our listeners what you mean by that and provide your thoughts on bitcoin as a store of value and where you see that blockchain space going?

 

TN: Well I think one of the key aspects of cryptocurrencies is that there should be a fixed amount of it. If it really is immutable, then there’s only so much of it and if there really is demand for something that’s limited, then the value should rise or fall based upon the availability of that fixed good, right?

 

Gold is similar in that I can’t necessarily go and buy a car with gold. I mean I’m sure I could. I can’t buy a loaf of bread with gold. I think cryptocurrencies is becoming a bit more spendable than precious metals, a bit more useful depending on which cryptocurrency you’re looking at. But yeah, it is similar in that cryptocurrencies to date have been more of an asset than a currency. They’ve behaved more like an asset than a currency.

 

Meaning the value goes up and down pretty dramatically based upon the perception of scarcity. Currencies don’t necessarily act that way. Currencies act as units of value so that you can buy other stuff. And so, it is. Gold is on some level kind of nature’s bitcoin or nature’s cryptocurrency. But I think we’re coming to a point where there’s a division between those two, where cryptocurrencies are starting to be used as and when II say starting of course they have already been, but more broadly be used as vehicles to buy other stuff not just stores of value. So the former is a currency the latter is an asset.

 

JB: Yeah. I definitely agree with you on that point as we move down this line of utilization. We saw with the Paypal news that recently came out Square News. Hopefully people will start using bitcoin more as a day-to-day currency. It’s one of the biggest I guess questions I get is, you know, it’s too hard to use bitcoin or what am I going to use at the store less of actually bitcoin has a store of value especially from some of the retail clients coming into this space.

So regarding bitcoin and Complete Intelligence, are you guys forecasting anything in the digital currency space? Are you forecasting the currencies themselves maybe the mining profitability or any of the mining machines and can you speak a little bit further on that?

 

TN: We do. We started forecasting limited cryptos about six months ago and as I’m sure you can imagine there’s been a lot of volatility in cryptocurrencies over the last couple years. And because we’re a machine learning platform, it takes a while for the machines to understand how cryptocurrencies trade and move and so just because we started forecasting cryptocurrencies doesn’t necessarily mean that we would recommend people making trades or taking positions based upon what we forecast. You know, it’s different for things like, I don’t know, copper or whatever that we’ve been doing for a long time and those are also relatively stable markets say industrial metals, you know, that sort of thing. But cryptocurrencies very volatile, very new, and the market is still learning how to value them.

 

This is one of the key things about cryptocurrencies that I think is misunderstood is the market is still learning how to value them. That’s not a comment on whether I think they’re undervalued or overvalued right now. I just think the market isn’t really sure how to value them. And so, you know, in our platform we expect it to take really another couple months before we’re confident in where our platform is saying cryptocurrencies will go again because it’s such a complicated asset in the way it moves and because there’s so little institutional and historical knowledge about it. We have to iterate it, you know, a couple billion more times for us to really understand where it’s going.

 

JB: Are you seeing a lack of data or trading data, network data in making these decisions that making it harder than traditional markets or have you seen that the data in the bitcoin space is relatively open and well established?

 

TN: I don’t really see an issue with data. I think part of the problem with cryptocurrencies is that it doesn’t really trade on fundamentals. So what we’re utilizing is a configuration of methodologies that balance out fundamentals and technicals. You know, some months, certain assets lean more toward technicals. Some months, they lean more toward fundamentals.

 

Cryptocurrencies don’t really have fundamentals to lean on and so then you’re looking at a lot of relatively short-term and ultra-short-term approaches to understand the value of something. So the memory of the price, it’s either sticky or it’s not and I know that sounds a little bit silly but you know cryptocurrencies move in bursts or they languish. There’s really not a lot of in between and so understanding which technical approaches to take and within what configurations to take them is what’s really kind of confounding our platform right now and I would say our error rates for cryptocurrency is probably I think three times what our average error rate is.

 

So our average error rates for across our assets on an absolute percentage basis is between five and seven percent something like that. Across currencies, commodities, equities. For cryptos, we’re looking at probably a 15 ish to 20 percent error and so it might be a little bit lower than that now. But it’s settling within the range that we’re comfortable with. We’re really comfortable when things are say less than 10 percent error and we expect to be there, you know, very soon. But part of what’s different about what we’re doing is that we’re not afraid to talk about our error rates. We’ll be very transparent with people about what our current and historical error rates are and have been because our clients are making decisions based upon the data that we bring to them and the forecast that we bring to them.

 

So when I say to you, look our, you know, our error rates for cryptocurrencies is between 15 and 20 percent, I’m not really sure you can find many other people who would admit that publicly. But if traders are making decisions based upon the forecasts that we bring to market, then they need to know that, right? They need to know how to hedge against that error range.

 

JB: And so you’re referring to that the cryptocurrencies are much harder to predict. Is that keeping any of your current clients from moving over to the digital currency space? Are they looking at this space for growth opportunities or for potential revenue generating opportunities or even a way to hedge from the current macro environment?

 

TN: I think everyone is either involved and trading let’s say even at a small level or they’re very committed. I think the approach that we’ve tried to take, the number of firms that get very hypey about cryptocurrencies and almost feel like they’re trying to push it on to their clients. We’re not that way. We don’t care if someone invests in iron ore or investing cryptocurrencies. It’s really what is their profile and you know how well can we forecast it. But I think the interest in cryptocurrencies obviously is still very high because nobody really knows what’s happening there.

 

Nobody really knows what the future is there and nobody really wants to miss out. Actually, I know maybe two or three people who want to miss out on that and do and already at all but very few people want to miss out on it and so they’re keeping an eye on it or dipping a toe in if they’re not already in in a big way. And I think you know you have to be fair on these sorts of things you know. It’s not as if say the main cryptocurrencies have have kind of fizzled out. They’re still around. They didn’t fizzle out after say two years. They’re still around. People still trade them. You’re still trying to you know we’re still trying to figure out how to get them into some sort of monetary system or some sort of transmission mechanism. And until that’s figured out, I think that you know unless they fizzle out you know the main ones I think it’s still necessary to stay involved. So we’re not seeing a massive demand for what we’re doing in terms of forecasting and when I say forecasting I’m not talking about the next say five to seven days. I’m talking about the next 12 months, okay. Monthly intervals over the next 12 months.

 

So for something like cryptocurrencies that have a relatively short-term horizon because it has been pretty speculative from an investment perspective. It’s been pretty hard to to look at this stuff over a longer term. But we’re getting better at it and I think as these things become more predictive, there will be a lot more interest and that’s largely the market coming to agreement on what the various cryptocurrencies are actually worth.

 

JB: And following up on that you know, how do you value them this being a common trend it seems like in the analysis that you guys are doing as a large bitcoin miner in this space, we believe the stock to flow ratio is a huge component of giving value to underlying cryptocurrency and so that is when the when you know the having occurs did your models take that into account or did they do they how do they kind of work with that event?
Because I think the having is an event where you don’t really have that in any other industry where you’re losing half of your new coins coming in or half a new supply coming in on a daily basis.

 

TN: Well I think you you know, what you. You do see this a bit with say central bank money supply, you know that sort of thing. So and you do see, let’s say with the Dollar or the Euro, the Japanese Yen or something like that. You do see central bank money supply coming in and the pickup of that money supply is not fundamentally dissimilar from cryptocurrencies. Although I think with cryptocurrencies, it’s a it’s a fair bit more technical. But I think it’s you know understanding both the stock and the flow is critical to understanding where that value is. If there’s too much stock, then, you know, it’s obviously not valuable unless there’s the demand, the flow going into demand.

 

So yeah. I think it’s… But until people can have a normalized discussion around where it’s similar to say central banks, then I think it’s really hard for people to contextualize within their kind of trading and valuation framework. So look. You know, if you look for example, you know, the Chinese government introduced this coin into Shenzhen a few weeks ago, right. They effectively gave people the equivalent of thirty dollars in this Chinese crypto currency to spend and then it was gone. So they’re calling that a study on how widespread adoption of cryptocurrencies will work and I’m sure it was gone within a day, right. I mean if I’m given 30 bucks to spend for free then I’m going to spend it probably today.

 

So you know, I think until we have a better baseline for widespread adoption and I think the government endorsement on some level kind of matters because let’s look at that thirty dollar. It’s effectively like a voucher or a gift card, right, that they’ve given people. They gave people a thirty dollar gift card for free. It doesn’t matter what currency it’s in. Okay. It’s gonna get spent, right. I don’t necessarily think that that’s a valid test of the adoption of a cryptocurrency.

 

I think you have to have something more widespread and more enduring because there you have a fixed amount of stock that’s spent over a very abbreviated period. Doesn’t really mean anything, right. But I think until we have a wider spread adoption for spend, we’re not necessarily going to get a fundamental based value, okay. We’ll get that technically based value, meaning looking at the stocks and the flows and trying to understand based on stocks and flows but not necessarily based on the inherent value that you get with a legit currency. Not that cryptocurrency is illegitimate. That was probably a bad word choice but let’s say a central bank endorsed currency, we’ll say that much.

 

JB: And on the central bank, endorsed currency kind of chain of thought, when you see the United States and Europe and also China adopting these different types of cryptocurrencies or I guess you could say ways to distribute capital to individuals for stimulus. How are you seeing China and the US and any other major players kind of deploying these central bank currencies over the next two or three years? As you did mention, you know China is already doing it. In the US, I’m not aware of us doing any type of central bank currencies or deploying central bank currencies to citizens. But are you seeing… I guess, how do you see that playing out over the next two or three years, if not and maybe longer?

 

TN: Sure. So China, the China central bank did a first test of a cryptocurrency I think in January of 2017.

 

JB: Oh wow.

 

TN: So they’ve been trying to figure this out for some time and I think china sees it as a potential way to rival the US Dollar. The problem is, there is no trust in the the People’s Bank of China. Nobody outside of China really trusts it, okay. So the immutable aspect of a cryptocurrency doesn’t have validity outside of probably the walls of the center of the People’s Bank of China building. And without that, kind of limited supply, without the immutability of it, then again, it’s just a gift card. It’s just a voucher. Now I think the PBOC, the Chinese central bank has had but with each day it’s kind of passing I think they’ve had an opportunity to utilize cryptocurrencies for things like trade finance which is a really opaque aspect of international finance related to trade. And if they had, let’s say gone to some of their trade partners and said look in Europe or the Middle east or somewhere, you know, we can get around using the US Dollar by utilizing this digital, you know, Chinese yen or something.

 

I think there was a time when people would have been open to it especially if it made payments faster and less costly. But I think that window has passed at least for now. I think it’s really hard for China to insert itself. I think if they had done this say in 2015-16, I think they would have had a real opportunity and they could have done a lot to displace some US Dollar denominated trade finance and probably displace a lot of Euro denominated trade finance. But they didn’t do it. They’ll keep trying.

 

I’m not sure how successful they’ll be outside of those places that have to trade with them meaning North Korea, Iran and and those sorts of economies Venezuela and so on. With Europe and the US, I don’t think the central bankers fully understand what a cryptocurrency is and I don’t think that they really have say the patience to understand how to say deploy it in a credible way, if that makes sense. And so, I think you’ll almost have these parallel currency regimes with cryptocurrencies.

 

The problem though is, I don’t necessarily, at least for the next few years, see them displacing a currency like the Dollar. They may displace say secondary or tertiary currencies within say international trade, trade finance, cross-border payments, these sorts of things, and even domestic payments where say a central bank doesn’t really have credibility that makes a lot of sense but I’m not necessarily sure that I see it displacing say US Dollar or Euro transactions let’s say in kind of main say kind of day-to-day activities.

 

If you look at a government like Venezuela or Turkey or something like that where you see a real currency crisis, I think it’s possible. I’m not necessarily saying it’s probable at a place like Turkey but I think it’s possible that you could see adoption of something like cryptocurrency especially if the government puts a a restriction on US Dollar use.

 

JB: Tony, do you see… I mean it seems like you’re saying that the western, you know, China will have its own central bank digital currency and maybe the United States will try to deploy theirs as well. Do you think this is going to move the global economy into being a more closed system or do you think this will actually open up finance and trade and make it you know better for everyone? Or do you think we’ll end up having this almost finance war. We already do have that but like on the digital currency level now where it’s traceable and trackable by a single entity and the capital or the cost to deploy these systems is much lower.

 

TN: It’s a great question. I think the people who accept the digital Chinese Yuan are going to have to decide if they want a centralized authority in China, tracking all of their activities in that digital CNY, you know. I think that’s a real decision and a real trade-off that those people who trade in that currency are going to have to figure out.

 

Although dollars are traceable, you know you can kind of transmit them and other currencies. You can kind of transmit them, I wouldn’t really say in an anonymous way but you can kind of get around tracking of every single transaction. But with cryptocurrencies, you know, the ledger tracks everything. And so if you have say the PBOC in China tracking every single transaction for every single digital CNY, that’s out there.

 

That’s kind of next level of information out there, right it’s not just Google understanding what’s in your email and it’s not just Alexa tracking what you’re saying. It’s every single Penny you put out there being tracked by a central ledger.

 

JB: And I think you said that perfectly you know China will be tracking every transaction and that will help these Central Bank digital currencies. If it’s China, if it’s the U.S. if it’s you know somewhere in Europe and as these different currencies are deployed.

 

They’ll really be able to build almost a very well put together social graph of who you’re paying. I mean it’s very similar to Venmo. When Venmo had the kind of privacy era, when you could see every transaction. If you had your transaction on public that you sent all your friends, right?

 

This is almost like that but the Central Bank can see that for every single person. Now we know who interacts with who, where you go, you know if you’re going to get coffee at Starbucks every morning. Where you’re going to be you know it’s very interesting to see the amount of power that you know these Central Banks in my opinion are going to start are going to gain over deploying a currency. Where it’s traceable trackable and it’s on a single ledger.

 

TN: Right, well also imagine, you know right now we have macroeconomic data releases like gross domestic product or industrial production or retail sales, those sorts of things. Imagine you know right now the way that happens is a statistics ministry does an estimate of what that economic activity is and they release it like a month after it actually happens. And then they revise it four times before they finally give up and say that this macroeconomic variable is finished.

 

If you do have a centralized kind of ledger for this stuff, you can actually look at national and global economic activity on a real-time basis, right? So you could actually see through Covid. You could see the U.S. economy declining on a real-time basis or the Europe economy declining on a real-time basis which would be pretty scary actually but that’s the reality of it. If you have this centralized ledger you can see let’s say, the velocity of that currency grinding to a halt as people don’t spend money which from a Central Bank perspective can help you understand how to incentivize people to spend money if they have it.

 

So from a kind of centralized monitoring of the economy perspective. I could see that being beneficial from a consumer and an individual saver. Spender perspective, I can see that being a little bit scary.

 

JB: It is a little bit scary but I agree with you also with the Covid situation. You know, the stimulus, really in my opinion didn’t get to the people as well as it should have. And Central Bank digital currencies will allow the these Central Banks to give stimulus to those who are most affected, at least in theory. And to be able to provide you know potentially different access to credit for different types of individuals we’re taking different types of risk being business owners or just employees. But on the Covid kind of analysis and as you guys with CI were we’re doing the analysis on the equity markets and in oil. And different types of currencies. Did you guys see any indicators you know as Covid was picking up in the analysis of the market. And how did it affect your predictions in these you know kind of broadly over the different markets that you guys predict and watch.

 

TN: I think what we saw in the wake of Covid was, and this is no surprise to anybody I don’t think is. A move to very short-term thinking you know, what data points are coming out. What’s moving. What are people doing let’s track to day what’s actually happening. Also an eye on kind of what is the government doing. What stimulus is coming out. When is it coming out. How much is it. Where is it going that sort of thing.

 

So I think for the probably three to four months I would say until July or August, a lot of trading and forecasting was really done on that basis kind of the news moved the market. It was fear and news that really moved markets and we had to come to a place where the size of the dump truck of stimulus was bigger than the fear that people had of Covid. And when we got to a number big enough you started to see markets break higher. Which was I guess a positive thing for people who weren’t working but getting stimulus from government so they could kind of day trade and make some money in markets to shore up some of their bills.

 

Now that the stimulus has gone out and now that we see at least some markets coming back to I wouldn’t say normal but at least to a significant level. We’re starting to see or we’ve started to see over the past, say six to ten weeks, more fundamental basis put into markets and put into some of those those value decisions whether it’s in equity or whether it’s a commodity or something. It’s still playing out in a number of ways a lot of the texts still very sentiment and stimulus based.

 

We see things like you know some of the commodities that are still very much based on that or I would say kind of more than 50 based on that but we’re starting to see markets move back into a direction that’s a bit more traditionally based and I use that term very loosely traditionally based but with at least a bit of fundamental analysis. But you know look at something like Tesla for example the price to earnings ratio is around 1100, I think something like that. It’s just I mean you may love Tesla but that’s a pretty healthy multiple, right? So you know at some point and I’m not necessarily predicting Tesla will fall to earth but at some point something will catch up with the valuations of these things.

 

Whether they’re commodities or whether they’re equities and will start to value things on a more traditional again. That’s a loose application there but on a more traditional basis.

 

TN: One of the things that I’ve been noticing in just conversations is it seems like you know the stock market is almost I would say really turning into a casino. Where you have people just buying stocks they heard on the news. They’re getting the motley fool every week and they have so many decisions to make. So many different options and I’ve noticed that it seems to be just too complex for I would say normal retail robinhood traders. They get overwhelmed with so many decisions. I think one of the nice things you know about value as we talked about valuing crypto. Is at least with Bitcoin you know what you’re getting. You know that this is an asset with a stable monetary supply with a stable issuance rate over the next 100 years.

 

What are your thoughts on how bitcoin mining? I’m actually gonna change it up and move to a separate topic a different topic but what are your thoughts on Bitcoin mining and how it relies on as on the global supply chain starts in semiconductor factories in China and you mentioned the supply chain optimization a lot on your website as a function of Complete Intelligence. Can you walk through a little bit how you guys optimize supply chain and then I’d love to talk with you through potentially how the Bitcoin mining supply chain works on our end and see where you know optimizations are and and how Covid or any of these other things impact supply chains and what you guys are seeing on a worldwide basis?

 

TN: Sure, that’s great, I think with any supply chain you have really three factors. You have cost, you have distance, and you have time, okay? And so I mean there’s quality as well but if you assume that you can get equal quality in you know in multiple locations. You have cost, distance and time. And so we help people initially with costs, okay? We’re helping them to kind of arbitrage the best cost locations.

 

We have a client who manufactures confectionary that makes candies and sweets. And they buy sugar, I think at eight different places around the world and so we help them understand where the sugar price is because there’s not a single global sugar price, right? There are local factors so we we help them understand where sugar prices will change and at what magnitude they change.

 

So that their factories can be prepared and that they can have the right margin they need so that they can take in the right inventory. So that they can make the right transactions at the right time. So I think from a pure cost basis with commodities for example like sugar, it’s possible to do that. When you look at something like semiconductors with a very sophisticated manufacturing process.

 

Cost is probably not the only, well I can assure it’s not the only factor associated with the decision. So then you start looking at things like time and you look at things like distance and so when we go back to say March, April, May, a lot of semiconductors travel by air and we had air freight rates from Asia to the U.S. that were normally say a dollar fifty a kilogram. That had in many cases been jacked up to say 15 dollars a kilogram. So, 10 times or more of the normal price. So that’s where distance becomes or let’s say cost becomes a function of distance, right? And so that’s that chipset that semiconductor may cost the same x factory but getting it to the destination is increasingly critical and increasingly costly.

 

So, that’s where we help people also to understand what the cost of that distance is and what the cost of that time is because you could put it on a vessel and you could ship it and it could take three weeks to get where it needs to go. But in many cases the cost of those the finished goods are high enough that you can absorb some of that transport cost. Okay? So there are a number of ways that we help people understand those transactions but at the end of the day it all has to do with the cost of that bill of material, meaning the cost of the goods that go into that finished item that’s ultimately sold to a customer.

 

So when we look at semiconductors for example and you look at what has happened over the last, particularly last year and if you look at say TSMC Taiwan semiconductor. Moving one of their locations to I think it’s Arizona in the U.S. We’re starting to get more of that high value supply chain in the U.S. more as a function to de-risk supply chains in the wake of Covid meaning, factories in China closed during Covid people still had to make stuff and they had to still have their business open but they couldn’t because the factories in China were closed.

 

Once the factories in China opened. There was constrained transport capacity so it would cost them a lot more so they had goods that were late and they had goods that were a lot more expensive than normal. And so I think what a lot of manufacturers have done especially in the wake of Covid and said, look we need to diversify our supply chains and have multiple sources for some of these high-value goods and we Complete Intelligence have been talking about regionalization of trade since 2017. We wrote about it more formally in say starting Feb of 18 when the steel and aluminum tariffs were put on by the current administration but we’ve believed for years that we would start to see a re-regionalization of trade and that cuts out some of the risk associated with supply chains and some of those costs. Maybe, transport costs that may be lower are offset by maybe marginally higher say labor or taxes or something like that either in the U.S. or Mexico or something.

 

So one of the things that many people don’t necessarily understand is when China came into the WTO in 2000 the U.S. was in the first decade of the NAFTA agreement North American Free Trade Agreement at the time there were a lot of manufactured there was a lot of manufacturing for the U.S. done in Mexico. Part of the reason a lot of factories moved to China was because electricity in Mexico was really really expensive at the time, okay? And the electricity in China was really cheap. So a lot of these manufacturing especially energy intensive manufacturing firms moved to China to save on their electricity. Which was a large fun factor within their total cost. So what’s happened in Mexico over the last… I think four years is laws were passed to deregulate the electricity market in Mexico. So now you have power in Mexico that’s a lot cheaper than it was 15, 20 years ago. So the attractiveness of Mexico as a location at least from a cost basis is quite a bit higher than it was in the past and especially quite a bit higher than it was when firms were leaving Mexico to go to China.

 

JB: So Tony you mentioned the impact of of Covid on these supply chains and I want to talk a little bit about something that we have in in Bitcoin mining called the supply gap. And it basically what that is when the price of Bitcoin is is skyrocketing and is hitting an all-time high, like it did back in 2017. The underlying you know value of these Bitcoin miners really relies on the profitability of those machines and that is heavily relies on the price of of Bitcoin.

 

So what we see is that you know these supply chains they they shrivel up, almost. They you know there’s being able to order machines over a three-month period it ends up going out to six months. You won’t be able to get machines and you know until six months later. Do you see this sent not centralization but going from globalization back to Mexico. Back to these localized economies. Do you see that helping these kind of massive supply fluctuations or kind of I guess events that occur specifically you know with Bitcoin price and Bitcoin miners but I guess also globally with events like code that really do shock the system we know of today.

 

TN: Yeah, I do. I think that of course you know we’re going to have some difficulties in the early days of it. We’re going to have some awkward moments where things don’t work as people plan, that sort of thing. Whenever you have a large systemic change you always have some moments that are a little bit embarrassing and cause you to second-guess the decision. We’re going to have those that’s normal but I think over time. What we’re building is a more robust global supply chain you know. Something like 40 of all manufactured goods are made in Northeast Asia, China, Korea, Japan and as we have re-regionalization of manufacturing and that’s to North America, that’s to Europe and so on. We have a diversity of manufacturing locations and so if there is let’s say Covid in China or in Asia but it hasn’t hit the U.S. yet then you know it’s possible to use additional capacity in say U.S. or European factories to help meet the needs of Bitcoin miners, right? Depending on what we’re doing. Depending on the sophistication of those factories and the capacity of those factories but I believe that as we have regionalization of supply chains you have much more robustness in those supply chains.

 

I also think that in the wake of Covid… so I lived in Asia for 15 years. I just moved back to the U.S. in 2017. I lived through probably five or six pandemics in that time and so we got a little bit used to it. In the U.S. it’s relatively new and I think people here trying to figure out how to contend with it and kind of the calibration of risk in the U.S. to pandemics is it’s new. So people aren’t really sure what it means or doesn’t mean. So the global transmission of viruses is not something that’s really going away. So will we have more code like viruses coming out of Asia or coming out of Europe or the U.S. It’s likely and so we’re at a point where we have to have regionalization of supply chains.

 

So first we have robust supply chains where we can source from the U.S., Europe, Asia wherever we want as capacity as demand and as costs require but also we have the flexibility if there is one of those events whether it’s a disease event or whether it’s you know let’s say a war or something like that. We have the flexibility to make stuff in other parts of the world too. So if there was a devastating conflict in Northeast Asia today. Global supply chains would be paralyzed that’s just a fact and so the sooner we can get regionalized supply chains the better, we’re all off because the risk of a let’s say a conflict in Northern Asia, if it ever happens, it won’t impact everyone on the planet as much as it would.

 

JB: We definitely, I agree are seeing that de-risking and a big huge news with a semiconductor in TSMC moving to potentially the United States to build a facility you know hopefully reducing on that that distance for Bitcoin miners specifically. I found it very interesting that you mentioned about Mexico and the electricity prices there. To understanding that those manufacturers actually had to leave Mexico and went to China because it was too you know too expensive to extract or to complete that manufacturing process. I view Bitcoin mining as a way to almost extracting you know Bitcoin from the network through a manufacturing process where we’re using these Bitcoin miners and large amounts of energy to do just that.

 

So I wanted to talk farther about how you’ve worked with clients in either the natural gas or the energy sectors in the United States specifically and pricing out those markets and where do you see the future of this industry going the electricity market specifically and the cost of power in the United States?

 

TN: Sure, so I’m in Texas the cost of natural gas is very low and the abundance of natural gas is very high. So electricity prices to be honest is not really something we worry about here. I know in other parts of the country and other parts of the world it is a worry you know, electricity is something that has kind of always been very regional and it has been always been very feedstock specific if you’re burning oil to make electricity or coal or nuclear or whatever and you really have to look at that blended cost, right? but in Texas we’re looking at a lot of natural gas to fuel our electricity. So not that much of a worry for us and and in this region it’s not that much of a worry.
I think in places like Europe where they’re net gas importers, I think it’s more of a worry and there’s always a lot of discussion around importing gas from say Russia or from the Middle East or from the U.S. I think they have an abundance of choice there but it’s relatively more expensive there than it is say here in the U.S.

 

I think in Asia you have a lot of imports from the Middle East particularly places like Qatar, these sorts of things for natural gas. China uses a lot of coal something like 70 plus percent of their power generation is from coal and it’s really hard to um to wean themselves off of that. Japan is a very large LNG and natural gas importer because they shut off their nuclear power after the incidents in 2010 or 2012 sorry with the reactors the Fukushima reactors. So you know it really all depends on the local power generation capacity in feedstocks. But I think generally you know we’re not necessarily seeing a world where hydrocarbons become all that expensive for quite some time. When we look at what Covid did to demand the demand destruction that Covid brought about is is pretty shocking that applies to industries and that applies to consumers so we don’t see say oil prices or natural gas prices hitting let’s say the highs of 2008 for quite some time. And you know since they are relatively global commodities although there are differences in certain aspects of them it also pushes down the prices, let’s say in other parts of the world say the middle east and so on and so forth. So we don’t see electricity prices outside of say regulatory impacts or things like fixed investment requirements.

 

So let’s say there’s a regulatory requirement that a power station can only be say 20 years old you know that’s a significant cost that would add to electricity prices but other than that it seems to us that the feedstocks, although we don’t necessarily expect to see kind of negative 37 oil like we saw in April. We don’t necessarily see energy price inflation coming anytime in the next say 24 months. And if you look at things like gasoline I know this isn’t electricity but things like gasoline prices are down say 30 percent from where they were a year or so ago. And they’re expected to remain that low at least for the next six to 12 months. So it’s not just electricity it’s also gasoline or petrol as well where because of muted demand prices will remain relatively low.

 

JB: I think that’s that’s great news for for miners in the in the United States and you know I really cross the world as more and more energy generation comes online. We’re seeing that that cost to produce coins is continuing to get cheaper and which allows miners here in the U.S. to compete if not beat miners in China on the cost per kilowatt hour. Tony, was there any other trends that you guys are focusing on right now in regards in to your investment portfolio analysis that you wanted to highlight on the show today?

 

TN: JP, I think there are hundreds of trends we’re following but I think we’ve cut most of the main ones. I think really it’s you know understanding risk of any asset that we follow or our clients follow is really really important. Whether it’s cryptocurrencies or whether it’s oil and gas or whether it’s you know I don’t know the SP500. Understanding the risk there is really critical we’re always trying to figure out how to balance the risk and opportunity associated with the assets that we forecast and that’s I would say for any of your listeners that’s the really critical part to understand. So you know we could pursue this down any avenue and I’m sure we could talk for another hour on you know on just about any asset. So I really appreciated the time today it’s been a fantastic discussion, thank you very much.

 

JB: Yes, thank you Tony it was great to have you on. I want to offer you the opportunity to join you have any questions that you want to ask me about Bitcoin specifically that you want the audience to make sure they hear, anything that’s on your mind?

 

TN: You know, I guess what I am curious about Bitcoin is you know we saw a bump in 2017. I think largely driven by broad awareness or a more broad awareness of the opportunities in Bitcoin. What will drive the next bump in Bitcoin or crypto value? What do you see driving that next rise let’s say 30 to 40 to 50 rise in the value of of cryptocurrencies?

 

JB: So the way I view the cryptocurrency market and really Bitcoin specifically is I’m all about as the stock to flow ratio and how that bitcoin is created. So when that having event occurs I got into cryptocurrency back in 2013. So I’ve been through two of these having events now and when that have even occurred in 2016 we see that it kicks off like a real almost momentum. Moving into the space where the cost of creating these new coins is exponentially higher, makes it so that all these older machines have to come offline and it really does a disservice or really degrades the value of these mining machines it makes the profitability got cut in half. And so when that happens I think that there are these the lack of coins new coins coming into the system, creates the momentum which is needed to push the price up to those 2017 highs you were talking about or potentially you know 2021, 2022 highs, simply saying it doesn’t happen instantly because it does take a while to get there but I expect that to you know to happen in the next coming years. Not necessarily because of one event but simply because of the schedule of new coins coming out of the market.

 

TN: So sorry if I understood you correctly are you also saying that the age of the infrastructure that the miners are working on has an impact on the so the replacement cost of that infrastructure also puts upward pressure on the price of bitcoin?

 

JB: I would say that exactly so the fact that we have to replace machines that have less efficiency. So the joules per tera hash or how well they can turn one watt of energy into one terra hash of mining power is needs to be upgraded by 50 so if you have a machine that was running 100 joules per terahash like the s9 that machine is no longer and it was just barely making money that machine is no longer going to be even anywhere close to profitable because of this having event, you know now, you would need to go upgrade all of your machines so they run at the 50 joules per tera hash level or you need to find half the cost of electricity and that is very hard to do especially because these facilities are massive with hundreds of megawatts of power.

 

So that’s what I drive as the underlying driver to this Bitcoin price push that we see every four years if you look back on the chart it happens every four years. Simply because the miners place such they’re one of the biggest components of the ecosystem there’s about five billion dollars in mining rewards today every year and that’s a huge driver in a relatively small market where Bitcoin is currently sitting.

 

TN: Interesting, so that that replacement cycle like you said it’s and this is a question it’s not a statement that’s that’s about every four years give or take.

 

JB: Every four years give or take either have to replace your equipment with newer machines which now you’re waiting in line because you know everyone else in the whole bitcoin network has to do that or you’re moving to power where it’s half as expensive but all miners are always searching for the cheapest power so that’s something that’s always occurring.

 

TN: Okay, so with the kind of the supply chain hiccups that we saw with Covid does that push that replacement cycle back like are is that replacement cycle being pushed back by six to nine months so or is that do we have a pent-up kind of inflation meaning. Do you believe that the value of bitcoin being driven up will last for longer because of the supply chain issues we saw in Covid?

 

JB: So with this definitely the supply chain issues in Covid it affected our shipping rates as you mentioned those increased dramatically it affected how fast machines could get out it actually caused bitmain and some of the other major manufacturers to delay their shipping by two or three months. So if you were to buy a batch to be delivered in November it still hasn’t been delivered.

 

So there is that that pushback and we’ve seen that greatly affect the market regarding the deployment of these machines and kind of scaling with the recent bitcoin price-wise guys new machines are very hard to get. I would say about maybe 10,000 to 15,000 new machines per month are coming to the U.S. And that might be even on the higher range that’s about 50 megawatts of power per month coming to the U.S. and coming out of these factories. Which is is only 50 million dollars worth of capital. So we have huge constraints on the semiconductor themselves and being making those mining machines and when the price of bitcoin even jumps up like it has over the past couple of days up to the 13,000 mark that’s going to create even more external pressure even more interest in mining which makes it even harder to get those machines and will push out the timeline even farther.
So yes it’s a huge issue when it comes to supply chain management because of Covid and the Bitcoin price increasing investors appetite to get exposure the space.

 

TN: Fantastic that’s really interesting. Thanks for that.

 

JB: Of course Tony, well thank you for coming on. I appreciate it and I’m glad we’re able to have you on. Thanks again Tony.

 

TN: Thank you, hope to speak soon. Have a great day. Thanks JP, bye-bye.

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Podcasts

Stories from the Cloud: The Forecast Calls For…

Tony Nash joins veteran journalists Michael Hickins and Barbara Darrow at the Stories from the Cloud podcast to talk about the forecast calls for businesses, and how AI and machine learning can help in predicting the futures in budget forecasting. How does his company Complete Intelligence dramatically improve forecast accuracy of companies suffering from a huge 30% error rate. He also explained the AI technology behind the CI solutions and strategic toolkit, and how this practically applies to global companies. How can they benefit from this new technology to better be prepared in their budget planning and reducing risks in costs?

 

Stories from the Cloud description:

It’s not easy to predict the future. But when it comes to business and cash or financial forecasting tool or software, the right data and the right models are better than any crystal ball.

 

Tony Nash, CEO and founder of Complete Intelligence, explains how AI and the cloud are giving companies better cash forecasting software tools to see into their financial futures.

 

About Stories from the Cloud: Enterprises worldwide are turning to the cloud to help them thrive in an ever-more-competitive environment. In this podcast, veteran journalists Michael Hickins and Barbara Darrow chat with the people behind this massive digital transformation and the effects it has on their work and lives.

 

Show Notes

 

SFC: Hey, everybody, welcome back to Stories from the Cloud sponsored by Oracle. This week, I am here, as always, with Michael Hickins, formerly of The Wall Street Journal. I am Barbara Darrow. And our special guest today is Tony Nash. He’s the founder and CEO of Complete Intelligence. And this is a very interesting company. Tony, thanks for joining us. And can you just tell us a little bit about what the problem is that Complete Intelligence is attacking and who are your typical customers?

 

Tony: Sure. The problem we’re attacking is just really bad forecasting, really bad budget setting, really bad expectation setting within an enterprise environment. Companies have packed away data for the last 15, 20 years, but they’re not really using it effectively. We help people get very precise, very accurate views on costs and revenues over the next 12 to twenty four months so they can plan more precisely and tactically.

 

SFC: It sounds like a big part of the mission here is to clean up… Everybody talks about how great data is and how valuable it is. But I mean, it sounds like there’s a big problem with a lot of people’s data. And I’m wondering if you could give us an example of a company, let’s just say a car maker and what you can help them do in terms of tracking their past costs and forecasting their future costs.

 

Tony: So a lot of the problem that we see, let’s say, with the big auto manufacturer, is they have long-term supply relationships where prices are set, or they’ve had the same vendor for X number of years and they really don’t know if they’re getting a market cost, or they don’t have visibility into what are those upstream costs from that vendor. And so, we take data directly from their ERP system or their supply chain system or e-procurement system and we come up with very specific cost outlooks.

 

We do the same on the sales revenue side. But say for an automaker, a very specific cost outlook for the components and the elements that make up specific products. So we’ll do a bill of material level forecast for people so that they can understand where the cost for that specific product is going.

 

Before I started Complete Intelligence, I ran research for a company called The Economist and I ran Asia consulting for a company called IHS Markit. And my clients would come to me and say, there are two issues at both companies. Two issues. First is the business and financial forecasting tool or even strategic toolkit that people buy off the shelf has a high error rate. The second issue is the forecasts don’t have the level of context and specificity needed for people to actually make decisions. So what do you get? You get very generic data with imprecise forecasts coming in and then you get people building spreadsheets and exclusive models or specific models within even different departments and teams and everything within a company.

 

So there are very inconsistent ways of looking at the world. And so we provide people with a very consistent way and a very low error way of looking at the future trajectory of those costs and of those revenues.

 

SFC: So I’m curious, what is the what is the psychology of better business forecasting software? So on your customers and I’m thinking, if I’m a consumer, so this is maybe not a good analogy, but if I’m a consumer and I look at the actual costs are of a phone that I may have in my pocket, I may think, jeez, why making a thousand dollars for this? But then part of me says, such things mark up and well, I guess so. There are uncertainties in financial projections. So on me, I mean, I don’t need a financial projection software to tell me that the components of the pocket computer have don’t add up to what I paid for them. But I kind of understand that there needs to be money made along the way. I just I want it. Right. How does that translate on a B2B perspective? What are the people’s attitude about price and how do they react to the data that, as you said, I mean, heretofore, it’s kind of been unreliable.And all of a sudden, I think you say a lot of procurement projections have been around 30 percent, which is huge. Right. So how does that happen and how do people react to something that seems more trustworthy?

 

Tony: Well, I think that expectations depend on the level within a manufacturing company that you’re talking to. I think the more senior level somebody is, of course, they want predictability and quality within their supply chain, but they’re also responsible to investors and clients for both quality and cost. And so at a senior level, they would love to be able to take a very data driven approach to what’s going on. The lower you get within a manufacturing organization, this is where some of the softer factors start to come in. It’s also where a lot of the questionable models are put in as well.

 

Very few companies that we talk to actually monitor their internal error rates for their cost and revenue outlooks. So they’ll have a cost business forecasting software model or a revenue forecasting model that they rely on because they’ve used it for a long period of time, but they rarely, if ever, go back and look at the error rates that that model puts out. Because what’s happening is they’re manually adjusting data along the way. They’re not really looking at the model output except for that one time of the year that they’re doing their budget.

 

So there really isn’t accountability for the fairly rudimentary models that manufacturing companies are using today. What we do is we tell on ourselves. We give our clients our error rates every month because we know that no no business forecasting software model is perfect. So we want our clients to know what the error rate is so that they can understand within their decision making processes.

 

SFC: And it’s kind like a margin of error in a political poll?

 

Tony: Yeah, we use what’s called MAPE – mean absolute percent error. Most error calculations. You can game the pluses and minuses. So let’s say you were 10 percent off, 10 percent over last month and 12 percent under this month. OK. If you average those out, that’s one percent error. But if you look at that on an absolute percent error basis, that’s 11 percent error. So we gauge our error on an absolute percent error basis because it doesn’t matter if you’re over under, it’s still error.

 

SFC: Still wrong, right.

 

Tony: Yeah. So we tell on ourselves, to our clients because we’re accountable. We need to model the behavior that we see that those senior executives have with their investors and with their customers, right? An investment banking analyst doesn’t really care that it was a plus and a minus. They just care that it was wrong. And they’re going to hold those shares, that company accountable and they’re going to punish them in public markets.

 

So we want to give those executives much better data to make decisions, more precise decisions with lower error rates so they can get their budgeting right, so they can have the right cash set aside to do their transactions through the year, so they can work with demand plans and put our costs against their say volume, demand plans, those sorts of things.

 

SFC: I have to just ask I mean, Michael alluded to this earlier, but I want to dive into a little more. You had said somewhere else that most companies procurement projections are off by 30 percent. That’s a lot. I mean, I know people aren’t… I mean, how is that even possible?

 

Tony: It’s not a number that we’ve come up with. So first, I need to be clear that that’s not a number that we’ve come up with and that’s not a number that’s published anywhere. That’s a number that we consistently get as feedback from clients and from companies that we’re pitching. So that 30 percent is not our number. It’s a number that we’re told on a regular basis.

 

SFC: When you start pitching a client, obviously there’s a there’s a period where they’re just sort of doing a proof of concept. How long does that typically last before they go? You know what? This is really accurate. This can really help. Let’s go ahead and put this into production.

 

Tony: Well, I think typically, when we when we hit the right person who’s involved in, let’s say, category management or they actually own a PNL or they’re senior on the FPNA side or they’re digital transformation, those guys tend to get it pretty quickly, actually. And they realize there’s really not stuff out there similar to what we’re doing. But for people who observe it, it probably takes three months. So our pilots typically last three months.

 

And after three months, people see side by side how we’re performing and they’re usually convinced, partly because of the specificity of projection data that we can bring to to the table. Whereas maybe within companies they’re doing a say, a higher level look at things. We’re doing a very much a bottom up assessment of where costs will go from a very technical perspective, the types of databases we’re using, they’re structured in a way that those costs add up.

 

And we forecast at the outermost leaf node of, say, a bill material. So uncertainties in financial projections are solved. A bill Of material may have five or 10 or 50 levels. ut we go out to the outermost kind of item within that material level, and then we add those up as the components and the items stack up within that material. Let’s say it’s a mobile phone, you’ll have a screen, you’ll have internal components. You’ll have the case on the outside. All of this stuff, all of those things are subcomponents of a bill of material for that mobile phone.

 

SFC: So I am assuming that there is a big role here in what you’re doing with artificial intelligence, machine learning. But before we ask what that role is, can you talk about what you mean by those terms? Because we get a lot of different definitions and also differentiations between the two. So maybe talk to the normals here.

 

Tony: OK, so I hear a number of people talk about A.I. and they assume that it’s this thinking machine that does everything on its own and doesn’t need any human interaction. That stuff doesn’t exist. That’s called artificial general intelligence. That does not exist today.

 

It was explained to me a few years ago, and this is probably a bit broader than most people are used to, but artificial intelligence from a very broad technical perspective includes everything from a basic mathematical function on upward. When we get into the machine learning aspect of it, that is automated calculations, let’s say, OK. So automated calculations that a machine recognizes patterns over time and builds awareness based on those previous patterns and implies them on future activities, current or future activity.

 

So when we talk about A.I., we’re talking about learning from previous behavior and we’re talking about zero, and this is a key thing to understand, we have zero human intervention in our process. OK, of course, people are involved in the initial programming, that sort of thing. OK, but let’s say we have a platinum forecast that goes into some component that we’re forecasting out for somebody. We’re we’re not looking at the output of that forecast and go, “Hmmm. That doesn’t really look right to me. So I need to fiddle with it a little bit to make sure that it that it kind of looks right to me.” We don’t do that.

 

We don’t have a room of people sitting in somewhere in the Midwest or South Asia or whatever who manually manipulate stuff at all — from the time we download data, validate data, look for anomalies, process, forecast, all that stuff, and then upload — that entire process for us is automated.

 

When I started the company, what I told the team was, I don’t want people changing the forecast output because if we do that, then when we sit and talk to a client and say, hey, we have a forecast model, but then we go in and change it manually, we’re effectively lying to our customers. We’re saying we have a model, but then we’re just changing it on our own.

 

We want true kind of fidelity to what we’re doing. If we tell people we have an automated process, if we tell people we have a model, we really want the output to be model output without people getting involved.

 

So we’ve had a number of unconventional calls that went pretty far against consensus that the machines brought out that we wouldn’t have necessarily put on our own. And to be very honest, some of them were a little bit embarrassing when we put them out, but they ended up being right.

 

In 2019, the US dollar, if you look at, say, January 2019, the US dollar was supposed to continue to depreciate through the rest of the year. This was the consensus view of every currency forecaster out there. And I was speaking on one of the global finance TV stations telling them about our dollar outlook.

 

And I said, “look, you know, our view is that the dollar will stabilize in April, appreciate in May and accelerate in June.” And a global currency strategist literally laughed at me during that interview and said there’s no way that’s going to happen. In fact, that’s exactly what happened. Just sticking with currencies, and for people in manufacturing, we said that the Chinese Yuan, the CNY, the Renminbi would break seven. And I’m sure your listeners don’t necessarily pay attention to currency markets, but would break seven in July of 19. And actually it did in early August. So that was a very big call, non consensus call that we got months and months ahead of time and it would consistently would bear out within our forecast iterations after that. So we do the same in say metals with things like copper or soy or on the ag side.

 

On a monthly basis, on our base platform, we’re forecasting about 800 different items so people can subscribe just to our data subscription. And if they want to look at ag, commodities, metals, precious metals, whatever it is, equities, currencies, we have that as a baseline package subscription we can look at, people can look at. And that’s where we gauge a lot of our error so that we can tell on ourselves and tell clients where we got things right and where we got things wrong.

 

SFC: You know, if I were a client, I would I would ask, like, OK, is that because you were right and everyone else is wrong? Is that because you had more data sources than anyone else, or is it because of your algorithm or is it maybe because of both?

 

Tony: Yes, that would be my answer. We have over 15 billion items in our core platform. We’re running hundreds of millions of calculations whenever we rerun our forecasts. We can rerun a forecast of the entire global economy, which is every economy, every global trade lane, 200 currency pairs, 120 commodities and so on and so forth. We can do that in about forty seven minutes.

 

If somebody comes to us and says, we want to run a simulation to understand what’s going to happen in the global economy, we can introduce that in and we do these hundreds of millions of calculations very, very quickly. And that is important for us, because if one of our manufacturing clients, let’s say, last September, I don’t know if you remember, there was an attack on a Saudi oil refinery, one of the largest refineries in the world, and crude prices spiked by 18 percent in one day.

 

There were a number of companies who wanted to understand the impact of that crude spike on their cost base. They could come into our platform. They could click, they could tell us that they wanted to rerun their cost basis. And within an hour or two, depending on the size of their catalog, we could rerun their entire cost base for their business.

 

SFC: By the way, how dare you imply that our listeners are not forex experts attuned to every slight movement, especially there’s no baseball season. What else are we supposed to do? I wanted to ask you: to what extent is the performance of the cloud that you use, you know, important to the speed with which you can provide people with answers?

 

Tony: It’s very important, actually. Not every cloud provider allows every kind of software to work on their cloud. When we look at Oracle Cloud, for example, having the ability to run Kubernetes is a big deal, having the ability to run different types of database software, these sorts of things are a big deal. And so not all of these tools have been available on all of these clouds all the time. So the performance of the cloud, but also the tools that are allowed on these clouds are very, very important for us as we select cloud providers, but also as we deploy on client cloud. We can deploy our, let’s say, our CostFlow solution or our RevenueFlow solution on client clouds for security reasons or whatever. So we can just spin up an instance there as needed. It’s very important that those cloud providers allow the financial forecasting tools that we need to spin up an instant so that those enterprise clients can have the functionality they need.

 

SFC: So now I’m the one who’s going to insult our readers or listeners rather. For those of us who are not fully conversant on why it’s important to allow Kubernetes. Could you elaborate a little bit about that?

 

Tony: Well, for us, it has a lot to do with the scale of data that’s necessary and the intensity of computation that we need. It’s a specific type of strategic toolkit that we need to just get our work done. And it’s widely accepted and it’s one of the tools that we’ve chosen to use. So, for example, if Oracle didn’t allow that software, which actually it is something that Oracle has worked very hard to get online and allow that software to work there. But it is it is just one of the many tools that we use. But it’s a critical tool for us.

 

SFC: With your specialization being around cost, what have you looked at… Is cost relevant to your business and so on cloud? How so?

 

Tony: Yeah, of course it is. For us, it’s the entry cost, but it’s also the running cost for a cloud solution. And so that’s critically important for us. And not all cloud providers are created equally. So so we have to be very, very mindful of that as we deploy on a cloud for our own internal reasons, but also deploy on a client’s cloud because we want to make sure that they’re getting the most cost effective service and the best performance. Obviously, cost is not the only factor. So we need to help them understand that cost performance tradeoff if we’re going to deploy on their cloud.

 

SFC: Do you see this happening across all industries or just ones where, you know, the sort of national security concerns or food concerns, things that are clearly important in the case of some kind of emergency?

 

Tony: I see it happening maybe not across all industries, but across a lot of industries. So the electronics supply chain, for example, there’s been a lot of movement toward Mexico. You know, in 2018, the US imported more televisions from Mexico than from China for the first time in 20 some years. So those electronics supply chains and the increasing sophistication of those supply chains are moving. So that’s not necessarily sensitive electronics for, say, the Pentagon. That’s just a TV. Right. So we’re seeing things like office equipment, other things. You know, if you look at the top ten goods that the US receives from China, four of them are things like furniture and chairs and these sorts of things which can actually be made in other cheaper locations like Bangladesh or Vietnam and so on. Six of them are directly competitive with Mexico. So PCs, telecom equipment, all these other things.

 

So, you know, I actually think that much of what the US imports will be regionalized. Not all of it, of course, and not immediately. But I think there’s a real drive to reduce supply chain risk coming from boards and Coming from executive teams. And so I think we’ll really start to see that gain momentum really kind of toward the end of 2020 and into early 2021.

 

SFC: That is super interesting. Thank you for joining us. We’re kind of up against time, but I want to thank Tony for being on. I want to do a special shout out to Oracle for startups that works with cool companies like Complete Intelligence. Thanks for joining us. Please try to find Stories from the Cloud at on iTunes or wherever you get your podcasts and tune in again. Thanks, everybody.

Categories
Podcasts

Fixing terrible forecasts and the lack of context

Tony Nash joined Geoffrey Cann in Digital Oil and and Gas podcast to talk about his revenue forecasting and predictive intelligence analytics startup company Complete Intelligence — how does the company solve the problem of terrible forecasts and the lack of context around data?

 

Geoffrey Cann joined us in QuickHit: 2 Things Oil & Gas Companies Need to Do Right Now to Win Post Pandemic.

 

This podcast originally appeared at https://digitaloilgas.libsyn.com/159-interview-with-tony-nash-of-complete-intelligence?utm_campaign=interview-with-tony-nash-of-complete-intelligence

 

Digital Oil and Gas Description

 

 

Jul 22, 2020

Today’s podcast is an interview with Tony Nash, CEO and founder of Complete Intelligence. Specializing in revenue forecasting and predictive analytics, Complete Intelligence develops artificial intelligence solutions. In this interview, we discuss predictive intelligence analysis, how Complete Intelligence works, and what value these forecasts can generate. 

 

Show Notes

GC: Welcome back to another episode of Digital Oil and Gas. My name is Geoffrey Cann, the host of the podcast. And I’m joined today by Tony Nash, who is the CEO and co-founder of Complete Intelligence. Tony, welcome to the podcast.

 

TN: Thanks, Geoffrey. It’s good to be here.

 

GC: You and I met probably a bit of a month ago. We did a short video exchange, and it was so much fun, we agreed that we should probably do something a little more involved, and here we are today. Of course, my interest is how digital innovation and digital strategic toolkit are transforming how the oil and gas world operates.

 

Your area of interest and expertise, the focus of your startup is in the application of smart technologies in agile budgeting and forecasting and market modeling. And that’s a big area of interest for oil and gas. That’s the reason why I thought you’d be a terrific guest to come on the show today and talk a little bit about that.

 

TN: Thank you very much.

 

GC: What’s your background? You were with The Economist, is that right?

 

TN: I was with The Economist. I led their global research business for a while. And I built what’s called the Custom Research business. It was a small niche business when I joined. It was a pretty sizable revenue by the time I left. Great organization. Had a lot of fun there. I then moved to a company called IHS MarkIt. Information services firm. I led their Asia consulting business. And from there, we started Complete Intelligence. I’ve been in information services off and on for way too long, since the late 90s.

 

GC: And what’s your education background? Did you start out in computer science or something?

 

TN: I was a graduate at Texas A&M in business and my grad work was in Boston at a school called The Fletcher School, which is a diplomacy school. So I was trained to be a diplomat, although I’m not very diplomatic at the moment. I have my moments.

 

Part of the reason I started going down this road is because in grad school, I had a trade economics professor who was amazing, great guy. I started my career after undergrad at a freight forwarder and customs broker. I didn’t have a glamorous first job. I was actually working the night shift in a warehouse at a freight forwarder, receiving exports and typing out airway bills and all that stuff. I got to know the nuts and bolts of world trade pretty specifically and pretty firsthand. I don’t know of any other trade economists who have started the way I have. I look at trade data differently than almost every other economist that I know of. I look at it somewhat skeptically. It’s that skepticism that I realized in grad school with this fantastic professor that my skepticism was an asset. My skepticism was an asset within statistical, mathematical models, within economic discussions and so on, so forth.

 

I had used it in business before that, but I didn’t think that I necessarily had the ability to apply it in this big world before I had this experience in grad school. So I then took it and I joined The Economist. I kind of conned them into hiring me, which was great, and then within a year or so, I was heading their global research business. From there, we just kind of took off.

 

GC: What are some of the products out of The Economist? Because I buy the magazine every week. And The Economist publishes an occasional handbook of global statistics, GDP by country and balance of trade and so forth. Were you involved in those kinds of products or were the products you were involved in much more specific to a client or customer requirement?

 

TN: I wasn’t. A lot of those are extracts from, say, IMF data. That’s part of The Economist publishing, which is a slightly different business to what I was doing. A lot of what I was doing was really applied work with clients. Solving real problems, figuring real things out. Some of this was corporate forecasting, looking at costs, looking at revenues, those sorts of things. Some of this was doing work for example the World Health Organization in places like Cambodia, comparing different treatments for mother-child transmitted HIV.

 

We had all kinds of cool, different approaches. And from my perspective, we could really play with different methodologies. We could really understand what was working and what wasn’t working. It was a huge sandbox for me. Again, really great smart people. That really started a lot of this kind of true love for me, which is what I’m doing now.

 

GC: What is the business problem that you saw that was sufficiently vexing that you decided to devote a lifetime in a career to trying to solve?

 

Because your career builds you to a point and then you say, “You know what? This is the problem I think I’m going to aim to solve.“ And you know what? You may go on to solve other problems, but at that moment, why would you become a founder to go solve something unless it was so big and so vexing, it was worth your time?

 

TN: I think I became a founder because I underestimated how hard it would be to build a business. Almost every founder will tell you that. When I was with both The Economist and IHS Markit, I had two really consistent feedback points that people gave me.

 

First is the quality of forecasting within information services, within corporate, say, strategy, finance, forecasting units, is pretty terrible. Most people forecast through, let’s say, a moving average approach. Some of the largest companies in the world will forecast using a moving average. If they are super sophisticated, they’ll use a very small maybe regression model or something like that.

 

But what mostly happens is one of two things. Either they look at last year’s and add a small percentage. “We’re just gonna have three percent on this year.“ That’s pretty common. The other one is really just a gut feel like, “I really think it’s going to be X this year.“ If a Wall Street analyst understood how unscientific the way outlooks are done within large companies, they’d be pretty shocked.

 

I mean, there is a belief that there is a lot that goes into the sausage machine. Traditional forecasting is terrible. Any forecast you buy off the shelf? Pretty terrible. Any forecast you’d get within a company? Pretty bad. Even the data scientists that are on staff with a lot of these big companies, really brilliant people, but they’re not necessarily fine tuning their forecasts based on error. And this is the key.

 

Companies who forecast should be required to disclose their error for every forecast they’ve done historically. That’s what we do for our clients. Because the number one problem was the quality of forecasts. So we spent our first two and a half years focused on that problem. We continue our approach to that.

 

GC: When you say “publish the error,” do you mean error in hindsight? How bad were we last year or do you mean here’s what we think our forecasted error is likely to be this year?

 

TN: Every year, any forecaster on planet Earth should say, this is what we forecast last year and this was our error rate. When we look at consensus forecast, for example, for energy like crude oil, natural gas, industrial metals, consensus error rates are typically double digits. Typically double digits. We just did a calculation. When I talk about error rate, I’m talking about absolute percent error. I’m not talking about gaming off pluses and minuses because that’s really convenient. But you look at a plus 10, you look at a minus eight, and that becomes a nine instead of a one.

 

People who forecast should be required to publish their error rates. Companies, especially energy companies, are paying hundreds of thousand dollars, if not seven figures to buy data. Those guys [forecasters] know they’re between 15 and 30 percent off in their forecasts regularly. Businesses are making decisions based on these data.

 

That’s the thing that, as someone who’s run businesses, not just analysts businesses, but run real proper businesses in different spaces, seeing planning people make decisions with a 30 percent error rate or 50 percent error or whatever it is, but no accountability from the information services provider? That’s a problem.

 

That’s a 1990s business model where you could play with the opacity around data. But in 2020, that should not be the case at all. We regularly show our prospects and our clients our error rates because they deserve it. They deserve understanding what our error rates are line by line.

 

GC: In oil and gas, when I’m building up a forecast, particularly for, say, an oil project, I’m having to forecast currency exchange rates, interest rates for my borrowings, the price of certain critical commodities like cement and steel. I’m having to forecast project delivery timeline and schedule. I’m having to forecast future market demand like, where’s my product likely to go? If each of these has a 15 to 30 percent error rate built into them and I’ve added them all up to get to a :here’s my forecasted economics for the year.“ Have I built in and basically had an accumulated error rate that makes my forecast pretty unreliable at that stage? Or these different errors, all sort of stand alone?

 

TN: That’s the budgeting process.

 

GC: I’ve been in that process. Right.

 

TN: Anybody who’s worked on a budget like that, they understand it. Maybe they don’t want to admit it, but we talk to people all the time who tell us. We have a client in Europe who admitted to us that some of their core materials that they buy, they know internally that their forecasts typically have a 30 percent error. And when we say that to people, to other companies, that’s feedback we get consistently that the people who actually know, the data know that their companies have error rates that are 20 to 30 percent or in some cases worse. They’re that far off.

 

When you think about it from a finance perspective, you’re over allocating resources for the procurement of something and that resource could have been used for something else. That’s one of the reasons why it’s really important for us to help people really narrow that down.

 

We check ourselves all the time and we looked at some industrial metals and energy stuff based on a June 2019 forecast for the following twelve months through the COVID period, comparing some consensus forecasts and our forecast. On average, we were 9.4 percent better than consensus. This is a Complete Intelligence forecast. It’s an aggregate looking at one of our manufacturing clients.

 

When you look at the different horizons, we look every three months, what was the error every three months, even up to the COVID period. On average, we were 9.4 percent better on a MAPE (Mean Absolute Percent Error) basis. If you’re buying off the shelf forecasts from some of the typical service providers, you’re looking at a pretty large disadvantage. They’re not using machine learning. They’re not using artificial intelligence. If they are, it’s typically very, very simple.

 

Now, part of what we’ve done through the process is we’ve removed the human process, human involvement in every aspect of data and forecast. From the data sourcing to the validation to anomaly detection to processing, to forecasting, we do not have human analysts who are looking at that and going, “that just doesn’t look right.“

 

GC: OK. It’s all done by machine?

 

TN: Right. We have machines that apply the same rules across assets. Because if we have human beings who gut check things, it just inserts bias and error through the whole process. And with no human intervention, we have a massive scale in terms what we do. We forecast about 1.1 million items every two weeks. Our forecast cycles are every two weeks. And we do it very, very quickly.

 

GC: And nine percent less error rate or a lot lower error?

 

TN: For the ones that we checked for that one client, yes. I would say in general, that’s probably about generally right. In some cases, it’s better.

 

GC: So a few things. One is the huge range of things that you can forecast when you remove all the humans out of it gives you these scale-ups. And then the fact that you can do it over and over and over again in much tighter cycle times than someone who just does it annually, once for a budget. And third, you’re testing your accuracy constantly to improve your algorithm so that you’re getting better and better and better over time.

 

TN: Exactly. When you consider something like crude oil, there are hundreds of crude forecasters who know that, they know that they know the six things that drive the crude oil price, right? And I guarantee you those crude oil forecasters who know what they know, what they know what those six things are, manually change their output once their models run. I guarantee you.

 

GC: I remember working for an oil company in Canada where the coming of the oil sands, but it was the monthly oil sands production expectation and would come into the finance function, where I was working, and the numbers would come in the spreadsheet and the finance people go, “add five percent to that.” Because they would say, they’re wrong every month, we’re tired of being embarrassed about being wrong. And they’re wrong because they undersell their performance. So just add five percent. And that was the number that would go to the market.

 

TN: And then that’s the error, right?

 

GC: And was that even the final error? There may or may not be on top of that?

 

TN: Probably not. And there are very few companies, we have some German clients, so they’re pretty good about doing this. But there are very few companies who actually track their error. And so most companies Are not even aware of how far off they are, which is a problem.

 

Here’s the second problem. The first one is forecasting quality is terrible. So we’ve developed a fully automated process. We measure our error, that sort of stuff. The second one is the context of the forecast. What I mean by that is, let’s say you’re making a specific chemical. You can go to some of these professional chemical forecasters, but they’re not making the chemical exactly where you make it. They don’t have the proportion of feedstocks that you have. Because we’ve built this highly iterative forecast engine that does hundreds of millions of calculations with every run, we can take a bill of material for that microphone in front of you or a chemical or a car, And we can forecast out the cost of every component to that every month for the next 12 to 24 months.

 

GC: Really? So, at any scale or any, I mean, you do it for a phone, you can do it for a car?

 

TN: That’s right. So if you look at a bill of material with, say, a thousand levels in it. Not a thousand components. But, you know, if you look at the parent-child relationship within a bill of materials, these things get really sophisticated really quickly. Some of the largest manufacturers have this data. They have access to it and we can tap into that to help them understand their costs, the likely trajectory of their costs over time. What does that help them? Helps them budget more accurately. It helps them negotiate with their vendors more accurately.

 

If you’re a, let’s say you’re a 20-billion dollar company and you have one percent on your cogs, how additive is that to your valuation if you’re trading at 15 or 20 times EBITDA.

 

GC: Yeah. And just right to the bottom line at that level.

 

TN: Exactly. This is what we’re finding. For the high context as of the second kind of business problem that we’re solving, and so we do this on the cost side. We do this on the revenue side. For that second problem, which is high context, again, the platform that we’ve built allows the scale, because if we had analysts sitting there scratching their head, rubbing their beard for every single thing we’re forecasting, there’s no way we could do this scale.

 

But because it’s automated, because it’s scalable, we can actually do this. And so it adds a whole level of capability within major manufacturing clients and it adds a whole level of risk protection or error mitigation to those guys as well.

 

GC: Just think about the current year that we’re in, which would include, at least in Canada, a pipeline constraints and the potential for rail expansion activity south of the border to either curtail production, the behavior of OPEC. When you think about getting into forecasting world of commodity prices… I can understand a manufacturer bill of materials and get into cost of goods sold and forecasting quite precisely what their forward manufacturing cycle will look like. I can use the same thing, though, in the oil industry, though, and probably gas, too, I would suspect.

 

TN: Yeah, yeah. Absolutely.

 

GC: And what’s the industry’s reaction to it? Because there’ll be people inside oil and gas who are doing forecasting today and they’ll be fairly proud of the models that they built that delivering a forecast. You’re walking in and saying ”I’ve got a whole new way to do this that is so many more cycles faster than what you can manually do, looking at many more products than you practically can. And if I show you that you’re nine percent off, 10 percent off with it.” I can imagine a negative reaction to this. I can also imagine for some organizations, pretty positive reaction on balance. How companies react when you told I can sharpen up your numbers?

 

TN: OK. So I’ll tell you a story about a gas trader. October of 2018, we went into a natural gas trader here in Houston. We showed them what we do. Gave a demo, give them access for a couple weeks so they could poke around. And we went back to them later and they said, “Look, you are showing a like a 30, 31 percent decline in the price of natural gas over the next 6 months. There’s no way that’s going to happen. So thanks, but no thanks.”

 

GC: This was your data telling them? All right. Refresh my memory. What was going on in October of 2018?

 

TN: Nothing yet. But Henry Hub prices fell by forty one percent within six months. So these guys were completely unprepared. The kind of conventional wisdom around natural gas prices at that time were unprepared for that magnitude of fall. But we were showing that that was going to happen. And so when you look at that, we had an 11 percent error rate at that point, which seems kind of high. But conventional wisdom was a 30 percent error rate.

 

We don’t expect to be the single go to source when we first go into a client. That’s not our thought. We know we’re a new vendor. We know we’re offering a different point of view. But we’re in a period of history where you have to think the unthinkable. And this is 2018, ‘19.

 

With the volatility that we’re seeing in markets, you really have to be thinking the unthinkable, at least as a part of your possibility set. It’s really hard. I would think it to be really hard for really anybody who’s trading any magnitude of oil and gas product to put something like this outside of their arsenal of strategic toolkit that they use.

 

GC: Well, certainly, if you had that gap in expectation of gas prices, the gas producer should have been thinking about hedging at that moment. And if their conclusion was, you’re completely wrong and I’m not going to bother with hedging, then shame on them really, because they should have done a far better job of managing to the curve. That’s a great story because it illustrates the challenge.

 

TN: That’s normal. It’s kind of the “not invented here” approach. And I see a lot of that within oil and gas.

 

We see a bit more interest in chemicals. They have to understand the price of their feedstocks. They have to understand their revenues better. And so we see a bit more on the downstream where there is a lot more interest. But midstream, upstream, it’s just not really there.

 

GC: What’s the untapped potential here to sharpen up forecasting? If you’re talking with a company and you say, “I can sharpen up your forecasts and your estimates and tighten up your variability and your business plan.” How does that translate to value and how do you extrapolate that to here’s the the slack, if you like, that’s built up economically within the system and as a whole that we stand potential to extract out and it’s going back to the misallocation of capital, the inadequate negotiations with suppliers, the margin left on the table because of the numbers aren’t just that reliable.

 

TN: We just went through this exercise with a manufacturer with about 20 billion dollars of turnover to help them understand. If you look at, say, the nine percent difference that we had in that exercise that I told you. So let’s say we’re working with the manufacturer with a 20 billion dollars and a PE ratio around 20, which is kind of where they’re trading. If instead of a nine percent or even four and a half percent improvement, let’s just say we had a one percent improvement in their materials. That one percent improvement in their costs translates to a three percent improvement in their net income. That’s three percent improvement in their net income translates to a 1.1 billion dollar improvement on their market capitalization.

 

We’re not going out there saying, “hey, we’re gonna help you save 10 percent of your costs.“ We’re not going out with statements that are that bold. We’re saying, “OK, let’s run a scenario where we help you with a quarter of a percent,“ which would help them add 280 million dollars on to their market capitalization. So procurement management and planning is kind of that tightly calibrated that if we helped this company with 0.25 percent improvement in their costs, keep in mind we’re nine point four percent better than consensus, that actually helps them add 280 million dollars onto their market cap. It’s just exponential.

 

GC: Well, it’s the leverage effect of earnings per share as you drop those earnings to the bottom line. And so anybody who’s actually measured on EPS or stock price should take a very interested look at this because you’re not selling a hardware, big capital investment, stand up a big department, not stuff. This is about taking the current process, that’s their budgeting, and squeezing out the variability or the error rate and trends that translates directly to value. When you think about it, it’s a complete no-brainer. Like, why would you not do this?

 

TN: It is. And we’re not going to charge them 280 million dollars to do it. But we could charge for this agile budgeting and forecasting. But we’re not going to.

 

GC: What you would do is you’d say, we’ll take shares in your company.

 

TN: I mean, that’s been suggested many, many times.

 

GC: Yeah, no, I totally get It. I say to oil companies, I’ll sell my services to you based on the price of oil. But the shareholder actually values the volatility on oil pricing. So they’re not prepared to give that away. And I’d be the same. I wouldn’t do that. But on the other hand, the back to this question of untapped potential. The ship, the bulk of the economy is operating off of wildly inaccurate consensus estimates. I think that’s fair to say, I don’t know if that’s accurate or not, but that would be my my conclusion. The bulk is operating off of inaccurate assessments. And so over time, what should happen is we should see a considerable improvement in that, which in turn translates into much better performing economy, allocation of capital and supply chains and so forth.

 

So you’ve been an entrepreneur now for how long’s it been three years?

 

TN: It’s five. We started as a consulting firm. It’s been about five years now. We actually started the company in Singapore. I moved it to Texas at the end of 2018. I couldn’t really find the coding talent and the math talent in Asia. I know this sounds really weird, but I couldn’t. And so I relocated the business to Texas in 2018.

 

GC: Yeah. And the talent pool is rich enough in the United States to fulfill this ambition?

 

TN: Yeah, yeah. Yeah. Totally fantastic.

 

GC: And what lessons have you taken away from all of this experience? Would you do it again?

 

TN: I would do it again. But I would do It differently. Anybody who starts a business has to realize that markets aren’t necessarily ready for radical new thinking. And it really takes a long time to get an idea of this out there. The kind of AI industry and the talk about automation has been around for a long time. But things like this, companies aren’t really ready to just let go of. It takes a lot for them to consider letting go of this stuff.

 

If your idea is pretty radical, it’s probably to take a while to socialize with an industry. But I would say it’s also, we as a company, we had a staff issue about a year ago, actually, that really shook us. And out of that, we developed our principles and our values. For anybody who wants to do this, you really have to understand what your own principles and values are from early on. It’s not something you wait until you’re 100 people to develop.

 

That issue a year ago was a very clarifying moment for us as a company. It really forced us to think about what kind of business we wanted to build. And I’m grateful for it, although it was really terrible at the time. I’m grateful for it because we have our values. It’s actually posted on our website. Whenever we recruit new people, that’s one of the first things I send to them and say, “Look, this is who we are. If you’re not comfortable with this, then this is not the right place for you. I’m sure you’re talented, all that kind of stuff. But we really live by this stuff and and those things are important.“

 

The other thing I would recommend for anybody who’s doing this is you’ve got to play nice with everyone on the way up and you got to play nice with everyone on the way down. It’s easy for tech entrepreneurs to really think a lot of themselves. And I think that’s fun. But it’s also not really helpful in the long run.

 

There’s a lot that I’ve learned about recruiting leadership teams, finding fit, looking for investors. I have the Asia experience. I have the U.S. experience. The math and the tech around A.I. is almost the easiest issue to solve. With technology, as long as you think big but retain humility, you can do a lot. You have to be bold, but be comfortable with mistakes.

 

The trick is getting the right team and the right investors who are comfortable with that environment. And if you get the right team and the right investors who are comfortable with that, then it can be much more fun. You actually have a chance at being successful because so many startups just fail. They don’t last a year or two years, much less for five. It’s really, really critical to get the right people.

 

GC: Yeah, I completely agree. The people and the money, it’s both sides. If the investors don’t have the patience or they’re marching to a different drum like they want short term results, and that’s as much of a death knell for for many startups as a talent talent deficit.

 

Tony, this has been excellent. Thank you very much for taking the time to join me today on Digital Oil and Gas. And if people want to learn more about Complete Intelligence, where do they go? What’s your website?

 

TN: Our website is completeintel.com. And we’re on Twitter. We’re on LinkedIn. There’s a lot of information there. And like you did about a month ago, we have a lot of five-minute interviews we do with industry experts and a weekly newsletter. There are a lot of ways to get to learn about us.

 

GC: Fantastic. Tony, thank you very much. This has been another episode of Digital Oil and Gas. And if you like what you’ve heard, by all means, press the like button and the share button and add a comment, and that helps other people find the show. And meanwhile, tune back in next week, Wednesdays, when we’ll issue another episode of Digital Oil and Gas. This is Canada Day week. So happy Canada Day to my all my Canadian listeners.

 

And Saturday is Independence Day. It’s July 4th. So, Tony, have a great time on Independence Day. Be socially distant and be safe out there. Thanks again.