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In the latest Week Ahead episode, three experts – Todd Gentzel, Chris Balding, and Sam Rines – discuss the impact of AI on the job market and the enterprise.
The conversation delves into the macro environment and the rise of AI, with Sam Rines framing the discussion by noting the fast adoption of AI tools like ChatGPT and Midjourney, which are taking out low and mid-level writing, creative, and analyst tasks. This is a threat at a scale not seen before as this generation of AI is targeting professional, corporate, and office jobs.
Todd Gentzel, who has consulted and led strategy for some of the world’s largest companies, discusses the current state of AI in the enterprise. He notes that many AI projects are just pet projects to tick a box and the “AI” portion of these projects is extremely limited. However, he believes that AI has the potential to change the enterprise significantly and identifies the factors holding the enterprise back from adopting useful AI.
Chris Balding, the founder of an AI-NLP firm, discusses whether AI will steal jobs. He notes that starting his firm has changed his view of the application of AI and its potential to take on whole job functions. The conversation covers the impact of AI on labor and capital, the potential for AI to be deployed to take on individual functions, and whether AI can only be used to augment job functions or take on whole job functions.
The discussion raises important questions about the impact of AI on the job market and the enterprise, and how it will change the way we work. While the experts have different perspectives on the potential of AI, they all agree that it will have a significant impact on the economy, the job market, and society as a whole.
Key themes: 1. Is the macro environment to blame for the rise of AI? 2. How will AI change the enterprise? 3. Will AI steal your job?
This is the 60th episode of The Week Ahead, where experts talk about the week that just happened and what will most likely happen in the coming week.
Hi everyone, and welcome to The Week Ahead. I’m Tony Nash. Today we’re joined by Todd Gentzel. Todd is an industry and technology strategist spanning healthcare, mining, oil and gas, transportation, and consumer goods. Todd, it’s your first time on the show. Thanks so much for joining us.
We’ve also got Chris Balding. Chris Balding you guys all know well from Twitter. He’s the founder of a stealth mode AI firm, and he’s also the founder of New Kite Data and a recovering academic.
We’ve also got Sam Rines of Corbu, who’s on here regularly. So guys, I really appreciate your joining us for the program today. This means a lot.
I’ve wanted to look at the hype around AI for quite some time. For non-experts, it’s really hard to tell what’s hype and what’s real. We see stuff about ChatGPT or whatever every day, and we can’t tell what’s real output, what’s simulated output, or whatever. So we try to assemble you guys, some experts, to tell us what’s happening. And there’s some real critical answers that we want to address. Why is AI on the rise right now? There are some reasons why AI is coming to the forefront right now. So what are those?
Will it take your job? A lot of people are, and some people are joking about that. Some people are taking it seriously, some not. But really, will it?
How will AI change corporate life? What impact will AI have on markets and regulations and so on? These are all things that we don’t know all the answers to right now, but we’re kind of figuring this out as we go along.
So, just over a year ago, I published a fairly rudimentary illustration showing the pace of impact that I thought at the time AI would take in the workplace and on jobs. So if you notice at the bottom, most of the kinds of infield jobs are retained. A lot of stuff has to physically happen. And my view, at least over the next, say, a few years, is 5% to 10% of jobs need to be automated. And I think that’ll largely grow toward the end of this decade.
So we have some key themes. First, is the macro environment to blame for the rise of AI? I think that’s a real concern, and we’ll talk about that with Sam. Second is how will AI change the enterprise. We’ll talk about that with Todd. He’s a real expert there, and I can’t wait to have that discussion. And finally, will AI steal your job? That’s kind of a silly question, but I think it’s one that everybody really wants the answer to, and we’ll talk about that with Chris.
So first, Sam, I want to frame up the discussion with a little bit of an understanding of the macro environment. We’ve had AI enthusiasm before. You have these really robust AI eras, and then you have kind of AI winters. We had a really robust era in 2018 when S&P bought a company called Kensho, which very few people talk about now.
This was just five, or six years ago. They bought Kensho for $550 million and really, nothing happened with it. They were folded into S&P. At the time I talked with people who had visibility to Kensho. They didn’t know what to do with it. It really wasn’t obvious value. But S&P kind of got the opportunity to tick the box on AI. So, in part, S&P wasn’t adopted by S&P’s customers. At least this is my running thesis. It wasn’t adopted by S&P’s customers because wages had been pretty stagnant for 30 years.
So even in 2018, you could kind of throw people at analysis problems and the type of things that Kensho was built to solve. But now we’re seeing ChatGPT, MidJourney, and those types of large language models and image models being adopted pretty quickly.
ChatGPT, as you guys know, had millions of users in the first hours, in the first couple of days. So we can say that processing power and coding and that sort of thing are responsible for advancement in AI, which is true. But adoption seems to be different than the actual capability. So when we see ChatGPT and MidJourney adopted so quickly, they’re really taking out low and mid-level writing, creative and analyst tasks. That’s what they’re taking out right now, are those tasks. These are things that earlier had 10-15 years ago, had been sent to, say, India and other offshoring places, but now it’s being experimented with doing this stuff virtually in developed countries. So I realize I’m talking a lot today. I don’t normally do this at the top of the show, but I think we need to introduce some of these ideas for people to watch.
I’m sorry I’m talking so much today, but one key point here is that AI has always been discussed more than robotics. So where it would take over the job of physical laborers, like people in warehouses, blue-collar workers, as Americans would call them. But this generation of AI is different. This generation is targeting professional jobs, corporate jobs, and office jobs, which are new. It’s kind of unprecedented, where this level of fear for white collar jobs is discussed to be replaced by technology. So, Sam, after that long intro, can you talk us through some of your thoughts on this? This is my hypothesis. Is there anything there? Can you talk us through some of the kind of capital versus labor and wage issues that we’re seeing right now? And is that having an impact on the adoption of AI?
Yeah. So don’t throw too much at me at once. Okay, so let’s take a big view of the history and kind of parse this out, because I do think it’s worth kind of going back to previous periods to look at what exactly spawns the adoption of various technologies. Because AI is a technology and it’s incredibly useful for those people that want to become, or can become much more productive over time. So I think that’s kind of the level set there. But if you look back at 70s and the level of inflation there, it spawned a significant amount of capital investment in things like computers, right. It was expensive to hire an individual, inflation was running out of control, and you wanted to maintain your margins if you were a corporation. So what did you do? You made people more productive by employing technology, specifically the computer at the time. Right. It sounds kind of ridiculous to say that the computer was a productivity enhancer because we all know that now productivity is not necessarily enhanced by a computer in front of you. But then it was incredibly enhanced for productivity. So when you have significant inflation pressures against a business, it spawns the want and the need to go ahead and invest in incremental technologies.
So kind of fast forward to COVID, and if you were a leisure and hospitality company or a company that faced individuals, you had an incredible incentive to invest in an underlying technology to allow your business to either exist in a couple of years or to survive and maybe even thrive. If you were very good at it. You had to go out and you had to make sure that your website could offer delivery or pickup options for food. You had to really invest in technologies that previously didn’t necessarily have to do. Were they emerging? Were they interesting? Yes. But all of a sudden they became existential to your business and the ability to survive going forward. So you saw an incredible amount of investment in platforms that allowed for delivery and pickup of food, et cetera. Kind of coming out of COVID. Now what you have is an incredible shortage of workers and a significant amount of wage pressures, and you have inflation pressures. So if you’re a business looking to maintain margins, grow going forward, AI is an incredibly interesting potential tool for you to be able to make some of your best workers and best thought leaders and intellectual leaders much more productive and allow you to grow going forward without having to worry about whether or not you’re going to be able to find that incremental employee.
And I think that really is an understated catalyst for why ChatGPT-4 is so incredible, right? I love it. It makes me a lot more productive at my job. I’m still playing with it and I don’t actually publish anything.
Can I just give you a tangible example of what you’re talking about? I know that you understand this Sam, but for our viewers. So my staff last week put together a persona in a large language model and called it Nash, and it looked at all of our previous shows of The Week Ahead and then it came up with a persona for Nash. So last week’s newsletter, Complete Intelligence Newsletter, and going forward, they’re largely written by this persona in Chat GPT. So we don’t have to spend the time anymore to actually write our newsletter. Of course we clean it up a little bit, but it has my voice, it has my word choice, sentence structure and so on. And so largely our newsletter is automated and of course there are little tweaks here and there, but for the most part those are the types of things where maybe I had to hire a newsletter person before, even if they were offshore. But now it’s done in three minutes.
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No, again, that’s productivity enhancing for your team, right? And it allows you to say, okay, now that we really kind of come up with a way to automate this newsletter, what else can we do? So it allows you to be not only productivity enhancing, but potentially revenue enhancing, potentially bottom line enhancing, producing new products, new services, et cetera, et cetera. So in my mind, that is the one of the tailwinds to AI adoption at this point is that you really have not only called a curiosity with it, but also a need to replace the incremental employee because you can’t find them. If the incremental employee doesn’t exist, you’re not destroying jobs, you’re creating/enhancing ones that exist. The idea I’m kind of running ahead of us. I know, sorry. But to me that’s really the catalyst behind the current adoption, right? And if you look at one of the most labor intensive businesses out there and we kind of touched on this while we were chatting before reporting if you look at agriculture, I mean, John Deere has been working on AI tools for farmers for a decade and has bought up a significant amount of IP around that to not only allow farmers to become much more productive, but potentially make it so the farmer doesn’t have to be in the tractor during planting, during when they’re spraying the plants early on and during harvesting, the farmer can go do other stuff.
So I think as we begin to really understand that there aren’t enough farm workers out there. That there aren’t enough people to hire into various businesses, I mean, just look at the participation rate. The participation rate is not exactly coming back the way anybody thought it would after COVID, and it’s unlikely that it’s going to recover anytime soon with the number of retirees. Retirees have a significant demand for services. If you’re going to provide those services, you’re going to need to not only adopt new technologies and new tools, you’re going to have to come up with new ways of doing things generally. So I think AI always was going to be something interesting, but it’s something interesting at the right time with the right catalyst moving forward. And this is not something that’s going to be… There’s a little bit of fattiness to it in different ways, but I don’t think it’s going to be one of those passing fads that everybody’s like, “remember when AI was a thing?” I think it’s much more of something that we’re going to interact with on a daily basis across a whole lot of services and a whole lot of businesses that we did not anticipate prior.
So two things there. Technology generally is deflationary, right? I mean, aside from like $1,400 iPhone or whatever, generally, technology is deflationary for kind of status quo activities. Is that fair to say?
That’s good. And then you said something like, we’re going to X with AI. But people are already experimenting with that stuff. So we do have people who are already doing that. And it’s really a question of it going at things going broad market. Like, I don’t want to be the AI hypester here. I’m really just kind of asking these types of questions just to understand your view on this stuff.
Sure. I think it’s pretty straightforward. Right. You have to have some way of replacing a nonexistent labor market, and AI does that in a fairly efficient manner.
So it’s demographics, wages, participants, demographics, wages.
Demographics change slowly than all at once. It’s not as though you can simply incentivize the demographics to change. Right?
That ship sailed a long time ago. Generally, to your point, demographics are a powerful force where when you have a significant amount of people that are older and out of the labor force demanding a significant amount of services, you have to figure out a way to deliver those services into them. With fewer people in the labor force, which is a massive long term catalyst to tools like AI, like ChatGPT, that type of thing, and it’s not going to stop there.
Yes. Okay. Good points. Okay, so let’s move from the kind of context and thanks for that, Sam.
Let’s move into how will AI change the enterprise? Todd, you’ve consulted and led strategy for really some of the world’s largest companies. In enterprise circles, we hear about AI projects from big consulting firms or a firm like Palantir, which really is a consulting firm. These are largely pet projects to tick a box. But at least in my mind, the kind of AI portion of these projects is extremely limited at this point. So given the economic context that Sam discussed and the corporate dynamics that you’re aware of, is AI in the enterprise a real thing right now?
Yeah, I think that you probably have to break it into a couple of groups. I think the earlier statement about agriculture and John Deere is true in oil and gas is true in healthcare. I mean, there are lots of companies that have been at this for a while, and they’ve got relatively mature environments, and in those environments, they’re really playing a different game. It’s not a check the box. It really is kind of fundamental to business models. I think there’s sort of a sort of much larger group of organizations that are just beginning to be aware of the opportunity in the kind of intermediate and long term. I’m super positive. I think this is unquestionably, the direction this has been headed for a long time. I think in the short term, we’re going to see what we always see during these periods of technical transition. It’s going to be messy. I think it’s important to always remember that there are real power dynamics around any adoption of new technologies. And in a lot of cases, the people who are in leadership and the people who are making these decisions are the authors of the current state.
And so they struggle to sort of conceptualize what the world would look like under a completely different set of norms. And I think unlike some of the previous generations of technical advancement, I would argue we’re coming out of the age of digital enablement. We’ve talked about transformation. I think there’s been very little transformation. I think it’s mostly just enabling some core things we were already doing and gaining some minor improvements in productivity. AI is one of a dozen exponential technologies that plays a very, very different role in accelerating innovation and accelerating business model development and changing operating models. That’s where things get really dicey. And I think there are going to be winners and there’s losers. And I know, Tony, you and I have talked over the years about when you do scenario planning, you sort of right off the bat, assume that there’s really no good or bad future. It’s good for some and it’s bad for others, and I think that’s going to be true here. I think what we’re going to see is there are organizations who have spent the last decade really creating the kind of agility, the kind of resilience that’s necessary to make a transition like this and really capitalize on it.
And there’s going to be some organizations that really struggle. And that’s why I actually think that this may not be the age of the incumbents. I think that the people who are really intending to disrupt have a window of opportunity here while people are kind of working through the internal dynamics of what it means to adopt these new technologies and brand new ways of working. People who are unencumbered by those cultures and those kind of leadership norms are going to be able to move much more quickly and likely be able to sell into that world. And I think that’s going to give rise to a whole new group of consultants. I think there’s always the system integrator model and we’re going to sell the big thing and we’re going to work it out over five years and rest of that. I think that the people who will play most prominently in this next phase really are hyper specialists and they’re going to come in and they’re going to solve significant real problems.
When you say that, I think you said the current operational architecture is a reflection of the current leadership or something like that. And it sounds like they won’t change willingly. Just to be a little bit brutal here, is there going to have to be a wave of retirements or something like that for AI to really hit larger firms or what would push larger firms to attract or to adopt really interesting levels of, say, technology and productivity?
I think that we’re at a kind of a unique place where a lot of the things that made us successful in the past are the things that actually inhibit our progress. And you know, if you’ve got folks who are relatively intransigent, I mean, really the only option is to move on. We used to have a firm I worked for. This sounds really crass. We had a phrase you either change the people or you change the people. And I think we’re at that kind of a moment where if you find yourself in an environment where the leadership and the operating norms really are not particularly conducive to making these key pivots, everything Sam said is right on the money. I mean, these are economic realities. You’re going to have to make these changes to remain competitive and you’re going to have to find a way to a new way of operating that will allow you to do that again and again and again. Because this isn’t an embrace AI. It’s embrace tool after tool after tool that’s solving these problems. It’s a very different discipline, but it’s also spinning up a bunch of interesting challenges. I was just talking to somebody this week that was working on some things around material science and leveraging AI in that space.
And we are so rapidly spinning up new materials that it’s difficult to find people who are capable by way of their training, of conceptualizing the utilization of those materials. And so these opportunities in some cases take a little while not just to ingest but to train up people to leverage these to their full extent. Which is why I think the short term is going to be really a story of fits and starts. There’s going to be some big wins and there’s going to be some significant resistance. One of the places where I’m kind of most interested right now is what was mentioned earlier about sort of the top of the food chain right. You’re talking about very elite, top level professional jobs. We’re already seeing some really incredible things in the healthcare space around second reads of scans.
What does that mean, second read? Can you walk us through that process? Yeah.
So the radiologist takes a look at your X ray or MRI and says, this is what I see. And then it automatically goes out to an AI engine that goes in and makes sure that everything was caught. And what we’re finding is that we’re routinely catching things with the AI. Well, that’s beginning to tell a story, not just about supporting the work of a radiologist, but potentially, over time, the machine actually becoming a superior mechanism to leverage as a first read and a second read, and you can actually create alternate models. And these are things that are not science fiction. These things are already happening. These are institutionalized systems are doing it really to mitigate risk. I now can say I’ve looked at it multiple ways, and we feel fairly confident at what we’re seeing. That’s happening in industries right now, where we’re actually seeing real life, serious use cases that are mitigating risk, lowering costs, improving outcomes that needs to be scaled. And that’s really what I’m getting at. I think that you see these really interesting spot treatments, right, where we’re looking at something saying, I can solve that. The question is, how do enough of those actually begin to be leveraged?
It becomes a way of working rather than just a tool in the box that we go to in very specific and very narrow circumstances.
So what about those people who say, “oh, I’ll never let AI be my doctor, I’ll never have a robot for a doctor, or I’ll never let AI be my CPA” or something like that? Will they have a choice?
Yeah, I don’t know that they will. I will tell you that there’s some pretty sophisticated tools that are already on the market that are very close to being able to achieve the same level of efficacy and diagnosis as the very best physicians that we have. When you think about that as a language model, I mean, if you think about, like, a Physician Desk Reference and you’re asking questions and you’re getting the medical history and you’re making decisions and there’s things that the machine is capable of doing that’s, just far more capable in the human mind in evaluating the different levels of risk and the likelihood that this is what I’m seeing versus this other thing. Because we’ve seen such a remarkable advancement just on that front in the last four or five years, and you’ve seen its adoption. You look at the NHS or you look at Medicare and you say, there’s absolutely no way, at least at that first level of diagnosis, that we’re not moving very aggressively in that direction for a lot of reasons. Number one, it’s much cheaper, but number two, it’s super available. It’s easy access. We’re actually catching these things long before they become genuinely problematic and cost the public a whole lot more by way of health care dollars.
So I get it. I understand it. I think there’s sort of an impulse initially to say “I’m very uncomfortable with that.” But increasingly there is a whole lot of diagnostic stuff that’s happening behind the scenes that people aren’t seeing that’s already in place. That’s pretty significant part of their care.
Right. Okay, so this is where I’m going to give a little shameless plug for complete intelligence, just to give people a little tangible idea of what can be done.
So we do budget forecasting for companies, and we have one company, a client, $12 billion in revenue. They have 400 people who take three months to do their annual budget process. We did that in 48 hours, taking one of their people less than a week of their time to transfer knowledge to us. We had better results in 48 hours than what 400 people did over three months. And this is a very tangible way of identifying the opportunity that’s available with AI tools and other technology tools. It’s not just replacement. It’s not RPA, robotic process automation. It’s not that it’s better. Right? And that’s where people should be a little bit aware, where we’re talking about doctors, we’re talking about people with MBAs, we’re talking about highly educated professionals where we can have a machine do that work better and faster. And that brings us to Chris Balding to give us great news, Chris. Thanks, Todd. I really appreciate that. And you guys jump in on this anytime.
Chris, the real question here is, will AI take my job? Right? My job? I’m hoping it does. But for most people, will AI take their job? I think you’re about to launch an AI NLP, a natural language processing firm. First question, I guess, is how has starting that firm changed your mind about the application of AI today versus even just a few years ago?
I think there’s this discussion about will it take people’s jobs? And if you look back on really any technological breakthrough from the cotton gin to fracking, what you really had is the per unit price would drop of a T shirt or how much it costs to get that oil and gas out of the ground. But what happened was it consumed people that had the technical training, higher levels of technical training. If you think about AI, people will say, well, hey, we don’t need as many coders. Well, you know, what’s going to happen is that opens up a whole new field of cybersecurity risks. And all those coder jobs are going to migrate into cybersecurity because all you’re doing is opening up cybersecurity risks, as a simple example. If you talk to any IT guy inside big companies or whatever, there’s typically a list of about 40 projects management wants them to work on, and there’s 20 that are constantly at the top of that field and they never get to those more advanced, maybe investment, longer term types of product. Well, if you’re able to blow through those 20 faster, as a simple example, you can move on to those more creative, risky type of projects.
So when I hear people talk about, well, it’s going to take my job, I think it’s absolutely going to change how people work. I think it’s going to change the types of jobs that we do. For instance, one type of coding might move more into cybersecurity. Is it going to eliminate these jobs so that the total level of employment disappears? Absolutely not. It’s just going to change how we work and the specific jobs we do.
So is it at least at this phase, is it more augmentation than it is automation?
So it really kind of depends on what you’re specifically saying. One of the things, and I think OpenAI has, has even said things to this effect, you know, we talked about macro and other stuff, but really, what has, what is undergirding this is that really, for the past, let’s say five to ten years, you’ve basically seen this exponential increase in AI type stuff. And that is really driven by, just to be blunt, the hardware of what you can do with GPUs. And part of the reason that we talk about this is, going forward, the amount of GPU capacity that you’re going to need is I mean, you’re going to start sucking down. I mean, the the amount of energy that they were sucking down from GPUs to do bitcoin will pale in comparison if it really takes off the way people say it will. I’ve used it for a lot of coding and similar types of things. And what you really see is, especially on more complex types of projects, you kind of use it to kind of seed what you’re doing, maybe take specific steps. It absolutely, I don’t think, is near the point where it can basically manage entire significant projects.
And so it’s absolutely a time saving tool. We talk about this with coders. It’s absolutely a time saving tool. Is it taking over their job? No, absolutely not. It’s going to help them do things faster, move on to more complex types of processes that they’re trying to automate.
Okay, but if it helps people do things faster, then that means they’re spending less time doing the job they have now. So somebody’s losing, right? Somebody’s losing a job, right?
Because if it’s helping people do stuff faster, then companies have to spend less time on headcount. Right? I’m trying to get out of the, hey, this is replacing jobs. But we kind of end up there with this type of technology.
Yeah. So think about it two ways. Let’s assume you have an IT department. All of a sudden, that IT department is doing less work, making sure that there’s not a paper jam at the printer and that the computer can talk to the printer. Okay. There’s less time spent doing that. But I guarantee you there’s hackers in Russia that are now using ChatGPT to say, “how do we break into this?” Part of the issue is that guy who started out in It is probably going to move over to cybersecurity. Okay? Or they might say, “hey, we can let go of a couple of people, but now we want these other guys to focus on these bigger investment type projects that maybe we had kept on the back burner because they just didn’t fit within our budgetary priorities.”
Okay, so those are relatively fungible skills. But if you’re like the Radiologist that Todd’s talking about, can those skills be repurposed to something else?
Well, honestly, I think it’s case by case, but I mean, Radiology is a great example and just health care generally. I think we’ve all probably heard that we have a nursing shortage and that you can’t find an endocrinologist and we’re constantly dealing with this really serious labor issue. A lot of that is because across the board in healthcare you have people really failing to operate at the top of their license because they’re spending an incredible amount of time doing the paperwork, meeting the CMS requirements. And so you have doctors who are doing 30% doctoring because the rest of their time is basically meeting all of the obligations to all the different stakeholders. Right.
I think what we’re likely to see is these people who are sitting in that sort of, again, that sort of top tier of kind of professional expertise, really spend more of their time doing value creating work. I think if you think about what’s really going on, we have effectively an opportunity cost that’s baked into everything that we’re just not doing because we’re doing all of these things that really don’t require somebody operating at that level.
What we’re trying to do. I think and I think this is really the way we should be framing the future of AI is that if you really get focused on value creation and you start talking about that opportunity cost gap, I need every one of these employees operating at the very top of their capabilities, regardless of whether they’re a physician or a coder. And I need most of their time being pushed against real value creating activities rather than all the stuff that really should be relatively easy to put off to this other way of operating. And I think you can be threatened by it or you can recognize that the greatest inhibitor to innovation over the course of the last decade has not been our ability to produce technology. It’s our ability to free up capable people to really focus on the innovative things that need to get done in order to make things go to the next level. This is that linchpin moment. And every leader ought to be asking the question like, “how do I maximize the value of every single human asset that I have and really get them operating at top their license.”
And if that’s not the focus, then this probably is going to be a challenging period and it will become about cost and it’ll become about reducing by way of eliminating positions. That’s not, I think, the way to go. I think that’s actually probably the wrong way to think about it. I don’t doubt that there will be people who will be in that trap because they just are going to have a hard time to make the move, but the smart companies are going to be able to understand that very quickly and move aggressively to make that happen.
Yeah. And I think that’s a critical point that should not be overlooked is you can be scared of it or you can embrace it and use it as a tool to enhance your one, your life, because none of us like doing the lower end of the spectrum stuff that we always have to do. If you use it to eliminate that and get to do the stuff that is much more highly value add, that is incredibly accretive not just to the business but also to your lifestyle in general. Right. I think embracing it and actually having a positive attitude about it and saying, how can I use this to make myself more productive and generally more happy? Because hopefully we’re doing things that we love to do. How do I use this to do that? I think it’s all about the mentality of approaching it rather than saying, “oh my word, is this going to take my job?” I think it’s a fundamental thing that if you think it’s going to take your job, it probably is simply because you’re not going to embrace it and learn and try to adapt to the new technology, you’re going to fear it and shut it.
And I think that’s going to be the fundamental difference between those that succeed with the new technologies that are coming and those that fail and fail in a meaningful way.
Yeah, but I think fear is a natural response to something like this. Right. I mean, we’re all kind of not all of us, but a lot of us are afraid of new stuff. We’ve had our same job for 10-20 years. We have a routine, we go in, we do our work, we leave it five and call it a day. That’s most people, the vast majority of people, and I don’t necessarily think maybe I’m a skeptic here and maybe I’m a bad person for thinking this, but as Todd you talk about people want to look at the greatest value add they can have within their job and that will help them from being kind of automated. I don’t know that most people think that way. Maybe they do. But I think most people are just kind of going in for hours to do a routine job and those are the things that are the most dangerous, I think the positions that are the most dangerous.
Before we kind of wrap this up, I don’t want people to think that I just kind of loaded this with people who I knew would have the same view as me.
So, guys, let’s take the other side of the table for a little bit. And I’m not accusing you of having the same view as me, but let’s take the other side of the table a little bit. Let’s assume that large language models and Chat GPT and all these things are overhyped right now, okay? What could stop the implementation of these technologies so that they aren’t adopted across companies and across the economy? What could stop this stuff? Chris, you’re muted.
I think one of the things is Todd has alluded to this is you’re going to need so basically the basic technology that ChatGPT used is really probably just ten years old. They just added a lot more data and a lot more GPUs. I mean, the fundamental technology is not new in the least. What you’re really going to need, what is going to stop this is now you have to get domain experts coupled with those tech geeks to say, what can we do together? So whether it’s an endocrinologist, whether it’s a financial analyst, whatever it is, and one of the things is outside of the mainstream that you’ve seen a lot, is how can you develop these language models that are providing very precise answers for very specific fields? I’m a tax accountant. I am an endocrinologist, I am whatever. So if you don’t bring those domain experts together with those tech geeks and you’re just stuck with ChatGPT, which is basically trained on the Internet, you’re going to get a lot of bad answers rather than being able to augment what those humans can do.
Well, I would go further on that and say that those domain experts are critical, especially at this moment in time, right? Like, you start thinking about healthcare, aviation, mining, oil and gas, places where there’s really some very significant risk, and you say, look, those domain experts working side by side, they see that risk coming, they bake that into the conversation. They talk about what to actually put in that learning model to actually create an environment where you accomplish those kind of incremental improvements, but without exposing the organizations to exponential risk. I would tell you right now, the issue is it’s early. And so there’s not a lot of domain expertise that’s actually fluent enough in this to have a dialogue that’s meaningful to kind of push this forward. And the risk that’s inherent to that is the sort of ugly pre adolescence, as we sort of learn our way into using the technologies appropriately, getting out over our skis and getting some things really profoundly wrong, that really creates sort of a downdraft, right? Like, oh, this failed, or this didn’t work or it opened up this massive amount of risk, that’s a human error question. That’s really just a function of moving more.
Just to kind of add to that, Todd. Give me 1 second, Sam. I’m sorry about that is one of the issues that especially in an issue like the medical field, and I’ve heard this talked about in multiple other fields, is humans are there for a reason and especially if there’s a license, if there’s legal liability, et cetera, et cetera. No human, no matter how good the technology is, even if the technology is demonstrably far superior to human, no human is going to turn that legal liability over to a computer without saying, I’m going to sign off on this, I’m going to check it. And as you said, Todd, that machine learning was basically double checking what the radiologist was doing, just verifying.
Yeah, to Todd’s point and to Chris’s point, and I think this is really important, if we don’t get the domain experts in there to actually help and make better decisions, better outcomes, better reporting by the by ChatGPT 4, 5, 6, 7, 8, we are going AI in general is going to end up being regulated in a meaningful way. It only takes a couple of really big incidences, car crashes, et cetera, before you end up with the FAA, before you end up with the Transportation agency, et cetera, et cetera, Department of Energy. However you want to look at it, the amount of regulation that will come down on top of this in a landslide like way if you don’t get it right from the beginning and have some sort of self regulating mechanism, whatever it might be, is another, I think, understated suffocating factor, right? There’s nothing that suffocates innovation like regulation. And if you don’t get it right and you don’t get it right pretty quickly the amount of regulation that’s going to come down on this, particularly when it’s consumer facing, when it’s labor facing, those are some very powerful lobbies that are going to absolutely hammer this if it’s deemed to be unsafe or dangerous. I mean, it’s that simple.
Interesting. So basically what I get from you guys is we’re likely to have at least a few years where it’s more augmentation, where those experts are feeding back into the models to help them understand what they do before these things can really go off on their own. Is that fair to say? So we can’t just open the box today, replace a bunch of jobs and everyone’s on government payments or whatever for the rest of their lives. It’s going to take a few years for this stuff to really get some practical momentum in the workplace.
I think that’s right. But I think to that previous comment, the industry has to be very careful to sort of self moderate here. I mean, there are going to be folks who really very diligently go about the process of ensuring that we do it right. And then there will be people who inevitably will play it fast and loose. It’s the folks on that side of the fence that actually create the downward pressure from the legislative and regulatory environment. And so it’s just kind of an interesting moment in time because it’s sort of the learning period that really puts it on a solid footing. But it’s also a period where there’s a great deal of volatility and potential for there to be some kind of significant things that happen that actually harm the long term ability to get it implemented in a way that makes sense for the public.
Very interesting. Yeah, I think that regulation point is so super important. Okay, guys, anything else to add before we wrap this up? This has been hugely informative for me. Anything else that’s on your mind about this?
I’ll just say don’t fear it. Use it. If you’re not using it, if you’re not trying to learn about it, then make it make you better or get out of the way.
Exactly. Watch a few videos, learn how to do some mundane tasks. Use it to your advantage and do things like we do with our newsletter. Just get some really routine tasks automated and then just start learning from there. So guys, thanks so much. This has been really, really valuable. Thank you very much. Have a great weekend.
This month, the S&P 500 officially hit bear-market territory—meaning a fall of 20+ percent from recent highs—and investors everywhere are looking for some way to predict how long the pain could last.
Machine learning startups specializing in “nowcasting” attempt to do just that, by analyzing up-to-the-minute data on everything from shipping costs to the prices of different cuts of beef. In times of economic volatility, investors and executives have often turned to market forecasts, and ML models can offer a way to absorb more information than ever into these analyses.
One example: Complete Intelligence is a ML startup based outside Houston, Texas, that specializes in nowcasting for clients in finance, healthcare, natural resources, and more. We spoke with its founder and CEO, Tony Nash, to get a read on how its ML works and how the startup had to adjust its algorithms due to market uncertainty.
This interview has been edited for length and clarity.
Can you put the idea of nowcasting in your own words—how it’s different from forecasting and the nature of what you do at Complete Intelligence?
So Complete Intelligence is a globally integrated machine learning platform for market finance and planning automation. In short, we’re a machine learning platform for time series data. And nowcasting is using data up to the immediate time period to get a quick snapshot on what the near-term future holds. You can do a nowcast weekly, daily, hourly, or minutely, and the purpose is really just to understand what’s happening in markets or in a company or whatever your outlook is right now
And what sort of data do you use to fuel these predictions?
We use largely publicly available datasets. And we’re using billions of data items in our platform to understand how the world works…Macroeconomic data is probably the least reliable data that we use, so we use it for maybe a directional look, at best, at what’s happening. Currencies data is probably the most accurate data that we use, because currencies trade in such narrow bands. We use commodities data, from widely traded ones like oil and gold, to more obscure ones like molybdenum and some industrial metals. We’re also looking at individual equities and equity industries, and we track things like shipping times for goods—shipping times…are usually pretty good indicators of price rises.
Who are your clients, and how are the nowcasts used in practice?
Our clients range from investors and portfolio managers, to healthcare firms and manufacturing firms, to mining and natural resources firms. So they want to understand what the environment looks like for their, say, investment or even procurement—for example, how the current inflation environment affects the procurement of some part of their supply chain.
In fact, we’re talking to a healthcare company right now, and they want to nowcast over the weekend for some of their key materials. In an investment environment, of course, people would want to understand how, say, expectations and other variables impact the outlook for the near-term future, like, days or a week. People are also using us for continuous budgeting—so revenue, budgeting, expenses, CFOs, and heads of financial planning are using us…to understand the 12- to 18-month outlook of their business, [so they don’t have to have an annual budgeting cycle].
Tell me about how the AI works—which kinds of models you’re using, whether you’re using deep learning, etc.
There are basically three phases to our AI. During the pre-process phase, we collect data and look for anomalies, understand data gaps and how data behaves, classify data, and those sorts of things.
Then we go into a forecasting phase, where we use what’s called an ensemble approach: multiple algorithmic approaches to understand the future scenarios for whatever we’re forecasting. Some of those algorithms are longer-term and fundamentals-based, some of them are shorter-term and technical-based, and some of them are medium-term. And we’re testing every forecast item on every algorithm individually and in a common combinatorial sense. For example, we may forecast an asset like gold using three or four different forecast approaches this month, and then using two forecast approaches next month, depending on how the environment changes
And then we have a post-process that really looks at what we’ve forecasted: Does it look weird? Are there obvious errors in it—for example, negative numbers or that sort of thing? We then circle back if there are issues…We’re retesting and re-weighting the methodologies and algorithms with every forecast that we do.
We’ve had very unique market conditions over the past two years. Since AI is trained on data from the past, how have these conditions affected the technology?
You know, there’s a lag. I would say that in 2020, we lagged the market changes by about six weeks. It took that amount of time for our platform to catch up with the magnitude of change that had happened in the markets. Now, back then, we were not iterating our forecasts more than twice a month. Since then, we’ve started to reiterate our forecasting much more frequently, so that the learning aspect of machine learning can really take place. But we’ve also added daily interval forecasts, so it’s a much higher frequency of forecasting and in smaller intervals, because we can’t rely on, say, monthly intervals as a good input in an environment this volatile.
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.
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.
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.
Complete Intelligence – a fully automated and globally integrated AI platform for smarter cost and revenue planning.
Complete Intelligence provides actionable, accurate, and timely data to make better investment and procurement decisions.
The platform provides an integrated global model to ensure that actions in one market, country, or sector of the economy are reflected elsewhere in markets, industries, and the global economy. International trade, economic indicators, currencies, commodity prices, and equity indices are all factored in to create a proxy of the global economy. Over 1200 industries in more than 100 countries are covered!
Based on interviews with Tony Nash, founder, CEO, and Chief Data Scientist, this brief report introduces Complete Intelligence, one of a growing number of highly innovative companies supported by the Oracle for Startups program. The company, founded in 2019, is already significantly improving the forecasting and budget planning of a variety of large corporations through its advanced AI-driven intelligence platform. The theme for this month is around startups in the energy and utility sector and how they are innovating, changing the competitive landscape, and contributing to sustainability. CX-Create is an independent IT industry analyst and advisory firm, and this report is sponsored by the Oracle for Startups program team.
The business context for Complete Intelligence
Commodity price volatility and a post-pandemic surge in demand drive the need for more timely and accurate forecasting Businesses coming out of lockdown have increased demand for commodities, from energy supply to raw materials for their products. In Europe, benchmark prices for natural gas to power their factories and heat their buildings have risen from €16 megawatt-hour in January 2021 to €88 in October. This, in turn, has sent electricity prices soaring. (Source: Euronews). While some have locked in prices through forward-buying, others have been exposed and seen profit margins plummet, unable to pass on price hikes to their customers.
But it is not just energy prices that are volatile. Semiconductor chip shortages have impacted many industries that depend on them, from automotive to electronic household goods manufacturers, putting a brake on their post-pandemic recoveries despite strengthening demand.
The growing demand for clean and sustainable energy sources and precious metals, like copper and lithium that power batteries have also seen tremendous volatility. As major industrial companies digitally transform their organizations and business models seeking elusive growth, the importance of data and AI are increasingly recognized as fundamental to success.
Forecasting and budgeting needs data science, not spreadsheets The ability to sense change, respond quickly and adapt rapidly relies on a synthesis of massively increased volumes and varieties of data, both from operational and external sources. Data volumes are too complex for manual approaches and spreadsheets and require AI to extract insight and meaning from this complex array of external demand and supply signals. The old industrial-age planning approaches can’t cope. They are too slow, involve armies of accountants and analysts, and political wrestling between departmental heads, and are often based on opinion and inaccurate forecasts leading to erroneous budgeting decisions.
Complete Intelligence provides the accurate evidence base for budgeting and forecasting decisions
When markets are relatively calm and stable, the cycle of annual planning and budgeting makes sense. But amidst continual volatility and dramatic accelerated change, the planning cycle is too slow. It fails to mitigate the risks unfolding at such speed and is impacted by a confluence of so many variables, like extreme weather, scarcity of raw materials, pandemics, and weakened supply chains. An array of intelligent internal and external feedback loops is needed to mitigate risks and optimize resources in pursuit of the company’s goals. This is what Complete Intelligence provides with its integrated and modular intelligence platform.
• Complete Intelligence provides the accurate evidence base for budgeting and forecasting decisions • The Complete Intelligence Platform consists of three modules – CI Futures, RevenueFlow and CostFlow • Forecast accuracy has rapidly improved, and error rates are now around 2%, which compares favorably with traditional methods and error rates of 35% or more
Complete Intelligence, the story so far
Tony Nash, founder, CEO, and Chief Data Scientist, is steeped in market intelligence. A former VP of market intelligence firm IHS (now IHS Markit), and The Economist Intelligence Unit, where he was Global Director Consulting and Custom Research. He observed that large international companies he had supported typically followed an annual budgeting cycle based on often inaccurate or opinion-based data. It was not unusual to find large teams of people, sometimes several hundred involved in the process and heavily reliant on gathering data from multiple departments in complicated spreadsheets. The process could last several months, and the variance between forecasts and actuals was often above 35%, which could erode profits or tie up resources unnecessarily.
Trial, error, and persistence As a data scientist familiar with cloud technologies, he developed algorithms to improve forecast accuracy and a complete process from data ingestion to forecasting and testing the results. He started developing the machine learning ML algorithms in 2017 while still consulting in Asia from his base in Singapore. His first iteration failed to produce a level of accuracy that would provide a sufficiently compelling proposition. He wanted to get down to an error rate of no more than 5%-7%. He adopted the ‘ensemble’ approach covering thousands of different scenarios layering external data on commodities such as the copper price with a customer’s actual costs, identified in their general ledger.
Ready for launch late 2019 In 2019, Nash returned from Singapore and set up his company in The Woodlands, near Houston, Texas. He continued his work on the algorithms and developed a commercial product ready to launch in early 2020. And then Covid-19 struck.
Through Covid-19, companies first tried to understand the changing environment, then remained risk-averse until public health, business environment, and supply chains became more stable. This has been a challenge for a cutting-edge machine learning firm like Complete Intelligence. It is only as the environment has begun to stabilize that enterprises have sought new solutions to legacy problems. With that has come a renewed interest in Complete Intelligence and deployment at a large scale.
Solution overview The Complete Intelligence Platform consists of three modules The Complete Intelligence Platform hosted on Oracle Cloud Infrastructure (OCI) consists of three forecasting modules:
•CI Futures – to forecast market trends. Covering over 1,400 industries in more than 100 countries and a database of over 16 billion data points from proprietary and publicly available data. Millions of learning algorithms are used, which factor in the most recent global events.
• RevenueFlow – provides accurate results for demand and forecast sales and revenue projections.
• CostFlow – to enhance product line profitability and improve supply chain and procurement outcomes.
Figure 1. provides a diagram of the Complete Intelligence Platform
Figure 1: Complete Intelligence Platform by Complete Intelligence.
Market data is ingested from multiple trusted data sources like national statistical agencies, multilateral banks, multilateral government bodies, commodities exchanges, bilateral trade bodies and combined with the client’s data from their general ledger. A multi-layer testing and validation process used to ensure the accuracy of the data to be used in any forecast. Third-party data is gathered via internet spiders and APIs.
The platform provides an integrated global model to ensure that actions in one market, country, or sector of the economy are reflected elsewhere in markets, industries, and the global economy. International trade, economic indicators, currencies, commodity prices, and equity indices are all factored in to create a proxy of the global economy.
A comprehensive list of futures, currencies, and market indices is covered and accessed through a highly graphical and easy-to-use interface. Almost 1,000 assets, with historical data from 2010 and forecasts over a one-year horizon, are provided. More assets are being added all the time.
The platform is designed around three attributes: • A globally integrated model • A data-driven process without human intervention in the output • A simple means of interfacing with the platform.
The platform can be connected to existing ERP systems and automatically upload pricing data from the general ledger at a very granular level for each item.
The Complete Intelligence Platform supports a variety of use cases: • Supply Chain & Purchasing Optimization – help lower costs, anticipate risks, and provide input to sourcing strategies. • Sales and market entry strategies – by identifying higher growth markets and optimizing resources • Strategic Financial Planning – identifying growth markets and fine-tuning resource allocations in each market to minimize exposure to currency fluctuations. • Mergers and acquisitions – provide a snapshot of cost structures and projections of future costs and profitability of target acquisitions.
Forecast accuracy has rapidly improved, and error rates are now around 2% Nash’s persistence has resulted in significant levels of forecasting accuracy. A twelve-month forecast now sees error rates around 2%, which gives users considerable confidence compared with traditional methods, where the error rates are often above 35%.
As well as dramatically improving forecast accuracy on markets, revenues, and costs, the onboarding process to going live is a matter of a few weeks. After that, forecasting takes hours, not months.
Successes to date
While still a relatively new company, Complete Intelligence has already proved its value to several large companies.
• A major petrochemical company wanted to improve its predictive intelligence capability for feedstocks and refined products. They asked Complete Intelligence to examine nine categories across crude oil, gasoline, diesel, natural gas, and gas-to-liquid (GTL) products. Monthly forecast averages are provided by category with extremely low differences from actual results on the order of 3% or less.
• A global furniture company wanted a more explicit link between their sales and revenue planning and their sales teams in China. Complete Intelligence built a sales forecasting model that more clearly identified and utilized market demand drivers and connected these directly to their business. An analytics-based approach to identify the drivers of sales by city and industry. Complete Intelligence built a city and industry-level forecasting tool that determined the company’s growth trajectory and provided recommendations to support the direction and transition of their sales teams. • A global chemicals company needed a better understanding of the trends for costs in their supply chain and a more precise way to manage margin expansion and contraction at the bill of material level. Complete Intelligence was commissioned to forecast factor inputs and currencies for the key categories. The forecasts were calibrated based on the component make-up of the bill of materials. This enabled the client to identify the direction of the materials pricing and the impact on their BOM. Through the process, the client learned how to anticipate cost movements and protect margins.
Current go-to-market model
Complete Intelligence sells directly to large organizations, mainly targeting CFOs and COOs with a broad view of their companies and strategic decisions.
The company also has strategic partnerships with Microsoft and is listed on the Azure Marketplace and with Oracle as part of the Oracle for Startups program and hosted on OCI.
Other partnerships with Bloomberg and Refinitiv allow for exchanging financial and market data and connection to their platforms.
More transparent accuracy reporting so customers can view accuracy/error for every line item
More robust and flexible data visualization for clients to utilize Complete Intelligence forecasts within their visual narratives
More sophisticated data science to account for detailed sentiment and other qualitative factors
Do-it-yourself forecasts for customers to do ad hoc forecasts for any data at any time. This will enable teams within a company to do their own sophisticated, reliable forecasts without waiting on their in-house market analysis or forecasting team with complicated macros and massive spreadsheet workbooks
Embedding Complete Intelligence forecast APIs into ERP and accounting software.
Oracle Cloud Infrastructure and the Oracle for Startups program prove their value to Complete Intelligence When asked what he felt about the relationship with Oracle and the Oracle for Startups program, Nash said, “Oracle Cloud Infrastructure is very flexible and secure. The Oracle for Startups team has been great. Oracle has been the most responsive and helpful of all our partnerships, connecting us to the right people to help with marketing, sales, or technical questions. I really feel that they want us to succeed. I’m a huge advocate of the Oracle for Startups program.’’
CX-Create’s viewpoint The Complete Intelligence Platform addresses a fundamental business need
Providing a global proxy model on markets, commodities, currency fluctuations, and many other aspects and making this easily accessible for business people will significantly improve strategic investment and procurement decisions. The emphasis on accurate and timely data supported by ML models will make it easier for business people to make informed decisions, stripped of personal bias. Digital transformation should lead to a more agile and responsive organization. The more progressive organizations will want highly attuned external signals that are constantly updated, enabling them to de-risk investment decisions and optimize resources for growth. Complete Intelligence provides for that.
Our engine also forecasts currency pairs for Forex, different equity indices in the world, and economic indicators like GDP and inflation. For a complete list of what we forecast, please go to the Forecast Assets list. 88.6% of the items we forecast have more than 95% accuracy.
Richard Lichtenstein of Bain & Company joins us this week to talk about advanced analytics. What is it actually and how can companies and private equity firms use this to make better business decisions? He also shares some B2C and B2B examples and use cases. Also, what are some common barriers for companies to incorporate advanced analytics to their toolset?
Richard Lichtenstein is an expert partner at Bain & Company in New York. He has been at Bain for 17 years and he leads their efforts around advanced analytics and private equity. To get in touch with Richard, please email him at Richard.Lichtenstein@bain.com.
This QuickHit episode was recorded on September 10, 2021.
The views and opinions expressed in this Here’s how Bain uses alternative data and AI to solve businesses’ biggest problems QuickHit episode are those of the guest and do not necessarily reflect the official policy or position of Complete Intelligence. Any contents provided by our guest are of their opinion and are not intended to malign any political party, religion, ethnic group, club, organization, company, individual or anyone or anything.
TS: For people that might not be familiar with advanced analytics or what that entails, can you kind of give us an overview of what this encompasses?
RL: At Bain & Company, we have a team of over 50 people that thinks about just how can we use advanced analytics to serve private equity? And so what do all these people do?
Well, we’ve got a bunch of people scouring the world trying to find the latest and greatest and interesting data sources that we can use. And those could be B2B or B2C. And I can talk more about what those are. Right.
Then we have teams of data cleaners, because sometimes those data sources are really messy on credit card data, and they specialize in cleaning them and making them usable for analysis.
Then we have a group of data scientists who are building Python libraries that they can use to take that data and run fairly sophisticated analysis on these over and over again. So these might be looking at retention by cohort or customer lifetime value or understanding switching behavior and things of that nature.
Then we have a group that takes that output and builds ways to automatically turn that in slides or into tableau so that we can get that in front of clients quickly in a form that brings out the insights.
And then lastly, we have some people who just help other people at Bain figure out how to use all this stuff. We get about over a thousand requests a year from teams trying to figure out which tools should I use? Which data source should I use? Et cetera. And so we just have to help them figure out how to do it.
TS: Can you give us some of your use cases, maybe go into a little bit more detail?
RL: Yeah, of course. So in a way, it’s quite different for B2B and B2C, but both of them have a lot of good advanced analytics examples. If we start with B2C, in that environment, there’s a number of interesting alternative data sets that we leverage, things like credit card data, things like e-receipt data. These show us what people are buying online. Sometimes what people are buying in store, where they go. But it goes beyond some of the traditional data sources, like Nielsen and IRI, and actually shows you what customers are doing. What happens at the customer level. And that allows you to learn some really interesting things.
So, for example, we’ve done some work recently with a fast food restaurant chain. And they’re trying to figure out why are we losing share? We were able to see well among people who are going to your restaurant less often or stopped going, a lot of them were going to Chick-Fil-A. And this isn’t a restaurant that sells chicken. So they hadn’t really thought of them as a competitor. But they are. And that was news to them. Or we did similar work for a coffee chain, and they thought they were losing to McDonald’s on the low-end for coffee. But it turned out, actually, that Starbucks was also a threat to them on the high-end. That help them to figure out strategy. But for private equity investor in these companies, it tells them a lot about the business and where to go.
TS: You wrote a piece last year on like Wayfair and how they used advanced analytics to understand that it was like on the precipice of rapid growth. So what kind of other data or companies using to better understand their market?
RL: Yeah. The Wayfair analysis was really quite interesting. So it’s a great example here. So that’s. And in that case, it was understanding the customer behavior that we were seeing. This was early in COVID, right before the huge spike that we all know now happened. And we were just seeing people coming to Wayfair for the first time. We had never been there before, buying stuff. We were seeing people coming back with great retention. And we were able to observe these kinds of customer metrics at completely outside in.
And that gave our client confidence to make an investment there. One of the other ways we can use analytics there. So we’re working with, you know, another company that’s in a similar space. And so one of the things you can see with this data is because you can see what people are actually buying, you can see what they’re buying from the competition.
So, for example, you could see what are customers who like to shop on Wayfair buying at Overstock or buying a Target or IKEA. And then you could say, Well, if you’re Wayfairer, you then say, well, maybe we need to stock those products. Right. So maybe we should think of adding them. Or maybe we had a stock out on that product for a little bit. And that cost us a business. And so we need to think about our inventory.
And so you can quickly. You can quickly think about your customers differently. At the same time if you’re a brand, obviously, you can use this data to get much better analytics than you ever could about who’s buying your products. Because previously, if you’re a brand and you’re selling online, you don’t know anything about your customers. And now you can start to understand loyalty and things like that.
TS: Have you found any big issues for companies using advanced analytics like it’s hard to access data. It seems fairly sophisticated. So is there a barrier to understanding this kind of data and how it’s presented?
RL: Yeah. I mean, I would say it’s not really for the faint of heart in terms of diving into advanced analytics. If you’re an individual company or an individual private equity firm, it’s hard to really dive in to the degree that we have for a few reasons.
One is there’s a lot of data sources out there. If you go to one of these conferences, there are hundreds of these sources out there, and then there’s more even if you don’t even go to these conferences, right. There’s a lot of sources. It’s hard to figure out which ones are good, which ones really have sufficient sample size and data quality. And these sources also come and go.
Sometimes you might have a source that you really like, and sometimes they disappear or the quality degrades. And what have you. And so you need to maintain a rotating stable of sources. And you need to think a lot about sourcing them. And again, we have people whose job is just to figure that out, which is hard for an individual company to do. And then you also need armies of people to figure out how to use the data in productive ways.
Again, at Bain, we’ve set all that up, but there is a high fixed cost associated with it. And so I think it’s a little self-serving. But I think my view would be that if you’re a firm and you want to get your feet wet in this kind of data, you’re better off partnering with a company like us, like Bain & Company or someone else who’s already got all this figured out and see what insights are possible. What can I really learn doing this? How can this help me make smarter business or investing decisions?
And then once you’ve figured that out, then sort of and you got a narrower focus, then figure out how can you get that on a recurring date? Get a feed of that on a recurring basis versus trying to start from scratch.
TS: Right. That absolutely makes sense. Did you have anything else that you wanted to add to give us any broader scope of your company?
RL: Yeah. The one other thing I might mention, it’s easy to get. And I mean, I just fell into this trap. It’s easy to get sucked into the B2C examples because they’re so enticing and easy to under stand. But I do think there is a lot of exciting work and B2B that we see. And so just to give a couple of quick examples.
One, I think is around people analytics. So that’s an area that’s really come a long way in the last few years. And there’s a lot you can do outside and to understand at a company who works there, what those people do, what’s their turnover and how does that change? And that’s actually enabled a lot of interesting insights. Just to give an example that we did a recent diligence on a software company that served, did a complex sort of B2B type of software.
And the company we looked at was cloud native, and there was a legacy software provider in the space who had been there forever and was slowly developing cloud functionality.
And there was a big question of, well, how fast are they going to catch up? At the moment the cloud native company was ahead. But obviously, the question is could they maintain advantage forever? And so we just looked at the people data, and we saw that our target, the cloud company had a hundred people there and software engineers doing R&D, and the legacy company had 200 people doing it. And so I mean, you sort of figure, well, if one company’s got 200 people and one’s got 100, the 200 person, and it’s going to catch up at some point.
RL: And I don’t know if it’s in a year or two years, but certainly within the holding period, you have to worry about them reaching parody. And that was not a super complicated insight, but one that had a big impact on thinking about the investment. And if you bought the company, what kind of investment in R&D is required? Just an example.
TS: I was actually I was looking at your site. What is the founder’s mentality?
RL: So that’s a great question. I mean, I will admit, I’m not the expert on founder’s mentality. That was a book that Jimmy Allen wrote. That’s a great book. And if you can get him on your show, he’s far more articulate on this than I am.
But the idea of the founder’s mentality is that, you know, founders can bring a certain sort of secret sauce to their companies and create a dynamic and innovative culture. And that once they leave, sometimes that dynamism can erode and things can become more bureaucratic and ossified. And it can be harder for companies to innovate.
And I think that that is actually, it’s interesting you mention that because this is actually something that’s come up in some of the work that I’ve been doing. One of the ways you can apply this data is in sourcing. So you can help a fund scan the ocean of companies out there and find, you know, of the millions and millions of companies, here’s a sector that’s interesting. And here’s a sub sector. And then within that here are companies that meet our specific thesis and so forth.
One type of thesis that we see sometimes is they’re interested in companies that are still led by the original founder or sometimes they’re interested in companies where the founders just left very recently. And there is an opportunity to think about the culture in a different way.
And we’ve actually built some tools that allow you to look at which companies have founders that have just recently left right. And that was something that at least the fund that we worked with on that, that was very exciting as they look for opportunities. So anyway, that’s the concept. And that’s at least how it fits into my world.
TS: I got it. It seems very interesting. And did you have anything else? We’re going to wrap this up here in a minute. So did you have anything else you wanted to add?
RL: No. I mean, I think we covered the main point. The main thing I would just say to people who are thinking about this is the world of alternative data is really exciting. And the insights that are possible today that just we’re not possible even a year ago.
So it’s really moving fast. We’re signing a new data source practically every month, at least. So it’s great. But it’s also very complicated and tricky and hard to navigate. And again, it sounds self serving. But we strongly recommend that if you’re waiting into this for the first time, you talk to people like us at Bain & Company to really understand specifically how this stuff can help, because often it’s hard to sort of just talk to a data provider. And then from that conversation, really figure out if they’re going to be the right fit. So anyway, we’re here to help, of course.
TS: If people want to contact you, how would they go about contacting you or.
RL: Sure. I mean, I’m happy to have someone reach out to me. I’m certainly here to talk to anyone who wants to think about this, how they can use alternative data. It’s Richard.Lichtenstein@bain.com is an easy way to get in touch with me. And I’m happy to talk to anyone again who wants to think about this stuff.
So thanks for the time, Tracy. Really appreciate it. And hope somebody out there who sees this gives me a call.
TS: Absolutely. Thanks again, Richard. We really appreciate everything you’ve shared with us today.
And for everyone watching, please don’t forget to subscribe to our YouTube channel, and we look forward to seeing you on the next QuickHit.
Here’s a mathematical problem: The sum of all the individual country GDPs never equals the global GDP. That means forecasting models are flawed from the start, and it’s impacting global supply chain economics in a big way. Entrepreneur Tony Nash found that unacceptable, so he built an AI platform to help businesses “understand the sum of everything” through a highly automated, globally data-intensive solution with zero human bias.
Complete Intelligence, Nash’s Houston-based startup, uses global market data and artificial intelligence to help organizations to visualize financial data, make predictions, adjust plans in the context of a global economy, all on the fly. The globally-integrated, cloud-based AI platform helps purchasing, supply chain planning, and revenue teams make smarter cost and revenue decisions. It’s a way on how to make better business decisions.
“The machines are learning, and many times that has meant deviating from traditionally held consensus beliefs and causality models,” said Nash. “Causal beliefs don’t hold up most of the time—it’s human bias that is holding them up—our AI data is reducing errors and getting closer to the truth, closer to the promise of superforecasting.”
Massive datasets across 1,400 industry sectors
More than 15 billion data points run through the Complete Intelligence platform daily, making hundreds of millions of calculations. Average business forecasting saas software models use 10-12 sector variables. Complete Intelligence, on the other hand, examines variables across 1,400 industry sectors. The robustness gives businesses insights and control they didn’t have before.
“We’ve seen a big shift in how category managers and planning managers are looking at their supply chains,” said Nash. “Companies are taking a closer look at the concentration of supply chains by every variable. Our platform helps companies easily visualize the outlook for their supply chain costs, and helps them pivot quickly.”
Superforecasting brings a modern mindset to an old industry
Australia-based OZ Minerals, a publicly-traded company, is a modern mining company focused on copper with mines in Australia and Brazil. OZ says their modern mantra is more than technology, it’s also a mindset: test, learn, innovate. They wanted to better navigate and understand the multi-faceted copper market, where the connectivity between miner, smelter, product maker, and consumer is incredibly complex and dynamic. They turned to Complete Intelligence.
“I need a firm understanding of both fiscal and monetary policies and foreign exchange rates to understand how commodity prices might react in the future because a depreciating and/or appreciating currency can impact the trade flows, and often very quickly, which might influence decisions we make,” said Luke McFadyen, Manager of Strategy and Economics at OZ Minerals.
“Our copper concentrate produced in Australia and Brazil may end up being refined locally or overseas. And then it is turned into a metal, which then may be turned into a wire or rod, and then used in an electric vehicle sold in New York, an air conditioner sold in Johannesburg, or used in the motor of a wind turbine in Denmark,” he explains. “The copper market is an incredibly complex system.”
With Complete Intelligence, McFadyen has a new opportunity to test for a bigger-picture understanding and responsiveness. Previously, he updated his models every few months. Now he could do it every 47 minutes if he needed to.
McFadyen points to the impact of COVID-19 as a “Black Swan” event that no business forecasting saas software could have predicted, but is nonetheless impacting currencies, foreign exchanges, and cost curves throughout global copper market and supply chains.
“If your model isn’t dynamic and responsive in events like we are experiencing today, then it is not insightful. If it’s not insightful, it’s not influencing and informing decisions,” he said. “Complete Intelligence provides a different insight compared to how the traditional price and foreign exchange models work.”
McFadyen says early results have reflected reductions in error rates and improved responsiveness.
Cloud power and partnership
Complete Intelligence needed a strong technology partner but also one with global expertise in enterprise sales and marketing that could help boost their business. They found it with Oracle for Startups.
“We have lots of concurrent and parallel processes with very large data volumes,” said Nash. “We are checking historical data against thousands of variables, anomaly detections, massive calculations processing, and storage. And it’s all optimized with Oracle Cloud.”
Nash, who migrated off Google Cloud, says Oracle Cloud gives him the confidence that his solution can handle these workloads and data sets without downtime or performance lapses. The partnership also gives him a credible technology that is native to many clients.
“As we have potential clients that come to us that are using Oracle, having our software on Oracle Cloud infrastructure will make it easier for us to deploy and scale. A seamless client experience is a critical success factor for us.”
Nash says the Oracle startup program‘s free cloud credits and 70% discount has allowed them to save costs while increasing value to customers. He also takes advantage of the program’s resources including introductions to customers and marketing and PR support.
“We’ve been impressed by the resources and dedication of Oracle for Startups team,” he said. “I’d recommend it, especially for AI and data startups ready for global scale.”
Beyond mining: superforecasting futures with AI
Beyond mining, Complete Intelligence is working with customers in oil and gas, chemicals, electronics, food and beverages, and industrial manufacturing. From packaging to polymers and sugar to sensors, these customers use Complete Intelligence for cost and revenue planning, purchasing and supply chain proactive planning, risk management, and auditing teams, as well as general market and economic forecasts.
The error rates for Complete Intelligence forecasts in energy and industrial metals performed 9.4% better than consensus forecasts over the same period, and Complete Intelligence continues to add methods to better account for market shocks and volatility.
OZ Minerals’ McFadyen said, “This is the next step in how economists can work in the future with change leading towards better forecasts, which will inform better decisions.”
Nash and Complete Intelligence are betting on it – and building for the future.
“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.
In just 2 minutes, you’ll learn why superforecasting is so much better than forecasting. Hear how automated, data-intensive AI with no human bias can help make predictions and adjust strategy on the fly, and how startup Complete Intelligence is making it happen.
Is forecasting enough when you need to analyze and take action? Meet the startup that says “no.” What’s needed is superforecasting.
Hi, it’s Mike Stiles, and this is Meet the Startups for the week of August 26th, brought to you by Oracle for Startups.
How can you be happy with forecasting when there’s something out there called superforecasting?
Startup founder Tony Nash and his company, Complete Intelligence are making super forecasting possible with a highly automated, data intensive A.I. solution.
Part of what makes it so SUPER is there’s zero human bias. No spin or wishful thinking allowed.
Complete intelligence is helping organizations visualize financial data, make predictions and adjust strategy on the fly. That gets you things like smarter purchasing, better supply chain planning, smarter cost and revenue decisions.
To get where they needed to be on performance and price, the company moved from Google Cloud to Oracle Cloud. That did it. Computing is at peak performance and Complete Intelligence’s global customers are reaping the benefits. That’s super.
We asked Complete IntelligenceCEO Tony Nash what this pandemic has done to forecasting and supply chains.
”We’ve seen a big shift in how managers are looking at their supply chains. As a result of Covid-19, companies are eager to understand their cost and revenue risks, things like concentration risk and the timing of their cost, that sort of thing. We’re helping our customers with timely and accurate information to make smarter cost and better revenue planning decisions.“
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