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“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.

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.