CI Futures is a globally integrated cloud-based AI platform that provides accurate market forecasts for over 1,200 assets including 700 currency pairs, commodities, market indices, and economics.
“With the addition of all stocks in the S&P 500 to our CI Futures platform, we are continuing to lead the market in providing reliable, accurate, and comprehensive financial forecasting,” said Tony Nash, CEO and Founder of Complete Intelligence. “This expansion will give our clients even greater insights to make informed long-term investment and trading decisions.”
CI Futures is already used by leading financial institutions, corporations, and investors around the world.
Besides CI Futures, Complete Intelligence also offers RevenueFlow™ and CostFlow, designed to provide companies with reliable, automated forecasts of revenues, costs, and expenses to become more efficient and profitable.
RevenueFlow™ augments and accelerates the budgeting process with AI while improving accuracy and profitability. It transforms the annual budget process and transitions to continuous monthly forecasting to eliminate the disruptive annual budget drama.
CostFlow™ streamlines planning and reduces costs with AI-driven expense forecasting. With a transparent, organized, and accurate planning platform, teams can forecast costs and expenses with ease.
About Complete Intelligence Complete Intelligence Technologies, Inc (CI) is a Houston-based company that offers AI-powered financial forecasting and planning solutions to businesses and investors worldwide. Its flagship platform, CI Futures, is a globally integrated cloud-based AI platform that provides accurate market forecasts for over 1,200 assets, including all S&P 500 stocks, commodities, market indices, and economics. The company also offers RevenueFlow™ and CostFlow™, which provide automated forecasts of revenues, costs, and expenses to improve efficiency and profitability. With Complete Intelligence, businesses and investors can make informed decisions and stay ahead in finance.
Contact:
Complete Intelligence Rick Nash info@completeintel.com
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.
Tony
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.
Tony
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.
Tony
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?
Tony
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?
Tony
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.
Tony
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.
Tony
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.
Tony
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.
Tony
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.
Tony
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.
Tony
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?
Sam
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.
Sam
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.
Sam
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.
Tony
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.
Tony
CI Futures is our subscription platform for global markets and economics. We forecast hundreds of assets across currencies, commodities, equity indices, and economics. We have new forecasts for currencies, commodities and equity indices every Monday morning. We do new economics forecasts for 50 countries once a month. Within CI Futures, we show you our error rates. So every forecast every month we give you the one and three month error rates for our previous forecast. We also show you the top correlations and allow you to download charts and data. You can find out more or get a demo on completeintel.com. Thank you.
Sam
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.
Sam
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.
Tony
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?
Sam
Sure.
Tony
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.
Sam
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.
Tony
So it’s demographics, wages, participants, demographics, wages.
Sam
Demographics change slowly than all at once. It’s not as though you can simply incentivize the demographics to change. Right?
Tony
Exactly.
Sam
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.
Tony
Yes. Okay. Good points. Okay, so let’s move from the kind of context and thanks for that, Sam.
Tony
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?
Todd
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.
Todd
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.
Todd
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.
Tony
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?
Todd
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.
Todd
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.
Tony
What does that mean, second read? Can you walk us through that process? Yeah.
Todd
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?
Todd
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.
Tony
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?
Todd
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.
Todd
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.
Tony
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.
Tony
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.
Tony
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?
Chris
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.
Chris
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.
Tony
So is it at least at this phase, is it more augmentation than it is automation?
Chris
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.
Chris
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.
Tony
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?
Tony
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.
Chris
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.”
Tony
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?
Todd
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.
Todd
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.
Tony
Right.
Todd
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.”
Todd
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.
Sam
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.
Sam
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.
Tony
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.
Tony
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.
Tony
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.
Chris
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.
Todd
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.
Chris
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.
Sam
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.
Tony
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.
Todd
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.
Tony
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?
Sam
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.
Tony
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.
The European Union will allow members to offer subsidies that match those offered by the US Inflation Reduction Act to prevent an exodus of green energy projects. The White House’s $369 billion initiative has been criticised by many countries, which fear it could attract local companies to move across the Atlantic.
Roger Hearing discusses this and more business news with two guests on opposite sides of the world: Stefanie Yuen Thio, joint managing partner at TSMP Law in Singapore, and Tony Nash, chief economist at Complete Intelligence in Texas.
Tony Nash, CEO and founder of Complete Intelligence, joined BBC Business Matters podcast, to discuss a range of topics from autonomous vehicles to green energy subsidies.
Nash shared his thoughts on the future of AI and autonomous vehicles. He discussed the challenges of ensuring self-driving cars can navigate changing road conditions and the safety concerns that come with autonomous driving. Nash also discussed the potential of AI in the transportation industry and the need for continued development in this area.
Nash also provided insights on Joe Biden’s tax plan, specifically focusing on corporate taxes and unrealized gains tax. He discussed the potential impact of the tax plan on companies and individuals and offered alternative solutions to the proposed policies.
Nash also discussed the transatlantic race for green energy subsidies in another episode. He explored the role of government grants in spurring innovation in the green energy industry and discussed the challenges facing countries caught in the middle of geopolitical forces. Nash also highlighted the importance of consumer pressure in driving environmentally friendly products.
Transcript
BBC
Hello, and welcome to Business Matters. I’m Roger Hearing. Coming up on the program today, the European Commission is allowing member states to subsidize companies with green energy projects. They’re trying to forestall a drift of such firms to the US. Where state aid is already in place. Also, as pro Western protests go on in Georgia, we take a look at the strength for the economy in a country that really desperately wants to join the European Union. President Biden’s budget plan see a big tax rise for rich individuals and companies. So how’s that going to go down?
Stephanie
What he’s promising is we’re going to have European style benefits, but still have incredibly progressive taxes, and that’s just not realistic.
BBC
And self driving cars are on their way, but how can we make them safe on crowded urban roads? And I will be joined throughout the program by two guests on opposite sides of the world. Stefanie Yuen Thio, who’s joint managing director at TSMP Law Corporation, is joining us from Singapore. And Tony Nash, founder of the AI firm Complete Intelligence, joining us from Houston, Texas. So clearly, Tony, let me come to you and ask, well, what’s going on down in Texas at the moment?
Tony
Hey, Roger. Well, we have the Houston Rodeo, which is the largest rodeo in America, and it sounds like a throwback, but it’s actually a really big deal. They raise about half a billion US. Dollars for scholarships for Texas students. So it’s a big deal here in Houston, and it sends a lot of kids to university.
BBC
Yeah, and worth watching, too, I imagine, isn’t it?
Tony
Yes, it is. Yes, sir.
BBC
But you don’t take part, I imagine, Tony. I mean, the picture in front of my mind at this moment is quite.
Tony
Last year, but I’m not good for 8 seconds on a horse, so I’ll just sit in sidelines.
BBC
The let’s hope you’re good for 60 minutes on the radio, and I’m sure you will be. Anyway, welcome both. Let’s first of all talk about what’s happened here in Europe, because really it’s a transatlantic issue. But Europe has moved to try and level the playing field for companies there who want to set up green energy projects. There’s been fears that very generous new subsidies for US firms brought in by President Biden would drain Europe of green energy projects as businesses moved across the Atlantic to take advantage of what was over there. Well, now the European Commission has relaxed the rules on state aid for projects aimed at speeding up energy storage and the use of renewable energy and wants that take out carbon from industrial processes. EU member states will have until the end of 2025 to set up their schemes. What’s your take on this? It’s your side of the Atlantic that has really upped the ante on this with the Inflation Reduction Act covers a multitude of things, but one of them is this enormous amount of subsidy, over $300 billion, and then it starts this war with the EU over it, really.
Tony
So, Roger, the first thing I want to do is start a green energy company to game both sides of the subsidy plan. Right. So I think it’s interesting. It started in the US and obviously it’s just a truckload of money, and like everyone has said, it’s just a race to get somewhere. And I think it’s really hard to believe that this race is a credible one when Germany is burning more coal than they have in decades. Right. So I think that is it going to stimulate innovation? I don’t think so, because it’s grants, right. These are grants that are being given out by government, which I think I.
BBC
Don’ think they’re necessarily direct grants. Some of them may be, but it’s a mixed picture, I think.
Tony
Yeah, it’s mixed. And so those grants will be the first to go and they’ll be given very inefficiently, and then the tax credits or the other things that are done, if they’re in small batches, then they could kind of engender some competition. But if there are very large tax subsidies to be given, then it’s just going to be pigs at a trough. That’s all it’s going to be here in the US, in Europe. Europe is not unique. It’s the same thing here.
BBC
Well, indeed, but at the same point, I’ve put to Stephanie, I mean, isn’t in the end, Tony, the problem that you can’t leave it up to the market to do something that actually matters much longer term than most markets really have anything to do with?
Tony
Oh, well, you can. When you look at emissions, the US has been well ahead of kind of targets for years, because for the most part, we’ve had markets that haven’t subsidized kind of inefficient companies to do this. Of course, we have companies like Cylindra, which was a big story 15 years ago or something, and other wasteful green tech companies. But for the most part, when you look at, say, the US auto industry, other industries, they’ve done they’ve worked very, very hard to reduce emissions. And the US auto industry, even on petrol-fuelled cars, has done an amazing job at reducing emissions. And of course, there are subsidies that go to US automotive makers, but they’re not new and they’re not a large part of the revenues that those auto makers get.
BBC
What’s the incentive for them to do this? Because there has to be some incentive.
Tony
Consumers want it.
BBC
Consumer pressure.
Tony
Why do people make a car Blue? Or why do people put a Bluetooth connection to your ipod or your iPhone in the car? It’s because consumers want it. So the more consumer pressure there is to have environmentally friendly automobiles, it moves in that direction.
BBC
That’s very interesting. But Tony, let me bring you in on this, because it is an interesting picture of a country that is in a very difficult position, caught between Russia and the west but also with an economy that clearly doesn’t basically function. It seems to be held together entirely by aid.
Tony
And wine.
BBC
And wine. The wine is very nice, don’t get me wrong on that.
Tony
Yeah. It’s in a tough position. It’s between some big powerhouses and they had a conflict with Russia a decade or so ago, so it’s a very kind of tenuous position, and it’s definitely not something that’s easy to get out of, I don’t think.
BBC
Tell me, the other thing is that being caught in the middle of very big geopolitical forces, what was very interesting, Georgia. Georgia’s economy right now seems to be run by mainly by Russians who fled from Russia, which is an extraordinary situation, isn’t it?
Tony
Yeah, it is. Roger, I’m really not sure. The basis of this protest is supposedly that NGOs have to register because of their foreign influence, foreign money. But that is required in a lot of countries, so it’s required in Singapore, for example. Right. So I’m not really sure why this is such a problem. If foreign newspapers, like in Singapore, every foreign newspaper has to be approved. Yeah, and I I’m sorry, I don’t mean to be picking on Singapore, but but this is the case in a lot of countries, and so I’m just puzzled as to why this is a problem, especially if there’s so much foreign aid there. I just don’t understand it.
Stephanie
Tony, can I hazard stephanie, come in. Yes. Yeah. Let me hazard a guess. What’s happening in the Ukraine is a very big part of the consciousness of that part of the world right now, as it is for the rest of us as well. How Ukrainians are getting their message out there, how they’re garnering support internationally, is through social media and the foreign press. So I can imagine that any move that tries to muzzle foreign ownership of media is going to look like it is a very authoritarian move. And by and large, we get worried about things like that. And Singapore has been criticized, as Tony, you’ve pointed out, for having those rules, and I can accept that. I can appreciate that that is an issue. Having said that, international interference in national issues has become an increasing thing. We’ve seen the effect of troll farms in Russia on the US elections in the past, for example. And while we think we don’t want there to be constraints on independent and credible news organizations, what if you had an Islamic State take a very large percentage of the news outlets shareholdings?
BBC
Yeah, it’s one of those issues. It has to be applied not in general, but in specifics, and then see how it plays out. And I think that is absolutely the problem in Georgia. No doubt we’ll hear more from that country… Of the Manhattan Institute. Right. Tony, I’m going to let you get your teeth into it, but I will say, first of all, there’s a sense in which this is a phony budget, isn’t it? Because he doesn’t even expect necessarily to get it through Congress.
Tony
Yeah, it’s not going to make it through Congress. I mean, it’s just not. I mean, look, the capital gains tax that he’s proposing is higher than the ordinary income tax of the US. Meaning if you work for a living and you pay taxes from your salary, the capital gains tax he’s proposing is higher than that. And so these people who are actually taking risk on investments, they’re going to pay a higher tax for putting investment money into the market. That’s just ridiculous, and that stuff won’t make it. The thought that companies are going to pay higher tax is just silly because it’s not going to happen. I mean, there are several tax attorneys who, if you believe that’s going to happen, then you need to talk to tax attorneys and understand and CPAs and understand how things really work.
BBC
You’re saying, Tony, that the taxes are not going to happen because he won’t get through Congress, but you’re saying it’s a silly idea.
Tony
I’m saying the corporate taxes won’t happen because it’s unrealistic. So companies pay tax and that’s fine, but they also employ a lot of people. They make investments, they generate intellectual property and so on and so forth. So do we want to tax them more? Sure, maybe a little bit more. But to take a plan like this and aggressively state that you’re going to make companies pay a lot more, it’s really questionable, especially as earnings are collapsing. Publicly earnings in publicly traded companies are collapsing right now, so we’re going to put higher tax on them. And you saw this in the UK when there was the pressure on Gilt six months ago, right? You can’t put this type of thing forward if you don’t have a legitimate plan. And so for Biden to say, if you don’t have a better plan, well, I have a better plan. Why don’t you tax electric vehicles for the miles they drive? Because they don’t pay any fuel tax in the US.
BBC
Yeah, but that’s not going to fill the gap, is it? I mean, if you compare these enormous companies with huge profits, some of them, particularly in the energy sector, the financials as well.
Tony
It’s net positive, right? So it’s net positive. And anybody who thinks like your guest said, people are going to game that $100 million. I mean, that’s just silly, right? Anybody who makes under $100 million, they’re going to distribute it to family and shell companies and LLCs and other things. Nobody’s going to be worth $100 million.
BBC
It’s that they tax people. The people who earn over $400,000. That was the figure, wasn’t it? That’s where the burden is going to fall. But to a lot of people, that seems very reasonable. It’s an awful lot of money.
Tony
What’s? An awful lot of money for $400,000. Yeah, but how many people who earn $400,000 are really going to pay it? Right? I mean, they will, of course, but most of them are also going to have a lot of deductions, too. So you would have to raise the standard deduction unless those guys are going to circumvent. The other really silly thing which your guest was really good at talking about was the tax on unrealized gains. Okay? So imagine if you own a stock and it’s gone up two or three times and you haven’t sold that stock yet. That’s what an unrealized gain is. So imagine this. You own a house and the value has gone up by 50% and the government comes to you and says, hey, I know you haven’t sold your house yet, but I’m going to tax you on that sale of that house anyway, right? That’s exactly what this unrealized gain tax is doing. It’s saying everybody who owns a house that’s gone up in value, the government’s going to come in and tax you on that gain in that house. And you own a house and you’re like, wait, that’s not fair.
Tony
I haven’t even got that money yet. Right? So let these guys make their gains and tax them on those capital gains. That’s fine. We don’t need to hate rich people just for being rich.
Stephanie
Also, Tony, does the house owner get it back if the house price falls?
BBC
And how do you measure it? What’s the measure of value anyway? It’s full of difficulties, clearly. Well, definitely they will find ways around it. Well, let me come back to you then, Tony, on this, because we’ve said basically what you don’t think will work with what Joe Biden is promising or suggesting. If he is attempting to increase the size of the state, which it seems he is, and perhaps a bit parallel to what’s happening in Singapore, how should he be seeking the money for that?
Tony
Well, I think the first thing he needs to do is look at why he’s hiring 17,000 new Environmental Protection Agency agents, right? I mean, you know, we need to understand why we’re hiring more people into the government rather than just putting the heads aside and saying we’re going to grow government, we’re going to be greener, and so on and so forth. There was a law passed last year that said there would be something like 70,000 new Internal Revenue Service agents. And once the new Congress came in, the first thing they did was attack that and defunded because Congress has the power of the purse. So effectively what Biden is doing is he’s trying to anchor the budget discussion. I don’t think many of these things are actually going to happen. This is a negotiation. We have the debt ceiling coming on. We have a number of other things happening with regard to federal government revenue. So all he’s doing here is trying to anchor the conversation very high. And I think what you have in Congress right now is you have a set of Republicans who are not going to negotiate with that.
Tony
What they’re doing with the budget ceiling is they’re ticking off item by item the things that they want and getting the federal government to give in on things one by one because the bureaucrats do not want the debt ceiling to be a problematic issue.
BBC
Well, yeah.
Tony
Is it likely to Republicans on things one by one? Of course.
BBC
Are we going to find the new debt ceiling problem, which seems to be.
Tony
Oh, my gosh, Roger, there’s going to be so much drama about the debt ceiling. Oh, my gosh, it’s going to be the end of the world and full fifth grade of the US. Government and all this garbage. It’s it’s not going to be an issue. It’s never going to be an issue.
BBC
Okay. Interesting. I mean, Singapore, I suppose. Tony, would you would you put your faith in in autonomous vehicles? I mean, they, they have tested some, I think in Texas.
Tony
Yeah. I was driving in Dallas probably a year or so ago, and I was on a very crowded highway, and I looked next to there was a big semi truck next to me, and it was supposedly an autonomous driven semi truck, but of course there was a driver there. And to be honest, I found it terrifying. I heard an interview with one of the grandfathers of AI. His name is Stuart Russell. This was probably about three years ago. And he has been in AI since the 70s or something, and he was involved in self driving cars in the 90s. According to him, and I’m sure the technology has come a long way in three or four years. But at the time he said that we were no further with self driving cars at the time of that interview, which I think was 2018 or something, than we had been in the 1990s. That’s extraordinary. It is. And I work for an AI company. I mean, it’s not magic. It’s code and math. And that’s really what it is. It’s computer code and math. And as Stephanie pointed out, we have trouble updating apps. Right. And so if you’re going to be moving along at 100km/h or whatever and put your faith in a car and other people’s cars, I think when everything is automated, that’s different.
Tony
Right. If we’re 100% self driving cars, then that’s a very different story. But when you have some self driving and some not, there are so many unknowns in the environment, and how can a car know if something walking along the side is a child or a mailman or whatever, right. And you just don’t know what they’re going to do. So I don’t think cars on their own have the compute power to understand what’s going on around them. I suspect that a lot of what we’re being told is marketing more than actual capability. I would really like to talk to somebody and understand if it’s actual capability, because I just don’t believe it. I want it to happen, but I just don’t believe it’s.
BBC
Isn’t it I mean, what you said they want it to happen because I certainly feel it will be hugely useful. I mean, elderly parents being able to get places, for example. But all sorts ways in which actually it’d be really useful to have such a thing. I suppose we feel. And, Stephanie, I’d be interested to get your intake on this. We feel that at this point, with all the technical know how, we have self demonstrated that we should be able to do this. I mean, it’s been a staple of science fiction films, probably going back to the 19th century, that these kind of things would exist.
Stephanie
Yeah, but I have a question on AI. We’ve been talking about Chat GPT and how biases get into it. Now, if you’re trouble, who is setting the safety standards for these self driving cars? If there is a person walking on the street, is it going to make a distinction between a minority race? If there are two people and it has to pick one to hit and it can’t stop, for example, does it pick the minority race guy to hit? What does it do?
BBC
That’s like the famous trolley example in a philosophy class. Do you run over the fat person or not? And these kind of things, which you can’t really expect, I suppose, a self driving car to think of. But I suppose that the point of this. If everything is autonomous, then, as Tony says, perhaps the issue isn’t really a big one. But I would say with all these caveats you’re putting in there, Stephanie, the fact is there are a lot of very bad drivers out there already. Is it worse to have one that’s autonomous?
Stephanie
No, I totally want to have a self driving car, frankly. I would like to not have to drive me around. I would like my husband to not have to drive around. He thinks he’s a race car driver. He’s not really that good. So I think that would be great. But I agree all the cars should be autonomous. And maybe we should have speed limits.
BBC
Well, yes. And you could impose them automatically very easily, couldn’t you? That would be one of the things. And Tony, I suppose you’re in AI. Okay, I take on board your point. You’re saying it hasn’t come people reporting it hasn’t come that far since even the 1990s. But it must be something that AI can take on, surely.
Tony
Sure, AI can take on a lot of things. But is it there right now? And would I want to drive in it right now? Probably not. And Roger, going back to your question about is it worse for a machine to, say, be a bad driver than a human? Absolutely. Yes, it’s worse.
BBC
Why?
Tony
Because the unique function of that machine is to drive you around safely. That driver person does not have a unique function, right? So if that machine is specifically made to drive you around safely, that’s the only thing it’s there for. So it should be able to drive you around safely. And until that can happen, we should absolutely not have autonomous vehicles on the road.
BBC
Okay, but take the bad drivers. Who knows what the function of the bad driver is? But if they hit you, they’ll still do damage, and that’s really what matters. Principle, surely.
Tony
Of course they will. And to go into any country and get a driver’s license. Anybody can get a driver’s license, right? And so that’s a kind of least common denominator standard. The worst driver can still get a license.
BBC
And the worst robot might be a better driver.
Tony
Yeah, but that’s that robot’s 100% job, and unless they can do it in the top, I would say, decile of drivers, it shouldn’t be on the road.
BBC
All right, well, I think they’ve got a big, long way, I think, to persuade either of you, really, that it’s happening. I think Stephanie probably would prefer it probably more than you would. I certainly would love it. Not least for the fact I can go to a lovely English country pub and after perhaps consumed a little bit of lovely, I can just get in the car and it’ll take me home. No issues. That’s what I’m all about. Anyway, thanks to both of you for being with us. Your rodeo of business Matters has been survival, I’m very pleased to say, Tony. And we’ll welcome you all back soon, I think. But thanks for listening to Business Matters. Bye.
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.
TN: Sure.
MG: I once developed an app called “How Edition”. I was having dinner with the head developer once and he said he just came back from a conference about machine learning and he was just basically well, having drinks with me laughing and joking saying everybody use this term machine learning but it’s really just regression analysis. Right? So, talk about machine learning what is actual machine learning? How important is recent data to changes in the regression? Because I assume that’s part of the sort of dynamic nature of what you do just kind of riff on that for a bit.
TN: Okay. So, when I first started Complete Intelligence, I was really cynical about AI. And I spoke to somebody in Silicon Valley and asked the same question: what is AI? And this person said “Well AI is everything from a basic I say, quadratic equation upward.” I’m not necessarily sure that I agree that something that simple would be considered artificial intelligence. What we’re really doing with machine learning is there are really three basic phases. Okay? You have a preprocess which is looking at your data to understand things like anomalies, missing data, weird behavior, these sorts of things. Okay? So, that’s the first phase that we look at to be honest that’s the hardest one to get right. Okay?
A lot of people want to talk about the forecasting methodologies and the forecasting algorithms. That’s great and that’s the sexy part of ML. But really the conditioning and the pre-process is the is the hardest part and it’s the most necessary part. Okay? When we then go into the forecasting aspect of it, we’re using what’s called an ensemble approach. So, we have a number of algorithms that we use and let’s say they’re 15 algorithms. Okay? That we use we’re looking at a potential combinatorial approach of any individual or combination of those algorithms based on the time horizon that we’re forecasting. Okay?
So, we’re not saying a simple regression is the way to go we’re saying there may be a neural network approach, there may be a neural network approach in combination with some sort of arima approach. We’re saying something like that. Right? And so, we test all of those permutations for every historical period that we’re looking at.
So, I think traditionally when I look back at kind of quote-unquote building models in excel, we would build a formula and that formula was fairly static. Okay? And every time you did say a crude oil forecast you had this static formula that you set your data against and a number came out. We don’t have static formulas at all.
To forecast crude oil every single week we start at obviously understanding what we did in the past but also re-testing and re-weighting every single algorithmic approach that we have and then recombining them based upon the activity that happened on a daily basis in that previous week. And in the history. Okay?
So, that’s phase two the forecasting approach and then phase three is the post process. Right? And so, the post process is understanding the forecast output. Is it a flat line? Right? If it’s a flat line then there’s something wrong. Is it a straight line up? Then that there’s something you know… those are to use some extremes. Right? But you know we have to test the output to understand if it’s reasonable. Right? So, it’s really an automated gut check on the reasonableness of the outcome and then we’ll go back and correct outliers potentially reforecast and then we’ll publish. Okay?
So, there are really three phases to what we do and I would think three phases to most machine learning approaches. And so, when we talk about machine learning that’s really what we’re talking about is that that really generally three-phase process and then the feedback loop that always goes back into that.
MG: Yeah. No that makes sense. Let’s get…
TN: That’s really boring after a while.
MG: No, no, no but I think that’s it’s part of what I want to do with these spaces is try to get people to understand you know beyond sort of just the headline or the thing that is thrown out there. As a term to what does that actually mean in practice you don’t have to know it fully in depth the way the that you do. But I think having that context is important.
TN: I would say on the idea generation side and on the risk management side right now. Okay? Now the other thing that I didn’t cover is obviously we’re doing markets but we also do… we use our platform to automate the budgeting process within enterprises. Okay? So, we work with very large organizations and the budget process within these large organizations can take anywhere from say four to six months. And they take hundreds of people. And so, we take that down to really interacting with one person in that organization and we do it in say less than 24 hours. And we build them a continuous budget every month.
Once accounting close happens we get their new data and then we send them a new say 18-month forward-looking forecast for them. So, their FPA team doesn’t have to dig around and beg people for information and all that stuff. So, some of this is on the firm event could be on the firm evaluation side, as well. Right? How will the firm perform? Nobody’s using us for that but the firms themselves are using that to help them automate their budgeting process. So, some of that could be on this a filtering side and the idea generation side, as well.
So, we do not force our own GL structure onto the clients. We integrate directly with their SAP or Oracle or other ERP database. We take on their GL structure at whatever levels they want. We have found that there is very little deterioration from say, the second or third level GL to say the sixth or seventh level GL, in terms of the accuracy of our forecast. And when we started doing this it really surprised me. We do a say a team level forecast for 10, 12 billion organizations, six layers down within their GL. And we see very little deterioration when we go down six levels than when we do it at say two levels. Which is you know it really to me it speaks to the robustness of our process but would we consider Anaplan a competitor not really, they’re not necessarily doing the kind of a budget automation that we’re doing at least, that I’m aware of. I know that there are guys like Hyperion who do what we’re doing but again their sophistication isn’t necessarily. What we’re doing and they do a great job and Hyperion is a great organization. I think Oracle gave them a new name now but they’re not necessarily using the same machine learning approaches that we’re using. And our clients have told us that they don’t get the same result with using that type of say ERP originated or ERP add-on budgeting process.
Yep. So, I would say we can’t we can do company-specific information for a customer if that’s what they want. Okay? We don’t necessarily have that on our platform today aside from say individual ticker symbols. Okay? But we’re not forecasting say the P&L of Apple or something like that or the balance sheet of Apple. Something we could do in a pretty straightforward manner but we do that on a customer-by-customer basis.
So, what we’re forecasting right now are currency pairs, commodities about 120 commodities and global equity indices. Okay? We are Beta testing individual equity tickers and we probably won’t introduce those fully on the platform until we have our daily interval forecast ready to go to market. But those are still we’re still working some kinks out of those and we’ll have those ready probably within a few months.
MG: Okay. So, let’s talk about commodities here for a bit tonight. Obviously, this is where a lot of people’s attention has gone to. What kind of variables and I know you said you have a whole bunch of variables that are being incorporated here but are there certain variables in particular when it comes to oil and other commodities that have a higher predictive power than others.
TN: There are I think one of the stories that I tell pretty often and this really shocks people is when we look at things like gold. Okay? I’m not trying to deflect from your oral question but just to you know we’ve spoken with the number of sugar traders over the years. Okay? And so, we tell them that say the gold price and the sugar price there may not necessarily be a say short term say correlation there but there is a lot of predictive capability there and we talk them through why. And I think the thing that we get out of the machine learning approach and we cast a wide net. We’re not forcing correlations is that we’ll find some unexpected say drivers. Although drivers implies a causal nature and we’re not trying to imply causality anywhere. Okay?
We’re looking at kind of co-movement in markets over time and understanding how things work in a lead lag basis with some sort of indirect causality as well as say a T0 or current state movement. So, with crude oil you know there are so many supply side factors that are impacting that price right now, that I can’t necessarily point to say another commodity that is having an impact on that. It really is a lot of the supply side and sentimental factors that are impacting those prices right now.
MG: That makes a lot of sense. And I’m curious how did you mention it’s I think the intervals once a month. Right? So, given the speed with which inflation has moved and yields have moved how does a machine learning process adapt to sudden spikes or massive deltas in in variable movement. Right? Because there’s always a degree of randomness going back to error. Right? And you can make an argument that the larger move is the that may actually be more error but I think that’s an interesting discussion.
TN: So, I’ll tell you where we were say two years ago when 2020 hit versus today. Okay? So, in March of 2020, April 2020 everything fell apart. I don’t think there were any models that caught what was going to happen. It was an exogenous event that hit markets and it happened very quickly. So, in June, I was talking with someone who is with one of the largest software companies in the world and they said “Hey has your AI caught up to markets yet because ours is still lost” And you guys would be shocked if I told you who this was because you would expect them to know exactly what’s going to happen before it happened. Okay? I’ll be honest I think it was all of them but the reality is you know Michael you where you were saying that ML is just regression analysis.
I think a lot of the large firms that are doing time series forecasting really are looking at regression and derivatives of regression as kind of their only approaches because it works a lot of the time. Right? So, we had about a two-month delay at that point and part of it was because… So, by June we had caught up to the market. And we had started in February to iterate twice a month, we were doing once a month; I hope you guys can understand with machine learning two factors are we’re always adjusting our algorithms. Okay? We’re always incorporating new algorithms. We’re always you know making sure that we can keep up with markets because you cannot be static in machine learning. Okay? The other thing is we’re always adding capacity why? Because we have to iterate again and again and again to make sure that we understand the changes in markets. Okay?
So, at that time we were only iterating twice a month and so it took us a while to catch up. Guys like this major technology firm and other major technology firms they just couldn’t figure it out. And I suspect that some of them probably manually intervened to ensure that their models caught up with markets. I don’t want to accuse any individual company but that temptation is always there. Especially, for people who don’t report their error. The temptation is always there for people to manually intervene in their forecast process. Okay?
So, now, today if we look for example at how are we catching changes in markets. Okay? So, if I look at the S&P 500 for April for example, our error rate for the S&P 500 for April I think was 0.6 percent. Okay? Now in May it changed it deteriorated a little bit to I think four or six percent, I’m sorry I don’t remember the exact number offhand but it deteriorated. Right? But you know when there are dramatic changes because we’re iterating at least once a week, if not twice a week we’re catching those inflections much much faster. And what we’re having to do, and this is a function of the liquidity adjustments, is where in the past you could have a trend and adjust for that trend and account for that trend. We’re really having to our algorithms are having to select more methodologies with recency bias because we’re seeing kind of micro volatility in markets. And so again…
MG: So, kind of like the difference between a simple moving average versus like an exponential moving average. Right? Where you’re waiting the more recent data sooner.
TN: It could be. Yeah.
MG: Right.
TN: Yeah. That’s a very very simple approach but yeah it would be something like that, that’s right. Yeah. What so when we work with enterprise customers that level of engagement is very tight because when we’re getting kind of the full set of financial data from a client obviously, they’re very vested in that process. So, that’s different from say a small portfolio manager subscribing to RCF futures product where we’re doing forecasts and they have their own risk process in place. And they can do whatever they want with it. Right? But again, with our enterprise clients we are measuring our error so they can see the result of our continuous budgeting process. Okay?
So, if we’re doing let’s say, we launch with a customer in May, they close their mate books in June get them over to us redo our forecast and send it over to them and let them know what our error rate was in May. Okay? So, they can decide how we’re doing by department, by team, by product, by whatever based upon the error rates that we’re giving at every line item. Okay? So, they can select and we’re not doing kind of capital projects budgets we’re doing business as usual budgets so they can decide what they want to take and what they don’t want to take. It’s really up to them but we do talk through that with them and then over time they just start to understand how we work and take it on within their own internal process.
MG: So, back a little bit Tony. So, you mentioned you do this machine learning forecasting work when it comes to broad economics, markets and currency; of those three which has the most variability and randomness in other words which tends to have a higher error? Whenever you do any kind of machine learning to try to forecast what comes next?
TN: I would say it depends on the equity market but probably equity markets when there are exogenous shocks. So, our error for April of 2020 again, we don’t hide this from anybody it was not good but it wasn’t good for anybody. Right? And so, but in general it depends on the equity market but some of the emerging equity markets, EM equity markets are pretty volatile.
We do have some commodities like say rhodium for example. Okay? Pretty illiquid market, pretty small base of people who trade it and highly volatile. So, something like rhodium over the years our air rates there have not necessarily been something that we’re telling people to use that as a basis to trade but obviously, it’s a hard problem. Right? And so, we’re iterating that through our ML process and looking at highly volatile commodities is something that we focus on and work to improve those error rates.
MG: Here, I hope you find this to be an interesting conversation because I think it’s a part of the of the way of looking at markets, which not too many people are themselves maybe using but is worth sort of considering. Because I always make a point that nobody can predict the future but we all have to take actions based on that unknowable future. So, to the extent that there might be some data or some conclusions that at least are looking at variables that historically have some degree of predictive power. It doesn’t guarantee that you’re going to necessarily be better off but at least you have something to hang your hat on. Right? I think that’s kind of an aspect to investing here.
Now, I want to go a little bit Tony to what you mentioned earlier you had lived abroad for a while in Europe. And when I was starting to record these spaces to put up on my YouTube channel the first one, I did that on was with Dan Arvis and the topic of that space was around this sort of new world order that seemed to be shaping up. I want you to just talk from a geopolitical perspective how you’re viewing perhaps changing alliances because of Russia, Ukraine. And maybe even dovetail that a little bit into the machine learning side because geopolitics is a variable. Which is probably quite vault in some periods.
TN: Yeah, absolutely. Okay. So, with the evolving geopolitical order I would say rather than kind of picking countries and saying it’s lining up against x country or lining up with x country or what country. I would say we’ve entered an era of opportunistic geopolitics. Okay? We had the cold war where we had a fairly static order where people were with either red team or blue team. That changed in the 90s of course, where you kind of had the kind of the superpower and that’s been changing over the last say 15 years with say, China allegedly becoming kind of stronger and so on and so forth. So, but we’ve entered a fairly chaotic era with say opportunistic macroeconomic relation or sorry, geopolitical relationships and I think one of the kinds of top relationships that is purely opportunistic today is the China-Russia relationship.
And so, there’s a lot of talk about China and Russia having this amazing new relationship and they’re deep. And they’re gonna go to war together or whatever. We’ve seen over the past say three, four months that’s just not the case. And I’ve been saying this for years just for a kind of people’s background. Actually, advised the Chinese government the NDRC which is the economic planning unit of the central government on a product or on an initiative called the belt and road initiative. Okay? I did that for two years. I was in and out of Beijing. I never took a dime for it. I never took expense reimbursement just to be clear, I’m not a CCP kind of pawn. But my view was, if the Chinese Government is spending a trillion dollars, I want to see if I can impact kind of good spend for that. So, I have seen the inside of the Chinese Government and how it works and I also in the 80s and 90s spoke Russian and studied a lot on the Russian Government and have a good idea about how totalitarian governments work.
So, I think in general if we thought America first was offensive in the last administration then you really don’t want to learn about Chinese politics and you really don’t want to learn about Russian politics because they make America first look like kindergarten. And so, whenever you have ultra-ultra-nationalistic politics, any diplomatic relationship is an opportunistic relationship. And I always ask people who claim to be China experts but say please tell me and name one Chinese ally. Give me one ally of China and you can’t, North Korea, Pakistan. I mean, who is an ally of China there isn’t an ally of China. There is a transactional opportunistic relationship with China but there is not an ally with China.
And so, from a geopolitical perspective if you take that backdrop looking at what’s happening in the world today it makes a whole lot more sense. And a lot of the doomsayers out there saying China is going to fall and it’s going to have this catastrophic impact. And all this other stuff, the opportunism that we see at the nation-state level pervades into the bureaucracy. So, the bureaucracy we hear about Xi Jinping. And Xi Jinping is almost a fictional character. I hate to be that extreme on it but there is the aura of Xi Jinping and there is the reality of Xi Jinping, just a guy, he’s not Mao Zedong. He doesn’t have the power that supposed western Chinese experts claim that he has. He’s just a guy. Okay?
And so, the relationships within the Chinese bureaucracy are purely transactional and they are purely opportunistic. So again, if you take that perspective and you look at what’s happening in geopolitics, hopefully you can see things through a different lens.
MG: Now, I’m glad you’re framing that in those terms because I think it’s very hard for people to really understand some of these dynamics when it’s almost presented like a like the story for a movie. Right? For what could be a conflict to come by the media because and it’s almost overly simplified. Right? When you hear this type of talk. So again, I want to go back into how does that dovetail into actual data. Right? Maybe it doesn’t at all. When you have some of these dynamics and you talk about market clearing data, you’re going to probably see mark movement somewhat respond off of geopolitical changes. Talk about anything that you’ve kind of seen as far as that goes and how should investors consider geopolitical risk or maybe not consider geopolitical risk?
TN: Yeah, I think, well when you see geopolitical adjustments today all that really is… I don’t mean overly simplified but it’s a risk calibration. Right? So, you know Russia invades Ukraine, that’s really a risk calibration. How much risk do we want to accept and then what opportunities are there? Right?
So, when you hear about China, you have to look at what risk is China willing to accept for actions that it takes? Keeping in mind that China has a very complicated domestic political environment with COVID shutdown, lockdowns and all of this stuff. So, having worked with and known some really smart Chinese bureaucrats over the years, these guys are very concerned with the domestic environment. And I don’t although there are idiot you know generals and economists here and there who say really stupid stuff about China should take over TSMC and China should invade Taiwan, these sorts of things. My conversations over the years have been with very pragmatic and professional individuals within the bureaucracy.
So, do I agree with their policies? Not a lot of them but they are well thought out in general. So, I think just because we hear talk from some journalist in Beijing who lives a very sheltered life about some potential thing that may happen. I don’t think we necessarily need to calibrate our risk based on the day-to-day story flow. I think we need to look at like… so there’s a… I’m sure you all know who Leland Miller is in China beige book like?
MG: Yeah, he’s not too long ago.
TN: Yeah. He has a proxy of the Chinese economy and that’s a very interesting way to look at an interesting lens to look through China or through to look at China or whatever. But so, I think that the day-to-day headlines, if you follow those, you’re really just going to get a lot of volatility but if you try to understand what’s actually happening, you’ll get a clearer picture. It’s not necessarily a connection of a collection of names in China and the political musical chairs, it’s really asking questions about how does China serve China first. What will China do to serve China first and are some of these geopolitical radical things that are said do they fit within that context of China serving China first? So, that’s what I try to look at would I be freaked out if China invaded Taiwan? Absolutely. I think everybody would right but is that my main scenario? No, it’s not.
MG: In terms of the data inputs on the machine learning side how granular is the data meaning? Are you looking at where geographically demand might be picking up or is it simply this is what the price is and who cares the source? Because again with hindsight if you knew that the source of China and kind of had a rough sense of the history of Russia-Ukraine maybe that could have been an interesting tell that war was coming.
TN: Yes or No. To be honest it had more to do with the value of the CNY. Okay? And I’ll tell you a little bit about history with the CNY. We were as far as I know, the only ones who called the CNY hitting 6.7 in August of 2019 with a six-month lead time. And so, we have a very good track record with USD-CNY and I would argue that China’s buying early in 2022 had a lot more to do with them from a monetary policy perspective needing to devalue CNY. So, they were hoard buying before they could devalue the CNY and I think that had a lot more to do with their activity than Russia-Ukraine. Okay? And if you notice they’ve made many of their buys by mid-April and once that happened you saw CNY, go to 6.8. Right? It’s recovered a little bit since then but China has needed to devalue the CNY for probably at least nine months. So, it’s long overdue but they’ve been working very hard to keep it strong so that they could get the commodities they needed to last a period of time. Once they had those commodities, they just let the parachute go and they let it do value to 6.8 and actually slightly weaker than 6.8.
MG: The point of the devaluation is interesting. I feel if I had enough space but we were talking about the Yen and what’s happened there. And this observation that usually China will start to devalue when they see the end as itself going through its own devaluation.
How does some of those cross correlations play out with some of the work that on machine learning you’re doing? Because there’s a human element to the decision to devalue a currency. Right? So, the historical data may not be valid I would think because you might have kind of a more humanistic element that causes the data to look very different.
TN: Well, they’re both export lab economies. Right? And we’ve seen a number of other factors dollar strength and we’ve seen changing consumption patterns. And so, yes when Japan devalues you generally see China devalue as well but also, we’ve seen a lot of other activities in on the demand-pull side and on the currency side especially with the US dollar in… I would say over the last two quarters. So, yes, that I would say that the correlation there is probably pretty high but there are literally thousands of factors that contribute to the movement of those of those currencies.
MG: Is there anything recently Tony in the output that machine learning is spitting out that really surprises you? That you know… And again, I understand that there’s a subjective element which is our own views on the world and of course then the pure data. But I got to imagine it’s fascinating sometimes if you’re sitting there and seeing what’s being spit out if it’s surprising. Is there anything that’s been kind of an outlier in in the output versus what you would think would likely happen going forward?
TN: Yeah. You know, what was really surprising to me after we saw just to stick on CNY for a minute because it’s the first thing that comes to mind, when we saw CNY do value to 6.8. I was looking at our forecast for the next six months. And it showed that after we devalued pretty strong it would moderate and reappreciate just a bit. And that was not necessarily what I was hearing say in the chatter. It was kind of “okay, here we go we’re going to go to seven or whatever” but our data was telling us that that wasn’t necessarily going to happen that we were going to hit a certain point in May. And then we were going to moderate through the end of the year. So, you know we do see these bursty trends and then we see you know in some cases those bursty trends continue for say an integer period. But with CNY while I would have on my own expected them. I expected the machines to say they need to keep devaluing because they’ve been shut down and they need to do everything they can to generate CNY fun tickets. The machines were telling me that we would you know we’d see this peak and then we would we would moderate again and it would kind of re-appreciate again.
So, those are the kind of things that we’re seeing that when I talk about this it’s… Oh! the other thing is this: So, in early April we had a we have people come back to us on our forecast regularly who don’t agree with what we’re saying and they complain pretty loudly.
MG: So, what do you say I talk when I hear that because whenever somebody doesn’t agree with the forecast, they are themselves making a fork.
TN: Of course. Yeah. Exactly. Right? Yeah, and so this person was telling us in early April that we’re way wrong that the S&P was going to continue to rally and you know they wanted to cancel their subscription and they hated us and all this other stuff. And we said okay but the month’s not over yet so let’s see what happens this was probably a week and a half in April. And what happened by the end of April things came in line with our forecast and like I said earlier we were like 0.4 and 0.6 percent off for the month. And so that person had they listened to us at the beginning of the month they would have been in a much better position than they obviously ended up being in. Right? And so, these are the kind of things that we see on a… I mean, we’ve got hundreds of stories about this stuff but these are the kind of things that we see on a regular basis. And we mess up guys I’m not saying we’re perfect and but the thing that we when we do mess up, we’re very open about it. Everything that we do is posted on our on our website. Every call we make, every error we have is their wars and all. Okay? And so, we’re not hiding our performance because if you’re using our data to make a trade, we want you to understand the risk associated with using our data. That’s really what it comes down to.
MG: It reminds me of back in 2011 and in some other periods I’ve had similar situations, where I was writing and I was very adamant in saying the conditions favored a summer crash. Right? I was saying that for the summer and the market should be going up and people would say oh where’s your summer crash and I would say this summer hasn’t started. Like it’s amazing how people, I don’t know, what it is, I don’t know if it’s just short-termism or just this kind of culture of constantly reacting as opposed to thinking but it is it is remarkably frustrating.
Going back to your point at the very beginning being entrepreneur don’t do it, that you have to build a business with people and customers who in some cases are just flat out naïve.
TN: That’s all right though. That’s a part of the risk that we accept. Right?
MG: Yeah, the other thing right now that happens with every industry but from the entrepreneur’s standpoint. It’s what you’re doing the likely outcome of your product of your service. You’re trying to communicate that to end clients but then in the single role of the die the guy the end client who comes to you exactly for that simply because they disagree with you know the output, now says I want out.
TN: Oh! Yeah! Well, your where is your summer call from 2011 the analogy today is where is your recession call. Right? So, that’s become the how come you’re not one of us calls right now. So, it’s just one of those proof points and if you don’t agree with that then you’re stupid.
So, I would say you never finish with that there is always a consensus and a something you’re you absolutely, must believe in or you don’t know what you’re talking about.
MG: Yeah, well, thankfully. What you’re talking about so appreciate everybody joining this space Tony the first time you and I were talking. I enjoyed the conversation because I think it said on investing and I encourage you to take a look at Tony’s firm and follow him here on twitter. So, thank everybody. Thank you, Tony and enjoy.
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.
Key observations
• 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.
Current position
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.
In this short video, we teach you how to see the forecast price of silver using CI Futures. For more information about this app, go to the CI Futures page.
How much has silver risen in 10 years? This video also shows price of silver over the last 10 years as well as the silver price trend chart. CI Futures is an AI/ML app for forecasting thousands of assets including commodities like silver and gold.
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.
📊 Forward-looking companies become more profitable with Complete Intelligence. The only fully automated and globally integrated AI platform for smarter cost and revenue planning. Book a demo here.
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.
Show Notes
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.
TS: Right.
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.
Every company wishes they have a crystal ball when it comes to making business decisions, and while a physical iteration of that wish is not possible, Tony Nash has developed the next best thing for his clients at his startup, Complete Intelligence.
Tony is the CEO and Founder of Complete Intelligence. Before founding Complete Intelligence, Tony was the global head of research for The Economist and the head of Asia consulting for IHS Markit.
Complete Intelligence is a fully automated and globally integrated AI platform for smarter cost and revenue proactive planning. Using advanced AI, they provide highly accurate cost and revenue forecasts fueled by billions of enterprise and public data points.
Key Takeaway: As a growing, scaling business, you must know what you are good at, what you do, and what you do not do. Maintain your mission-critical focus on the most important aspects of your business and outsource the parts that you are simply not good at or are outside of your mission.
Lessons Learned:
• Put Significant Thought into Your Senior Hires – hire low first, then hire the upper levels as they will be the ones that have to share your mission and must be the right hire.
• Know what You Do Not Do – Knowing what you don’t do is just as important as knowing what you do do.
• Define Your Culture – Define the culture you are building and continually and intentionally reinforce it.
Show Notes
JC: Hello everybody, Jeff Chastain here with the building to scale podcast again, where I get the opportunity really to speak with entrepreneurial business leaders growth-minded leaders who are working to grow and scale their own companies. And some of the we’ll discuss some of the challenges. Some of the successes as they’ve had over the years working through that.
Today’s guest with me here is Tony Nash with Complete Intelligence out of the Houston, Texas area. So first off Tony welcome to the show and thank you for taking a few minutes out of your busy day to join us here.
TN: Thanks, Jeff. I appreciate the opportunity.
JC: So give us a little bit about what Complete Intelligence is and what you guys have got going on there?
TN: Sure. We run an artificial intelligence platform. We use it to forecast market activity say currencies, commodities, equities for investors. We also help people companies understand their costs and their revenues which are really important on the budgeting side. So we help people de-risk their future business decisions by understanding where their costs are going to go and where their revenues will likely go.
JC: Okay, so I’ve got a background in technology and we kind of talked about AI and stuff beforehand but if we were to bring that down. And say okay I put you on the spot here but it was well the networking questions I’ve heard before like. Okay, if you describe that to a five-year-old what do you really do? So I know we kind of talked beforehand that this is typically big enterprise focus but for those that are not into that industry or not dealing with 9 10 figure dollar budgets, kind of a thing. Proactive budget planning. What does that really mean from a obviously from a company your size or your perspective?
TN: Sure, if I have to describe it to a five or ten year old. It’d say look, if you run a lemonade stand you have to understand how much the lemons are going to cost. How much the water is going to cost. How much the sugar is going to cost you. Also want to understand how many customers you’re going to have. How much money they’re going to spend. How much money you’re going to take in through the lemonade stand, right?
So we work with customers to understand all of those things. Now when companies themselves forecast this stuff and we know this from talking to our clients. They typically have 30 error rates or worse, even for raw materials costs. So their planning is way off, okay? When you look at industry experts investment banks economists, industry experts, these sorts of things. Their error rates are typically 20% off, okay? Our error rates are typically about around 4.6 percent, okay? And that’s on an absolute percent error basis. So we’re not gaming the pluses and minuses, okay?
So if you’re buying those lemons and that sugar and that sort of thing you can pay a dollar 20 for it. For us maybe a dollar five or something like that, right? So we’ll help you save 15 cents a lemon, okay? And you’ll understand where those costs are going. And so when you scale that up to very large customers who have you know 2 billion, 5 billion, 20 billion dollars in turnover or more. They’re buying in tens and hundreds of millions of dollars.
So let’s say a 17% improvement in their ability to forecast things, those are very large numbers. And so we’re working with enterprise scale data in the cloud and helping them understand where their business is going. And I would say probably better than just about anybody else out there. And so it doesn’t have to be the biggest company in the world doing this stuff. We work with mid-sized companies as well, okay? Because we’ll take data out of their enterprise planning system or something like that. And we’ll use it on our platform to help them make better decisions. We’re not telling them what to do, we’re just telling them where the data tell us that things are going to go.
So the real problem we’re solving aside from the obvious of what’s going to happen in their markets and their costs. Every company has a very painful budgeting process, okay? Some companies it takes a month or two or three months. Some companies some of our customers it takes six or seven months. And they’re going through in a very meticulous way of proactive planning their budgets. And there are hundreds of people involved and at the end of the day it goes up to the CFO and the CPO the chief procurement officer or the CFO and the head of sales and it’s a verbal agreement on what’s actually going to happen. This is actually one of the CFO pain points.
Not all that data driven, right? And so what we do is we give them a straw man to base it on so they can a very meticulous and detailed straw man. So that seven month process is taken down to a couple days, okay? From data transmission processing to sending back. And they also get a continuous budgeting exercise, okay? Every month we’ll reforecast their budgets for them so if something like Covid happens as it did last March, April. We help them understand what’s likely to happen uh in their business.
JC: Now that makes sense and that’s really one of those things that regardless of the business side that it’s like, okay having actual real data not seven month old data actually having it on a monthly basis or even closer kind of a thing. You can actually make real decisions on it at that point rather than just thinking like you said one code would happen. Everybody had their budget set January, February for what 2020 was going to be. And now two months later they’re completely invalidated that either the like you said earlier some some businesses are up, some are down, some are pulling back the the expenses. So it may have turned out okay but all the proactive planning they did initial on is completely out of window at that point.
TN: Right and most of those guys their revenue budgets were blown out like they had no idea what was going to happen there. They were saddled with their cost budgets that they had to continue paying for all this stuff. They didn’t know what was coming in on the top line. And so they then had to be very reactive on the on the cost side. And initially it was just a lot of you know arbitrary cost cutting and no disrespect to anybody. They were doing the best they could right but a lot of these big companies initially were just like, we don’t know what what we’re going to be in three months.
We were initially told covered was four to six weeks. And you know it’s still going on right and so what we saw is a lot of companies cut costs in the second quarter and the third quarter and by the end of the third quarter the management views looked up and said, well we’ve cut it as much as we can through the first three quarters let’s not release any more budget in Q4. So that just helped them on the income side so that they you know their bottom line looked better than it probably would have if they would have been a status cooperation.
JC: Yeah
TN: But still what we’re doing is using actual live data to help clients make the actual decisions that they need to make to run their businesses.
JC: Yeah and that’s really to me the key whether you’re got the small business that you simply just don’t have that much data to be processing all the way up to the enterprise. It’s still the same thing of saying, okay making those decisions on the numbers rather than, like you said with with Covid where it’s almost an immediate knee-jerk panic reaction of, hey we’ve got to cut things or hey everything’s going to be down. It’s like okay let’s look at the numbers and hopefully by a Q2 Q3 et cetera we’ve got some actual real data that we can start looking at.
So but yeah that’s that’s interesting so going back to Complete Intelligence then take us back. And say I think you said it 6 to 7 years old for the company itself. So how did this how did this kind of come about from a entrepreneurial standpoint.
TN: Sure, yeah, I used to run global research for a company called The Economist based in the UK, publishing company. And then I moved to a company called IHS Market which was just bought by S&P about six months ago. I was their Asia head of consulting. I was working with clients on a lot of data-driven decisions. And what clients were telling me were two things first the forecast that everyone was doing not just stuff, us were wrong and there was no accountability for that, okay?
The second is they could never get a forecast for their exact decisions. Forecasts were always too high level or not the right thing or something. So I rolled out of IHS market saying I want to have a data driven company that actually helps people make real decisions about their business. And so we started as a consulting firm for our first few years we were a consulting firm. And I was trying to understand the types of decisions that people needed to make I knew it from my consulting days with bigger firms but I wanted to understand what we could actually do.
About three years in we decided to turn into a product firm. Which is a very different type of business and so you know we built an initial platform that was very customizable but then to productize it out to build it to scale really is a very different skill set. Aside from a little bit math and a little bit of code it’s a very different same marketing and sales operation. It’s a very different you know infrastructure and all that stuff, right?
So a couple years ago we decided to productize with some subscription online subscription data products. And then we’ve got more specific with say cost and revenue products. So, I started the company in Asia in Singapore and then in 2017 we moved to Texas. So part of our, my calculation there was the talent in my mind is better here in the US. The customers are much easier to access here in the US and the business environment is pretty friendly. So it was a pretty easy decision for us to decide to come to Texas.
JC: Interesting. Okay. So what kind of challenges or what did you face in going from I guess I don’t necessarily know what your role was when you were saying with the economist except I’m assuming you’re you’re managing a team but you’re not necessarily managing a company. At that point to now owning and running your own company here with you said what 10 11 something employees up to now?
TN: Yes, that’s right that’s right, I think. So you know first is always the administrative part of it, right. I mean I think every new business owner just isn’t aware of the administrative stuff. And also the fear of missing something, right. What have I not done. what what tax filing have I not done or you know something like that, right? So there’s always that which was not a major issue but it was an additional burden.
When I think the biggest part of it was, I was just doing everything. And you come as a as a business owner you come to a point where you’re doing everything and you’re involved in everything. And then you’ll come to a point where you have to delegate stuff. And finding the right balance of when to do that and how to do that is I would say it’s more art than science. And other things like scaling RIT infrastructure that’s never really a decision I’d make before. I’m a math nerd and economics and data nerd, right.
So you know those types of decisions were really new but also on the customer side. Although, I had been customer facing when and this is kind of a no-brainer of course but when you don’t have a big brand behind you. Getting to the right people is a much more difficult process. And so we, I knew that coming out of the gate but I underestimated how hard it would be.
We started talking with some of our sales partners right away. Knowing that they wouldn’t give us a yes, right away but starting the relationship so guys like oracle guys like Bloomberg, Microsoft, Refinitive Tompson, Reuters these guys are all major partners for us now. Major sales channel partners and it took us four to five years to get those relationships moving and commercialized. So for a small business owner who is looking at channels as a major part of their business strategy. I would recommend you have to start talking to those partners right now like a year or two or three before you intend on getting your first dollar.
And so the other part as we’ve grown is we’ve had to think through, what do we do well as a company. And what’s best for us to outsource so things like HR. You know what, we don’t have an HR team. We have an outsourced HR firm, right, that’s a no-brainer but you know I can’t do it all myself. I don’t know the laws and stuff so we have outsourced HR. As I said with our channels we are scaling up our sales force but to have that as a kind of a force multiplier is huge for us, right. And things like marketing we have a marketing team in the Philippines and we have some marketing here but where can we get great skills at the best price really, right. And so we have to look around to find out you know what that stuff looks like.
We don’t have any of our data science team or any of our developers offshore. They’re all here in the US and part of that is for our client base. We don’t want things going to Eastern Europe or Asia or whatever but where we can push things off and make sure that we keep our core business. We’re happy to push things off. And so what I mean is we are a technology company, okay. We are not a human resources company we are not a marketing company and we’re not a consulting firm. And so we partner or outsource so that we can stay small and scale but do it very very well.
JC: Yeah and really even still that’s giving you the ability to scale because you’re not having to hire in like you said a whole team of HR. It’s a lot more cost effective especially for a smaller business to say hey we’re going to go pay a much smaller fraction of that to an outsourced group still allows you to scale and grow the business but at a much slower cost at that point.
TN: Right.
JC: So kind of what was that did you just walk into that and say day one we’re just not going to do HR. We’re just not going to do marketing etc. or was that kind of a a transition process because I know a lot of people will try to do some of it before they finally throw up their hands. And say okay, yeah this is not us or how quickly did you make that handoff there.
TN: That was immediate. I knew we didn’t want to do that from the start. Just from my corporate experience I knew that that wasn’t something I knew that we would spend a lot of money there not necessarily get good value. And so when somebody is a vendor you can you know you need some output, you need some outcomes. And so we just chose to make some of those guys vendors instead of making them full-time employees.
JC: So I’m curious since obviously you’re a numbers driven company accounting stuff like that. What does your relationship with some of these vendors look like how much of a numbers kind of basis relationship are you doing with them or are they is that more free flowing?
TN: Well, U think when you say numbers basis what what do you mean by that? I’m sorry.
JC: A lot of times. I’ll work with companies to sit here and say okay we’ve still got to measure our return on ROI kind of a thing on everything. So do we have specific numbers do we have specific milestones measurables et cetera tied to outside vendors the same way as we’d have tied to an employee?
TN: Oh, yeah absolutely. So like with our HR you know our outside stage our vendor. What we get from them on a monthly basis, I would probably have to hire a couple people to do internally. It just doesn’t make sense for us the the fully loaded FTE costs are just way too much. On the marketing side, unless somebody has absolutely stellar marketing skills, a lot of the direct marketing campaigns, social media marketing all that stuff for a firm our size at least it just doesn’t make sense to hire somebody. We can direct that activity manage it every day that sort of thing but the execution of it is better outsourced because we can do better with an outsourced vendor like dramatically better than we can by hiring those people directly, right. And so and so and we’re not talking a small kind of we’re saving 20% we’re saving a lot more than that by hiring marketing people directly.
JC: Yeah, that makes sense.
TN: Yeah and so I think again with most of the decisions we make. We really question how core is that to our business does it add to the technology, does it add to the customer relationship? And that’s really what it comes down to so I think we’re you know we’re at a place with things like video calls. And with a lot of the other technology that’s come around over the last 10 years. Where you don’t necessarily need that you don’t need everything in house it’s just not necessary. And if I have a vendor then I don’t necessarily have to pay for them to learn. If somebody is on staff I have to pay for them to learn. And so it’s not necessarily all fully productive time, right. And so again we’re very results oriented company. And so again we think through all that stuff. So for the guys who are watching your podcast. I would say look you know if you’re growing a company you really need to think through what your head count expectations are. What are they doing can you get that outsourced do you absolutely need to hire that person or can you turn it into an invoice.
JC: Yeah and that’s that’s really the the key because I see a lot more today of having a lot more availability and options of those outsourcing kind of a thing. That it’s not just necessarily the one big accounting firm that you had to be local face to face meeting somebody with the technology these days. I can have my account on the other side of the country kind of a thing and it’s just no big deal or I can have a marketing firm like you said all the way over the Philippines. It’s no big deal at that point so it’s almost it’s driven competition in those fields for sure. So it’s really almost like you said a no-brainer that okay why would you why would you want to go build your own in-house marketing firm when you’re a technology company or when you’re a financial services company something like that. It’s like that’s not your core business but still really identifying that core business is obviously the key there.
TN: Right.
JC: So talking about that core business you said you kind of made a an evolutionary change there with within your own company of saying okay consulting to now today being the the 100 product focus. What did that process look like or I guess for that matter? Why did you necessarily say because a lot of people I was that was my own background coming out of corporate America was, okay we’re going to be a consultant kind of thing. So how did you go from the consultant to saying okay we need to do something different or something transitioning towards the product side?
TN: Yeah, it’s very simple. As a consultant my upside is limited. I only have so many hours in the week and I can only bill against those hours. And if I hire people the upside is limited for them, right. So and if I want to grow a large revenue base I then have to hire a lot of people and then add x percent on top of their cost. And you know if their time isn’t sold then I can’t hire them anymore, right.
So I just got really tired of being the main guy consulting and you know billing against my hours. And so we productized because you know I wanted to make sure we could scale the kind of intellectual property that was in my head. And build that out as much as possible. Now that process was a it took a lot longer than I thought and a lot longer than I had hoped. That transition really took 18 months to two years. So you because you know, I had resources that were helping us on client engagements. I had to take them off of client engagement so they weren’t revenue generating to develop the IP around our product business because they can’t do both, okay. They can’t serve clients and develop IP because the development of IP always gets put off. And so I had to make as a business owner, I had to make a very hard decision to say we’re going to stop you know selling, right now, okay.
And I’m going to pay the cost on these resources to develop this capability so that we can then productize it in 18 months time. And that was a very very hard decision but we did it because we had to otherwise I would have been flying all over working you know 90 hours a week, all that stuff. And we did it we bit the bullet and we came out with some pretty amazing capability.
JC: Oh and that’s really the key to me of saying, yes it’s a longer term vision you’re playing the longer game there even like you were talking about with the channel partners. Okay, you gotta start investing in things now looking towards that that longer term goal. And if you’re only looking towards next quarter, next month even next year. You might not necessarily have made that change to go product because you’re just looking at okay how can we get more billable revenues here in the next quarter.
So yeah it’s looking at that so kind of going down that direction. What does what does the vision look like for Complete Intelligence? Well how do you define vision from a company perspective and what’s your what’s your bigger picture vision there since it obviously sounds like you’re one to look longer term than just focusing on the immediate short term?
TN: Yeah I think so so our focus is really to continue to build out what we’ve started to do which is licensing sales for our core capability and aligning with other products. So how do we get built into core let’s say core erp software or core e-procurement software or you know something like that. So that a client doesn’t even have to think about working with us it’s just all baked into that software, right. And so that’s part of the vision.
The other part of the vision is how do we ensure that the results of our efforts are easy for a client to work into their internal processes. So just producing data or just producing something. If it’s an extra step then it’s a hassle for people, right. So how do we make sure and part of this is integration with other software that sort of thing but how do we make sure what we’re doing is really really easy for our customers to use. So that it helps them rather than adds more tasks to their day.
JC: Makes sense. So a lot of times I’ll see this where the the company owner. I’m not saying you are but the company owner has the vision there the ideas going forward how do you bring that down or how how do you bring that down in your own company to the team to say okay there. How do you get them bought into that vision or them understanding that vision internally?
TN: I think anybody doing that has to be comfortable with a lot of kind of a lot of mistakes and ongoing iteration of processes. I may have a short-term view of things that may not be right my team may be doing stuff that ends up wrong. I have to be okay with that and we have to learn. So and it’s not that’s not a luxury if you’re doing something like we’re doing we have to be a learning organization that is always seeing things that aren’t just right. And say okay that’s not right let’s take a couple days fix it. And then we’ll you know we’ll roll it out again or something like that, right. So as a software company we can do that. If we were making something physical it could, it would be different.
JC: Yeah.
TN: But as a software company we can iterate as we’re going, right. And so I think delivering that vision is really helping people understand on an ongoing basis. What the original vision is but then adjusting incrementally on a regular basis. And those regular adjustments they may be technology issues where we can’t actually do what I want to do, okay but that’s fine we iterate and we move along toward that path.
JC: Makes sense. So running a little long here running out of time. I always like to kind of come back and we we’ve talked about a bunch of different things over time but still what is kind of the best tip the best strategy that hey if I had known this six years ago. When we started the company or if I had this in mind this path in mind things might have been easier? What comes to mind as being your kind of your top idea here that wish I’d known this or thought about this or done this earlier?
TN: I think you know the biggest thing that I would have done is really thought through what I needed in a management team. If you’re scaling and you’re building the people who you put in place in a management team are really really critical. So what I would say is higher lower levels first and then make sure that the senior level management team that you’re hiring is somebody that you can really trust and someone who can really manage a team.
So put off those senior hires as long as possible. And it’s going to be painful and it’s going to mean you’re going to have to work a lot. And you know that sort of thing but higher low first then higher the upper levels, okay. And that’s almost the opposite of what say a venture capital investor or something would tell you. They want to see a management team but the fact is you need execution and then you need to build into those senior people that you can really trust to execute on the vision.
JC: That makes sense that’s interesting since we hadn’t touched on that one yet. I was figuring you’d go different directions but yeah I know a lot of times I’ll see that especially with the small ones if you’re don’t not having to do venture capital or stuff like that because I do agree there but a lot of times it is. Still it’s almost more the challenge that was what I run into of you start building out the lower levels. And you’re still trying to wrap your arms around it for honestly too long before you start introducing that management but yeah it’s doing that lower level and really understanding what’s going on first. And making sure you’ve got to keep handle on it before you can start bringing in people and really focusing at that point on.
Okay, what even going back to like what you were saying. Okay, what’s our core focus in the business this turns into. Okay, what’s your core focus as a leader to say. Okay, what are the aspects that I don’t want to do that I don’t enjoy doing that I don’t do well etc to hire on but yeah I like that from the focus on on building out the lower level team first that makes a lot of sense because a lot of times you’ll see startups said hey here’s our full sweet sea level
suite all these people we brought in it’s like. Okay, who’s actually doing the work at this point so yeah very cool, right?
TN: That’s right.
JC: So the listener wants to learn more about uh your company about Complete Intelligence about yourself where can they go find some more information here?
TN: Sure, so you can find us on on the web at completeintel.com. On social media on twitter we’re @complete_intel and you know just look us up online and we have a lot of interviews. A lot of resources on our website to find out more.
JC: Okay, we really appreciate it so thank you for taking time out.
How much confidence do you have in traditional price forecasts for the components of your supply chain? Your answer is probably “not much”, if you have been in business for over a decade — or even just during 2020! But AI can do better — much better — at price forecasting than the standard statistical technique of linear regression most of us learned in college.
Complete Intelligence has built a comprehensive platform for making very accurate supply chain ingredient forecasts. The forecasting Saas have done the hard work of aggregating (and cleaning!) billions of data points from many high-quality sources, including import/export trade data, all feeding the AI algorithm engines to produce amazingly accurate predictions. You should follow the postings of Tony Nash , for his economic commentary based on many forecasts for exchange rates, basic commodities, and supply chain components important for world economies and local business operations.
Many companies have antiquated, inaccurate processes for forecasting costs in their supply chain. Their standard statistical forecasting is usually done with linear regression – a straight-line projection of historical costs, into the future. But the price behavior of most commodities is not linear, it is non-linear. Artificial intelligence algorithms are especially suited to making accurate forecasts using non-linear data, which is why they are increasingly applied to dynamic financial forecasting.
Many industries are especially sensitive to supply costs:
Manufacturing (electronics, energy equipment, automotive, health supplies, pharmaceuticals, metals, plastics, papers)
Extraction operations (oil and gas, forestry, mining)
Services (transportation, shipping, hospitality, food and beverage)
Supply chain cost planning is a core process, and AI tools are destined to become key ingredients, deeply embedded in operations. They enable automation of proactive planning and monitoring to digitally transform the organization. The licensing cost for these financial forecasting tools or financial projection software is a small fraction of the operations cost – and potential savings. It is also worth noting that having reliable forecasts of future price trends can create a rational basis for supplier negotiations. Simplify financial planning with AI and machine learning.
I’m excited about the AI-driven digital transformation of micro-economic forecasting, and would eagerly discuss the benefits with you.
