Trading Insights: Thinking ‘Future Back’, with J.P. Morgan’s Head of Corporate Futurism

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Debora Kantt: So from a methodological standpoint, Foresight starts by doing what we call horizon scanning, which is a type of futures research in which we look to identify what we call weak signals. Which are the first seeds or symptoms of a future trend or change that usually need another to 10 years to mature into a trend. And if you identify them soon enough, they represent a huge competitive advantage because you can adjust your strategic priorities sooner and even uncover new revenue streams in advance.

Eloise Goulder: Hi, I'm Eloise Goulder, head of the Data Assets & Alpha Group here at J.P. Morgan, and today I'm so excited and in fact intrigued to be joined here by Debora kantt, who is head of Corporate Futurism here at J.P. Morgan and who has a lot to say about predicting the future. So Debora, thank you so much for joining us here today.

Debora Kantt: Thank you for your interest in the future and corporate futurism.

Eloise Goulder: So Debora, many of our listeners won't be familiar with futurism as a concept at all. So can you explain what it is you do?

Debora Kantt: Sure. I'm a certified futurist, which means that my job is to investigate the future through different methods and tools. A futurist is a person trained to explore possible futures. Imagine you had a way to understand in advance what disruptions might evolve over the next decade. Imagine there was a discipline that could prepare you for a world that doesn't exist yet. What new products, services, experiences might these futures bring? And how would you adapt your strategies if you knew what was coming? This is what a futurist does. In my case, by applying a discipline known as strategic Foresight that in the U.S. was actually born seven decades ago at RAND Corporation. RAND is a think tank that does research and development, especially for the U.S. Army Air Forces among others. And it makes sense because many of the technologies that we use today commercially were firstborn in the military. Think about the GPS or even the internet. Now, the biggest influence in Foresight in Europe came from France, from philosopher and futurist Gaston Berger. So this is a discipline that I introduced at J.P. Morgan four years ago with the goal of building anticipatory forward-thinking and future back leadership and giving executives and senior leaders the tools to think about the markets, clients and products of the future. Because as Ray Kurzweil once said, "An invention needs to make sense in the world in which it is finished, not in the world in which it started." In today's world, adapting means you're already late. You need to be able to anticipate.

Eloise Goulder: Well, it's fascinating stuff. And of course predicting the future is the holy grail for our corporate lives and also for our investing lives in predicting asset returns. So Debora, when it comes to your, your role here at J.P. Morgan, what is it specifically that you do?

Debora Kantt: The work that we do has three pillars. The first one are future awareness master classes for executives and our clients to help land the seed for a new type of thinking, think bigger and understand what might become possible in the immediate near and far futures, especially 10 years and beyond. And then the second pillar is futures research and advisory. We want to spot emerging disruptions before they fully manifest into trends, before they become obvious to the industry. The research that we do in Foresight is called horizon scanning, and it has to do not only with knowing where to look but also how to look. We are trained on spotting these type of emerging disruptions and we look to understand, for example, what interesting startups are emerging across niche topics, what interesting patents are being filed and more. And the third pillar are special projects. That is when we move from discovery to action and we look to test or pilot certain specific solutions or technologies that we believe will have impact across different businesses here at the firm.

Eloise Goulder: So Debora, can you tell me about how exactly you forecast the future and also how that differs from the more traditional backtesting type methods that many investment professionals use?

Debora Kantt: That's a great question. So I always like to say that my job consists on doing archeology in reverse because in the same way that a historian or an archeologist can study the past based on facts and statistics and evidence about what happened, we can study the future that is what might happen based on evidences of that future that we see are emerging in the present. So future back, which actually Foresight we call back casting, has to do with understanding the multiple possible futures that might unfold, including who your clients, competitors, and core business models are going to be in those futures. And standing in a preferred future and working backwards into the present. So that is what we call future back or back casting. Now, forecast is very different because forecast is based on extrapolating today's trends into the future. And that might work well in context that are relatively stable or linear.

Eloise Goulder: Yeah.

Debora Kantt: But that's not how the world operates today. The world today is volatile, energetic, exponential, uncertain. And that is where Foresight works best because again, we don't rely on a single probable or most likely to happen future that might unfold, but on an array of multiple possible and alternative futures that might manifest so that you have strategies that are robust enough to withstand future shocks. And that includes even the futures that are based on wild guards, which are kind of like black swans, so highly strategic events that have a very low probability of happening. But if they do end up manifesting, they end up disrupting entire markets and industries. In the past we relied on certainty to survive. We needed to know if the fruits from a specific tree were toxic or if that noise that we were hearing belong to a hungry predator getting closer to us. So our brain evolved to reward certainty and punish uncertainty. The future is uncertain by nature, therefore it triggers this threat response in our brain. And that is a problem in particular in our world that is increasingly uncertain, increasingly volatile, and in a world in which technologies are progressing exponentially and in many cases faster than our cognitive capacity to process them. Think about a shift in longevity that increases human lifespan drastically or a shift in human cognitive capacity or humans evolving self-repairing bodies or or self-repairing smart cities. So we have to acknowledge the existence of all of those futures and then stand in a preferred future and work backwards into the present. And that is what back testing or future back is all about.

Eloise Goulder: And it's striking when you get those examples that you are really talking about the long-term future. I assume you are talking 5, 10 plus years ahead.

Debora Kantt: Exactly. But soon we will see when we start talking about linear thinking and other forms of cognitive bias, that because these technologies are progressing exponentially.

Eloise Goulder: Mm-hmm.

Debora Kantt: What do you believe will end up happening in 10 years? Will most likely happen in the next two or three or even sooner?

Eloise Goulder: Yes. I love your point that linear thinking doesn't necessarily model the real world that we are in. And for what it's worth, our quant research colleagues have made similar points when it comes to investing and they've shown that traditional back testing techniques will often assume linear relationships and a continuation of a prior trend. And in many cases, that doesn't work in reality. And that's actually why they have advocated the use of complex machine learning strategies to predict the future because those strategies do not assume linearity. And your point about behavioral biases, I think that's also fascinating, this idea that we as humans are not set up to instinctively think about the future. And this is why you need to be very intentional in your work in futurism. Is that correct?

Debora Kantt: Yes. That's absolutely correct. And there's actually a stream of work in Foresight called Neuro Foresight that works at the intersection of neuroscience and strategic Foresight. And that has to do with understanding how our brain processes the future, how we deal with uncertainty and emerging disruptions and how our brain responds to that. And long story short, we are not good at dealing with the future. Actually, scientists from Washington University, they did an experiment in which they used brain scans to take pictures of people's brains as they were remembering a past event and compare them with pictures as they were imagining a future event. And they made an important discovery. We used the same regions in our brain, the same neural networks to recall memories than to imagine the future, which means that we forecast the future by referencing the past, which means that most of us can only imagine what we already know. But we know that the future will not be a linear extrapolation of the past. It will have some elements of the present, but not necessarily a linear extrapolation of the present. So that makes it even harder. And not only that, there is also a decision making bias called normalcy bias that makes us underestimate the likelihood of a disruptive event. And there's a very famous meme of a dog sitting calmly in a house that is catching fire and the dog keeps on saying, that's fine, even though the fire keeps getting closer to him, he keeps on saying, "This is going to be fine." Because the brain wants to believe that what has been normal so far will continue to be normal in the foreseeable future, which makes us underestimate their transformational shifts or disruptions that lie ahead. And of course, it leads to poor decision making.

Eloise Goulder: Absolutely. And I think that particularly holds true if there's friction or hard work or effort associated with a change. I'm just thinking about the classic investment bias of status quo bias, that if you hold a portfolio, you are typically disincentivized to change that portfolio unless you have very, very clear evidence that you need to. So that friction can also make change very difficult.

Debora Kantt: It is exactly that. And to your point, linear thinking is a form of cognitive bias on its own, and it is based on the fact that all the technologies that have been evolving for at least 50 years now are exponential. Why? Because they are based on a law called Moore's Law. Gordon Moore was the founder of Intel, the chip manufacturing company. And back in 1965, he observed that the number of transistors on a microchip was doubling every two years, which meant that computing processing power was predicted to increase exponentially over time because transistors don't cheap the more computing power. And this law has been true for more than 50 years now. It explains almost all the advances we're seen in artificial intelligence, virtual reality, augmented reality, synthetic biology, blockchain, and more. So the point here is that the technologies that we're building are doubling in power every 18 to 24 months progressing exponentially. But we are not because we are linear thinkers. We have all the suspicious, thousands of years ago in the savannas of Africa where nothing really changed much. So our brain evolved as local and linear, but changing the world, today's global and exponential. And that poses a problem.

Eloise Goulder: It's such a fascinating time to be having this discussion because as you say, the human brains haven't evolved a lot in the last few thousand years. And yet the technology that we work with, machine learning, AI, AGI, have all evolved so rapidly.

Debora Kantt: And the problem, at least the problem to us humans, is that exponential growth is deceptive, meaning that it doesn't seem to grow that fast at first, but all of a sudden it does and it ends up disrupting entire markets and industries. But you mentioned complexity, you mentioned machines, and interestingly, the most complex machine ever created was the human brain. A collection of 86 billion neurons doing parallel processing, which is something that most machines struggle with because traditional computing process information as if they were following a recipe, right? They actually process one instruction at a time. But biological systems don't because think about the leaves on a tree, they all grow at the same time. So traditional computing systems cannot process information in parallel. They do it linearly, unless you're talking about neuromorphic computing systems, which is an emerging and very interesting field in which we look to create artificial neurons and synapses to process information in a way similar to how our brain does. Think about, for example, learning a new language. Every time you hear the word bonjour, hello in French, the neurons in your auditory cortex sends signals to the neurons in the language center of your brain. And with each repetition, every time you hear that word again, then the repetition makes that connection stronger.

Eloise Goulder: Mm-hmm.

Debora Kantt: If you stop using that word for too long, then the brain that is always looking to be energy efficient starts eliminating the connections that are not needed. And so these neuromorphic computing systems, what they do is they try to emulate this capacity of our... the biological algorithms in our human brain so that you have algorithms that again, gets stronger or weaker depending on how much information flows through them. Now simulating an entire version of the human brain, even a simplified version of the human brain, requires around one exaflop of computing power. To give you an idea, that's a system that can do a billion, billion operations per second. That's a one followed by 18 zeros. So even when you think about artificial intelligence algorithm, like more traditional algorithms, they are all inspired in our own biology in the human brain.

Eloise Goulder: Well, that's very inspiring, as you say to know. So let's say we are sold that understanding the future is absolutely critical to our business and to our lives. Then how do you go about modeling those possible scenarios in the future? And what are the key examples of areas that you are really delving into at this stage?

Debora Kantt: So from a methodological standpoint, Foresight starts by doing what we call horizon scanning, which is a type of futures research in which we look to identify what we call weak signals.

Eloise Goulder: Mm-hmm.

Debora Kantt: Which are the first seeds or symptoms of a future trend or change that usually need another 5 to 10 years to mature into a trend. And if you identify them soon enough, they represent a huge competitive advantage because you can adjust your strategic priorities sooner and even uncover new revenue streams in advance. Now, there are specific domains in which I go to scan these weak signals, for example, biocomputing because we are entering a decade in which biology will be integrated into almost everything. In fact, McKinsey estimated that at least 60% of the world's inputs will be produced biologically by 2030 and more and more we'll see that bio becomes tech and tech becomes bio. But what I am mostly interested on is not on traditional biotech approaches, but on the unexpected ones. Because it is not a single technology as a standalone, what will create the business models of the future, especially when we're talking about exponential technologies. But the unexpected synergies that these technologies or new business models create when they start colliding with each other. And one example is discovering that we can use cells for computing because of their impressive low energy consumption and parallel processing capabilities. So what if instead of computers made of metal and plastic, we could create biological computers made of cells and neurons for computational tasks. And this is the field known as biocomputing that instead of how transistors in traditional digital computing work, that is creating logic gates by allowing the flow of or block of electricity through them. We could use biological systems, we could use DNA proteins and enzymes to build logic gates for computing through chemical signals instead of through electricity as transistors do. And that's really interesting to me because so far everything in the history of computing has been driven by magnetism and electricity, and now we're talking about using cells for computing. And that's a huge paradigm change. And of course there are challenges. Cells are unpredictable, they are hard to control, but on the other hand they are everywhere. And computing based on cells is almost free because billions of bacteria just need a little bit of sugar water to grow. In comparison, it is estimated that we're going to need $10 billion to build the next generation of AI systems. And also computing based on cells has the power and the potential to change pretty much anything. Every industry imaging, even a system that can self-repair, adapt, learn on the fly, and adjust to different stimuli and responses. Very much to like our brain does. Imagine even cells embedded in a future spacecraft. If you are launching a space mission, which you're going to do more and more in the upcoming decade because of the demonetization of this technology driven by advances in reusable rockets. You don't want that spacecraft to be coming back to earth every time it needs maintenance. So what if you could have cells embedded in that spacecraft that can sense damage and trigger a repair response? What if you could have self-repairing cars, self-repairing roads, even maybe in the far future self-repairing body as well. So you can start to see all the impacts that this piece of natural technology that has been evolving for 4 billion years now has, it only makes sense that we start using it for computing as well.

Eloise Goulder: It's fascinating stuff. And Debora, are there any other topics that you are really delving into where you've seen those initial weak signals?

Debora Kantt: Yes, there are. There's a really interesting field known as effective computing, which is a discipline actually born decades ago at the MIT by researcher and engineer Rosalind Picard through which AI systems are learning to understand, express and simulate human emotions. The field is also known as emotion AI or artificial emotional intelligence. And it has a lot of implications from fraud detection to creating more empathetic AI assistance. To close and to your point, exponential technologies progress gradually than suddenly. And so the goal for us in spotting these weak signals is trying to answer what are we seeing that others are not? And this is called the hidden future and how did we gain this perspective? But at the same time, what are others seeing that we are not? And these are our blind spot futures. So what disruptions are we underestimating and what assumptions are we making about the future? Then we also look at answering what are the futures that are open to everyone? That is what is everyone seeing regarding the future and how are you preparing for predictable change in your industry? And then what are the unknown futures which are unknown to everyone, to you and others? And do you have strategies for unexpected scenarios? And what steps can you take to become more comfortable with uncertainty? So it's not about only being ahead of the crowd, it is about having the possibility to influence the future that ends up manifesting.

Eloise Goulder: That's absolutely fascinating. The idea that you have these hidden futures, these blind spot futures, the unknown wild car type futures, and then the known, the opened futures, which we also shouldn't forget, should we? Because in many cases we are aware of a trend, let's say we just don't necessarily act on it, particularly with the behavioral biases, the status quo type biases, the friction that may be preventing us to act on that probable future state.

Debora Kantt: Absolutely agree. So we need to keep in mind that there's a horizon one, a horizon two and a three. So ideally we should have strategies across these three horizons and understand what can be done today, but also what from the far future can we start bringing to the present to start innovating today and gain competitive advantage.

Eloise Goulder: And I'm just thinking all of these actions we can apply to our corporate lives in the sense of where are we focusing to future-proof our businesses. But we can also apply them to our investing lives. And many of the topics you mentioned have parallels with sustainable investing, truly thinking about the future state of the economy and building a portfolio that should work in that future state. So Debora, before we close, can I just ask what's next? I mean, what a person to be asking that question to, but from the perspective of your workflow, Debora and futurism as a topic overall, what is next for you?

Debora Kantt: So what's next for me and my team is keep on working with senior executives and business leaders to plant the seed for a new type of thinking and to be able to move from BAU, from business as usual to DAU, to disruption as usual. Because as I always say, we're entering a decade in which sci-fi is becoming sci fact. And the more exposed to these type of weak signals you are, the less shockable your mind becomes. In terms of innovation we are just getting started because think about this, it took us in the 1920s, 40 years when the telephone, the rotary dial phone was invented to move from the rotary dial phone to a phone with an numeric keypad. And then it took us another 20 years just to remove the cable of the phone. Today you have startups, bioprinting hearts with living cells in space because microgravity and radiation acceleration process. So you can study what happens to those organs on an off planet environment, much faster, 20 times faster than here on earth. You have startups that are delivering space, pharma, innovative solutions, creating, building, designing cancer drugs in space because in the absence of gravity, proteins crystallize better and this leads to better quality design in drugs. So clearly today, the sky is not even the limit, and we need a strategy to thrive in a world that is moving faster than our ability to cognitively process it.

Eloise Goulder: Thank you so much, Debora, with all of these concepts, thinking future back, overcoming these inherent ingrained behavioral biases, training us to think more in a complex and a a non-linear way. I mean, they're so relevant to us in the investing sphere. So thank you so much, Debora, for taking the time to speak with us here today.

Debora Kantt: Thank you very much. As a great futurist is called Alvin Toffler once said, "If you don't have a strategy, you are part of someone else's strategy." So my strategy is helping executives and listeners get future ready. So thank you for being so present today.

Eloise Goulder: And thank you also to our listeners for tuning into this biweekly podcast series from our group. If you have feedback or if you'd like to get in touch, then please do go to our website at jpmorgan.com/market-data-intelligence where you can reach out via the contact us form. And with that we'll close. Thank you.

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