Tag: automation

Will AI Take Your Job? Exploring the Realities of Automation

Post author By admin
Post date April 6, 2023

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In the latest Week Ahead episode, three experts – Todd Gentzel, Chris Balding, and Sam Rines – discuss the impact of AI on the job market and the enterprise.

The conversation delves into the macro environment and the rise of AI, with Sam Rines framing the discussion by noting the fast adoption of AI tools like ChatGPT and Midjourney, which are taking out low and mid-level writing, creative, and analyst tasks. This is a threat at a scale not seen before as this generation of AI is targeting professional, corporate, and office jobs.

Todd Gentzel, who has consulted and led strategy for some of the world’s largest companies, discusses the current state of AI in the enterprise. He notes that many AI projects are just pet projects to tick a box and the “AI” portion of these projects is extremely limited. However, he believes that AI has the potential to change the enterprise significantly and identifies the factors holding the enterprise back from adopting useful AI.

Chris Balding, the founder of an AI-NLP firm, discusses whether AI will steal jobs. He notes that starting his firm has changed his view of the application of AI and its potential to take on whole job functions. The conversation covers the impact of AI on labor and capital, the potential for AI to be deployed to take on individual functions, and whether AI can only be used to augment job functions or take on whole job functions.

The discussion raises important questions about the impact of AI on the job market and the enterprise, and how it will change the way we work. While the experts have different perspectives on the potential of AI, they all agree that it will have a significant impact on the economy, the job market, and society as a whole.

Key themes:
1. Is the macro environment to blame for the rise of AI?
2. How will AI change the enterprise?
3. Will AI steal your job?

This is the 60th episode of The Week Ahead, where experts talk about the week that just happened and what will most likely happen in the coming week.

Follow The Week Ahead panel on Twitter
Tony: https://twitter.com/TonyNashNerd
Sam: https://twitter.com/SamuelRines
Todd: https://twitter.com/ToddGentzel
Chris: https://twitter.com/BaldingsWorld

Transcript

Tony

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

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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.

Todd

Thanks, Tony.

Sam

Thank you, Tony.

News Articles

Complete Intelligence – an AI-powered intelligence platform for strategic investment and procurement decisions

Post author By admin
Post date December 13, 2021

This article first appeared and originally published at https://cxcreate.io/complete-intelligence-an-ai-powered-intelligence-platform-for-strategic-investment-and-procurement-decisions.

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.

Download the report to get the full story.

CLICK HERE TO DOWNLOAD REPORT

About this report

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.

Summary details
Table 1: Fact sheet

News Articles

China’s Belt And Road Has Failed. TONY NASH In Conversation With Daniel Lacalle

Post author By admin
Post date November 4, 2021

Tony Nash joins Daniel Lacalle in a discussion on the rise of the machines in a form of AI and machine learning and how Complete Intelligence helps clients automate budgeting with better accuracy using newer technologies like now casts. How GDP predictions are actually very erroneous yet nobody gets fired? And how about China’s GDP as well, and why it’s different from other economies? All these and so much more in markets in this fun discussion.

The video above is published by Daniel Lacalle – In English.

Show Notes

DL: Hello everyone and welcome to this podcast. It is a great pleasure to have somebody that you should actually follow in social media on Twitter, Tony Nash. He is somebody that you definitely need to need to look for because it has very very interesting ideas. Tony, how are you?

TN: Great, thanks Daniel. Thanks so much for having me today.

DL: It’s a tremendous pleasure as I said I was very much looking forward to to have a chat with you. Please introduce a little bit yourself. A little bit to our audience and let us know what is it that you do.

TN: Sure, thanks Daniel. My name is Tony Nash. I live in Houston, Texas. I’ve spent actually most of my life outside of the U.S. I spent most of my 20s in Europe, North Europe, the UK, Southern Europe and from my 30s to almost the end of my 40s I was in Asia. And so you know being in the U.S., Europe and Asia has really given me personally an interesting view on things like trade economics markets and so on and so forth.

During that time I was the global head of research for the economist out of London, I was based in Singapore at the time. Led the global research business. I moved from there to lead Asia consulting for a firm called IHS Markit which is owned by S&P now.

And after that I started my current firm Complete Intelligence which is a machine learning platform. We do global markets currencies, commodities, equity indices, economic concepts. We also do corporate revenue and expense forecasting so we’ll automate budgeting for large multinational firms.

DL: Wow! amazing. Truly amazing. You probably have a very interesting viewpoint on something that a lot of the people that follow us have probably diverging views. Know the situation about the impact of algorithms in the market the impact of high frequency trading and machines in markets.

We had a chat a few months ago with a professor at the London School of Economics that he used to invite me to his year-end lectures to to give a master class. And he mentioned that he was extremely concerned about the almost the rise of the machines. What is your view on this?

TN: I think so an Algo is not an Algo, right? I mean, I think a lot of the firms that are using Algo’s to trade are using extremely short-term algorithmic trading say horizons. Okay? So they’re looking at very short-term momentum and so on and so forth. And that stuff has been around for 10 plus years, it continues to improve. That’s not at all what we do we do monthly interval forecasts, Okay?

Now, when you talk to say an economist they’re looking at traditional say univariate and multivariate statistical approaches, which are kind of long-term trendy stuff. It’s not necessarily exclusively regression, it gets more sophisticated than that.

When we talk to people about machine learning, they assume we’re using exclusively those kind of algorithms. It’s not the case. There’s a mix we run what’s called an ensemble approach. We have some very short-term approaches. We have some longer-term traditional say econometric approaches. And then we use a configuration of which approach works best for that asset or that revenue line in a company or that cost line or whatever for that time.

So we don’t have let’s say, a fixed Algo for gold, Okay? Our algorithm for the gold price is continually changing based upon what’s happening in the market. Markets are not static, right? Trade flows economics, you know, money flows whatever they’re not static. So we’re taking all of that context data in. We’re using all of that to understand what’s happening in currencies, commodities and so on, as well as how that’s impacting company sales. Down to say the department or sub department level.

So what we can do with machine learning now. And this is you know when you mentioned should we fear the rise of the machines. We have a very large client right now who has hundreds of people involved in their budgeting process and it takes them three to four months to do their budgeting process. We’ve automated that process it now takes them 72 hours to run their annual budgeting process, okay? So it was millions of dollars of time and resources and that sort of thing. We’ve taken them now to do a continuous budgeting process to where we churn it out every month. So the CFO, the Head of FP&A and the rest of the say business leadership, see a refresh forecast every month.

Here’s the difference with what we do, compared to what a lot of traditional forecasters and machine learning people do, we track our error, okay? So we will as of next month have our error rates for everything we forecast on our platform. You want to know the error for our gold price forecast, it’ll be on there. You’ll know the error for our Corn, Crude, you know, JPY whatever, it’s on there. So many of our clients use our data for their kind of medium term trades so they have to know how to hedge that trade, right? And so if we have our one, three month error rates on there, something like that it really helps them understand the risk for the time horizon around which they’re trading. And so we do the same for enterprises. We let them know down to a very detailed level to error rates in our forecast because they’re taking the risk on what’s happening, right? So we want them to know the error associated with what they’re doing with what we’re doing.

So coming out of my past at the economist and and IHS and so on and so forth. I don’t know of anybody else who is being transparent enough to disclose their error rates to the public on a regular basis. So my hope is that the bigger guys take a cue from what we’re doing. That customers demand it from what we’re doing. And demand that the larger firms disclose their error rates because I think what the people who use information services will find is that the error rates for the large firms are pretty terrible. We know that they’re three to seven times our error rates in many cases but we can’t talk about that.

DL: But it’s an important thing. What you’ve just mentioned is an important thing because one of the things that is repeated over and over in social media and amongst the people that follow us is well, all these predictions from the IMF, from the different international bodies not to the IMF. Actually the IMF is probably one of the one that makes smaller mistakes but all of these predictions end up being so aggressively revised and that it’s very difficult for people to trust those, particularly the predictions.

TN: Right. That’s right.

DL: And one of the things that, for example when we do now casts in our firm or when with your clients. That’s one of the things that very few people talk about, is the margin of error is what has been the mistake that we have made in the in that previous prediction. And what have we done to correct it because one might probably you may want to expand on this. Why do you think that the models that are driving these now cast predictions from investment banks in some cases from international bodies and others? Are very rarely revised to improve the prediction and the predictability of the of the figures and the data that is being used in the model.

TN: It’s because the forecasters are not accountable to the traders, okay? One of the things I love about traders is they are accountable every single day for their PNO.

DL: Yeah, right.

TN: Every single day, every minute of every day they’re accountable for their PNO. Forecasters are not accountable to a PNO so they put together some really interesting sophisticated model that may not actually work in the real world, right? And you look at the forward curves or something like that, I mean all that stuff is great but that’s not a forecast, okay? So I love traders. I love talking to traders because they are accountable every single day. They make public mistakes. And again this is part of what I love about social media is traders will put their hypothesis out there and if they’re wrong people will somewhat respectfully make fun of them, okay?

DL: Not necessarily respectfully but they will.

TN: In some cases different but this is great and you know what economists and industry forecasts, commodity forecasters these guys have to be accountable as well. I would love it if traders would put forecasters up to the same level of criticism that they do other traders but they don’t.

DL: Don’t you find it interesting? I mean one of the things that I find more intellectually dishonest sometimes is to hear some of the forecasters say, well we’ve only made a downgrade of one point of one percentage point of GDP only.

TN: Only, right. It’s okay.

DL: So that is that we’ve grown accustomed to this idea that you start the year with a prediction of say, I don’t know three percent growth, which goes down to below two. And that doesn’t get anybody fired, it’s sort of like pretty much average but I think it’s very important because one of the things. And I want to gather your thoughts about this. One of the things that we get from this is that there is absolutely no analysis of the impact of stimulus packages for example, when you have somebody is announcing a trillion dollar stimulus package that’s going to generate one percent increase in trendline GDP growth it doesn’t. And everybody forgets about it but the trillion dollars are gone. What is your thoughts on this?

TN: Well, I think those are related in as much as… let’s say somebody downgraded GDP by one percent. What they’re not accounting for, What I think they’re not accounting for is let’s say the economic impact kind of multiplier. And I say that in quotes for that government spending, right? So in the old days you would have a government spending of say you know 500 billion dollars and let’s say that was on infrastructure. Traditionally you have a 1.6 multiplier for infrastructure spend so over the next say five years that seeps into the economy in a 1.6 times outs. So you get a double bang right you get the government spending say one-to-one impact on the economy. Then you get a point six times that in other industries but what’s actually happened.

And Michael Nicoletos does some really good analysis on this for China, for example. He says that for every unit of say debt that’s taken out in China, which is government debt. It takes eight something like eight units of debt to create one unit of GDP. So in China for example you don’t have an economic multiplier you have an economic divisor, right?

DL: Exactly.

TN: So the more the Chinese government spends actually the less GDP growth which is weird, right? But it tells me that China is an economy that is begging for a market. A real market, okay? Rather than kind of central planning and you and Europe. I’m sure you’re very familiar with the Soviet Union. I studied a lot of that in my undergrad days very familiar with the impact of central planning. China there’s this illusion that there is no central planning in China but we’re seeing with the kind of blow-ups in the financial sector that there is actually central planning in China.

And if you look at the steel sector you look at commodity consumption, these sorts of things it’s a big factor of china still, right? So but it’s incredibly inefficient spending. It’s an incredibly inefficient way and again it’s a market that is begging for an open economy because they could really grow if they were open but they’re not. They have a captive currency they have central planning and so on and so forth.

Now I know some of the people watching, you’re going to say you’ve never been to China, you don’t understand. Actually I have spent a lot of time in China, okay? I actually advise China’s Economic Planners for about a year and a half, almost two years on the belt and road initiative. So I’ve been inside the bureaucracy not at the high levels where they throw nice dinners. I’ve been in the offices of middle managers for a long time within the Chinese Central Government so I understand how it works and I understand the impact on the economy.

DL: Don’t you think it’s interesting though that despite the evidence of what you just mentioned. And how brutal it has been because it’s multiplied by 10. How many units of debt are required to generate one unit of GDP in a little bit more than a decade? Don’t you find it frustrating to read and hear that what for example the United States needs is some sort of central planning like the Chinese one. And that in fact the the developed economies would be much better off if they had the type of intervention from from the government that China has?

TN: Sure, well it’s it’s kind of the fair complete that central bankers bring to the table. I have a solution. We need to use this solution to bring fill in the blank on desired outcome, okay? And so when central bankers come to the table they have there’s an inevitability to the solution that they’re going to bring. And the more we rely on central bankers the more we rely on centralized planning. And so I’ve had so many questions over the last several years, should the us put forward a program like China’s belt and road program, okay?

We know the US, Europe, the G20 nobody needs that, okay? Why? Because Europe has an open market and great companies that build great infrastructure. The US has an open market and although European infrastructure companies are better. The US has some pretty good companies that build infrastructure in an open market. So why do we need a belt and road program? Why do we need central planning around that? And we can go into a lot of detail about what’s wrong with the belton road and why it’s not real, okay? But that type of central planning typically comes with money as the as kind of the bait to get people to move things. And so we’re already doing that with the FED and we’re already doing that with treasure with money from the treasury, right?

And if you look at Europe you’re doing it with the ECB. You’re doing it with money from finance ministries. The next question is, does the government start actually taking over industries again? And you know maybe not and effectively in some ways they kind of are in some cases. And the real question is what are the results and I would argue the results are not a multiplier result they are a divisor result.

DL: Absolutely. Absolutely it is we saw it for example. I think it’s, I mean painfully evident in the junk plan in Europe or the growth and jobs plan of 2009 that destroyed four and a half million jobs. It’s not easy to to achieve this.

TN: You have to try to do that.

DL: You have to really really try it, really try.

I think that you mentioned a very important factor which is that central banking brings central planning because central banks present a program of monetary easing of monetary policy. And they say well we don’t do fiscal policy but they’re basically telling you what fiscal policy has to be implemented to the point that their excuse for the lack of results of monetary policy tends to be that the that the transmission mechanism of monetary policy is not working as it should. Therefore because the demand for credit is not as much as the supply of money that have invented. They say, well how do we fill in the blank? Oh it has to be government spending. It has to be for planning. It has to be so-called infrastructure spending from government.

You just mentioned a very important point there is absolutely no problem to invest in infrastructure. There’s never been more demand for a good quality infrastructure projects from private equity, from businesses. But I come back to the point of of central banks and a little bit about your view. How does prolonging easing measures and maintaining extremely low rates affect these trends in growth and in these trends in in productivity?

TN: Well, okay, so what you brought up about central banks and the government as the transmission mechanism is really important. So low interest rates Zerp and Nerp really bring about an environment where central banks have forced private sector banks to fail as the transmission mechanism. Central banks make money on holding money overnight, that’s it. They’re not making money necessarily or they’re not doing it to successfully to impact economies. They’re not successfully lending out loans because they say it’s less risky buying bonds. It’s less risky having our money sit with the Fed. It’s less risky to do this stuff than it is to loan out money. Of course it’s less risky, right? That’s goes without saying.

So you know I think where we need to go with that is getting central banks out of that cycle is going to hurt. We cannot it… cannot hurt, well I would say baby boomers in the West and and in Northeast Asia which has a huge baby boomer cohort. Until those guys are retired and until their incomes are set central banks cannot take their foot off the gas because at least in the west those folks are voters. And if you take away from the income of that large cohort of voters then you’ll have, I guess I think from their perspective you’ll have chaos for years.

So you know we need to wait until something happens with baby boomers. You tell central banks and finance ministries or treasuries will kind of get religion and what will happen is behind baby boomers is a small cohort generally, okay? So it’s that small cohort who will suffer. It’s not Baby Boomers who will suffer. It’s that small cohort who will suffer. It’s the wealth of that next generation that Gen x that will suffer when central banks and finance ministries get religion.

So we’re probably looking at ten more years five more years of this and then you’ll see kind of… you remember what a rousing success Jeff Sax’s shock therapy was, right?

DL: Yeah.

TN: So of course it wasn’t and it’s you know but it’s gonna hurt and it’s gonna hurt in developed countries in a way that it hasn’t hurt for a long time. So that kind of brings to the discussion things like soundness of the dollar, status of the Euro that sort of thing. I think there are a lot of people out there who have this thesis. I think they’re a little early on it.

DL: Yeah, I agree.

TN: So economists you know these insurance people see it from a macro perspective but often they come to the conclusion too early. So I think it’s a generational type of change that’ll happen and then we start to see if the US wants the dollar to remain preeminent. They’re going to have to get religion at the central bank level. They’re going to have to get religion at the fiscal level and really start ratcheting down some of the kind of free spending disciplines they’ve had in the past.

DL: Yeah, it’s almost inevitable that you’re in a society that is aging. The net prison value of bad decisions for the future is too positive for the voters that are right now with the middle age, in a certain uh bracket of of age. Me, I tried the other day my students I see you more as the guys that are going to pay my pension than my students. So yeah…

TN: But it’s you and me who will be in that age bracket who will pay for it. It’s the people who are 60 plus right now who will not pay for it. So they’ll go through their lives as they have with governments catering to their every need, where it’s our age that will end up paying for it. So people our age we need to have hard assets.

DL: Absolutely.

TN: You know when the time comes we have to have hard assets because it’s going to be…

DL: That is one of one of the mistakes that a lot of the people that follow us around. They they feel that so many of the valuations are so elevated that maybe it’s a good time to cash in and simply get rid of hard assets, I say absolutely the opposite because you’ve mentioned a very important thing which is this religious aspect that we’ve that we’ve gotten into. And I for just for clarity would you care to explain for people what that means because…

TN: I say get religion? I mean to become disciplined.

DL: I know like you because that is an important thing.

TN: Yes, sorry I mean if anybody but to become disciplined about the financial environment and about the monetary environment.

DL: Absolutely because one of the things that people tend to believe when you talk about religion and the the state planners religion and and central bank’s religion is actually the opposite. So I wanted to write for you to very make it very clear. That what you’re talking about is discipline you’re not talking about the idea of going full-blown MMT and that kind of thing.

TN: No. I think if there is if there is kind of an MMT period, I think it’s a I don’t think it’s an extended period. I think it’s an experiment that a couple of countries undertake. I think it’s problematic for them. And I think they try to find a way to come back but…

DL: How do you come back from that because one of the problems that I find when people bring the idea of well, why not try. I always, I’m very aware and very concerned about that thought process because you know I’ve been very involved in analyzing and in helping businesses in Argentina, in Hawaii, in Brazil and it’s very difficult to come back. I had a discussion yesterday with the ex-minister of economy of Uruguay and Ignacio was telling me we started with a 133 percent inflation. And we were successful in bringing it down to 40 and that was nine years.

TN: Right. So, yeah I get how do you come back from it look at Argentina. look at Zimbabwe. I think of course they’re not the Fed. They’re not you know the EU but they are very interesting experiments when people said we’re going to get unhinged with our spending. And we’re going to completely disregard fundamentals. Which I would say I would argue we are on some level disregarding fundamentals today but it’s completely you know divorced from reality. And if you take a large economy like the US and go MMT it would take a very long time to come back.

DL: Absolutely.

TN: So let’s let’s look at a place like China, okay? So has China gone MMT? Actually, not really but their bank lending is has grown five times faster than the US, okay? So these guys are not lending on anything near fundamentals. Sorry when I say five times faster what I mean is this it grew five times larger than the bank lending in the US, okay? So China is a smaller economy and banks have balance sheets that are five times larger than banks in the US. And that is that should be distressing followers.

DL: Everybody say that the example of China doesn’t work because more debt because it’s growing faster what you’ve just said is absolutely critical for for some of our followers.

TN: Right, the other part about China is they don’t have a convertible currency. So they can do whatever they want to control their currency value while they grow their bank balance sheets. And it’s just wonderland, it’s not reality so if that were to happen there are guys out there like Mike Green and others who look at a severe devaluation of CNY. And I think that’s more likely than not.

DL: Yeah, obviously as well. I think that the the Chinese government is trying to postpone as much as it can the devaluation of the currency based on a view that the imbalances of the economy can be sort of managed through central planning but what ends up happening is that you’re basically just postponing the inevitable. And getting a situation in which the actual devaluation when it happens is much larger. It reminds me very much. I come back to the point of Argentina with the fake peg of the peso to the dollar that prolonging it created a devastation from which they have not returned yet.

TN: Right. And if you look at China right now they need commodities desperately, okay? Metals, they need energy desperately and so on and so forth. So they’ve known this for months. So they’ve had CNY at about six three, six four to the dollar which is very strong. And it was trading a year ago around seven or something like that. So they’ve appreciated it dramatically and the longer they keep it at this level. The more difficult it’s going to be on the other side. And they know it these are not stupid people but they understand that that buying commodities is more important for their economy today because if people in China are cold this winter and they don’t have enough nat gas and coal then it’s going to be a very difficult time in the spring for the government.

DL: And when you and coming back to that point there’s a double-edged sword. On the one side you have a currency that is out to free sheet are artificially appreciated. On the other side you also have price controls because coal prices are limited by the government. And therefore you’re creating on the one hand a very big monetary hole and on the other hand a very big financial hole in the companies that are selling at a loss.

TN: That’s true but I would say one slight adjustment to that things like electricity prices are controls. When power generators buy coal, they buy that in a spot market, okay? So coal prices have been rising where electricity prices are highly regulated by the government this is why we’ve seen blackouts and brownouts and power outages in China. And why it’s impacted their manufacturing base because they’re buying coal in a spot market and then they’re having to sell it at a much lower price in the retail market.

And so again this is the problem with central planning this is the problem with kind of partial liberalization of markets. You liberalize the coal price but you keep the electricity price regulated and if you don’t have the central government supporting those power plants they just blow up all over the place. And we’ve seen the power generators in the UK go bankrupt. We saw some here in Texas go bankrupt a couple years ago because of disparities like that and those power generators in the UK going bankrupt that’s the market working, right? So we need to see that in China as well.

DL: Yeah, it’s a very very fascinating conversation because on the other hand for example in Europe right now with the energy shortage we’re seeing that a few countries Spain, France, etc. are actually trying to convince the European Union, the European Commission to try to get into a sort of intervened market price in the in the generation business. Which would be just like you’ve mentioned an absolute atrocity very very dangerous.

TN: This creates a huge liability for the government.

DL: It creates a massive liability for the government. This is a key point that people fail to understand the debate in the European union is that, oh it’s a great idea because France has this massive utility company that is public. And therefore there’s no risk it had to be bailed out twice by the taxpayers. People tend to forget that you’re paying for that.

TN: But again this is what’s that block of voters who doesn’t really care about the impact 10 or 20 years down the road. That’s the problem. There’s a huge block of voters who don’t really care what the cost is because the government’s going to borrow money long-term debt. And it’s going to be paid back in 10 or 20 years and the biggest beneficiaries of this and the people on fixed incomes they actually don’t care what the cost is.

DL: Yeah, yeah exactly, exactly. There’s this fantastic perverse incentive to pass the bill to the next generation. And that obviously is where we are right now. Coming back to the point of the infrastructure plans and the belt and road plan. What in your view are the the lessons that we must have learned or that we should be learning from the Belgian road initiative?

TN: So here’s a problem with the Belton road and I had a very candid discussion with a senior official within China’s NDRC in probably 2015 which was early on, okay? And this person told me the following they said the Belgian road was designed to be a debt financed plan. What’s happening now, and again this was six or seven years ago, very early on in the in the belts and road dates. They said the beneficiary countries are pushing back and forcing us to take equity in this infrastructure, okay?

Now why does that matter well the initial build out of infrastructure is about five percent of the lifetime cost of that asset, okay? So if you’re if China is only involved in the initial build out they’re taking their five percent, it’s a loan and they get out. If they’re equity holders in that let’s say they’re 49 equity holders in an Indonesian high-speed rail then they become accountable for part of that build-out. And then they have to maintain the other 95 of the cost for the next 30 to 50 years. So they thought they were going to be one and done in and out. We do this infrastructure we get out they owe us money and it’s really clean what’s happened is they’ve had to get involved in the equity of those assets.

And so I’ve since had some uh government officials from say Africa ask me what do we do with the Belton road with china? Very simple answer force them to convert the debt to equity, okay? They become long-term involved on a long-term basis. They become involved in those assets and then they’re have a different level of interest in them in the quality maintenance and everything else but they’re also on the long-term basis accountable for the costs.

So they don’t just build a pretty airport that and I’m not saying this necessarily happens but they don’t just build a pretty airport that falls apart in five years, okay? They then have to think about the long-term impacts and long-term maintenance costs of that airport, right? And so but you know the original design of the Belton road was debt financing. Mobilizing workers and so on and so forth what it’s become is a mix of debt and equity financing. And that’s not what the Chinese government has wanted.

So I’ve been telling people for three or four years the Belton road is dead, okay? And people push back me and say no it’s not, you know think tank people or whatever. But they don’t understand the fundamental fact of how the Belton road was designed it was designed as a one-and-done debt financed infrastructure build out it’s become a long-term investment all around the world. So it’s a different program. It’s failed, okay?

They’re not going to make the money they thought yes they’ll keep some workers busy but they’re not going to make the money they thought. All of those assets, almost all those assets are financed in US dollars, okay? So they’re not getting their currency out. It’s not becoming an international unit like they had hoped. They’re it’s not they’re not clean transactions and so on and so forth. So this is what’s happened with the Belgian road. So the lesson learned is they should have planned better. And they should have had a better answer to you become an equity owner. And uh

I think you know if any western governments want to have kind of a belt and road type of initiative. They’re going to have to contend with the demand from some of these countries that they become equity owners. And I think that’s a bad idea for western governments to be equity owners in infrastructure assets so you know this is this is the problem.

Japanese have taken a little bit different because of where the Yen is and because of where interest rates are in Japan. Japanese have basically had kind of zero interest or close to zero interest on the infrastructure they’ve built out. And so they haven’t gone after it as aggressively as China has. They’ve had a much cleaner um structure to those agreements. And so they’ve been, I think pretty successful in staying out of the equity game and staying more focused on the debt financing for their infrastructure initiatives.

DL: Oh, absolutely big lesson, big lesson there because the we see now that the vast majority of those projects are impossible to the debt is impossible to be repaid. There’s about 600 billion dollars of unpayable debt out there. And we also have the example from from the internationalization of the French, Spanish, Italian companies into Latin America that they fell into the same trap. They started with a with a debt-financed infrastructure build type of clean slate program that ended up owning equity. And in some cases with nationalizations hopefully that will not…

TN: And watch for debt to equity conversions in these things. It’s good. There’s going to be huge pressure because the Chinese say the exit bank the CDB. A lot of these organizations are going to be forced to convert that debt to equity and then unload it on operating companies in China. They’re not going to want to do it but we’re going to start to see more and more pressure there over the next couple of years.

DL: Great! Well I’m absolutely convinced that will happen. Tony, we’ve run out of time so it’s been an incredible conversation lots of things that are very very interesting for our followers. We will give all the details to follow you and to get more information about your company in the details of the of the video. And thank you so much for your time. I hope that that we will be able to talk again in a not too distant future.

TN: Thank you Daniel. Anytime. Thank you so much.

Tutorials

How to find the forecast price of silver?

Post author By admin
Post date October 14, 2021

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.

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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.

Podcasts

Tony Nash in Money MBA Podcast

Post author By admin
Post date November 18, 2019

Money MBA Podcast

Tony Nash joins Jon Kutsmeda of Money MBA Podcast for a deep discussion on AI, enterprise computing, procurement & supply chains, automation, markets, the future of work, and the rise of the machines.

Listen to the Money MBA Podcast here.

Tags artificial intelligence, automation, bad data, big data, bitcoin, cryptocurrency, excel, machine learning, Money MBA Podcast, MoneyMBA, podcast, robotics

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