FYI - For Your Innovation: Big Ideas 2023: Technological Convergence

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Hey everyone and welcome back to FYI, the four-year innovation podcast. I'm Michael

Cromer, a product marketing manager here at Arc. So according to Arc's research,

five innovation platforms are converging to create unprecedented growth trajectories.

Artificial intelligence is the most important catalyst, its velocity cascading through all

other technologies. The market value of disruptive innovation platforms could scale 40% at an annual

rate during this business cycle from $13 trillion today to $200 trillion by 2030. In 2030, the market

value associated with disruptive innovation could account for the majority of the global equity

market capitalization. Our Chief Futurist, Brett Winton, has more on today's episode. Please enjoy.

Hi, I'm Brett Winton, Chief Futurist at Arc Invest and I'm extremely proud to talk about

big ideas 2023, our annual report on the state of technology and how we see technology changing

and evolving. Today, I'm going to talk about convergence. This has been a year of convergence.

There's no question that technologies are reinforcing each other and expanding even

faster than we anticipated a year ago and two years ago. There are risks of investing in innovation

and this is a disclosure slide talking about some of those risks. I think it's fair to say that

technology investing involves all kinds of known and unknown uncertainties and the actual results

could and likely will differ materially for how we think they will. We think part of the value

app for us at Arc Invest and the research we do is we do try to quantify and put numbers to

the technologies that are growing and changing the world and really trying to determine

how big and how valuable these technologies are going to be and then comparing those numbers to

how they're valued in the marketplace today. That's how we identify the inefficiencies that

we try to invest in to take advantage of the asset approval that we anticipate.

It's clear to us, given how technologies are reinforcing each other, that this really is a

technological boom, that future historians will look back upon this business cycle and say

we can't believe that all of these technologies were hitting critical stages of inflection

at the same time. In fact, if you total up all of our forecasts, you'll find that we believe

that disruptive technologies that we focus on are going to accrue hundreds of trillions of

dollars in value over the course of this business cycle through 2030. Today, we think that disruptive

technologies are valued in the marketplace at roughly $13 trillion and we think the value

approval will exceed $200 trillion by 2030. More than half of global equity market capitalization

will be comprised of disruptive technologies and the innovation platforms that we focus on.

It's our reminder. These are the five innovation platforms that we think will define this decade.

Public blockchains, particularly cryptocurrencies, smart contracting protocols,

and the digital wallets that allow people to access those public blockchains will, in our view,

change people's financial lives. They'll change the incentive structures for how capital is deployed

in the economy and they'll reduce the net drag, the economic rent that's charged by financial

intermediaries on every single transaction worldwide. We believe that public blockchains

are going to scale into the tens of trillions of dollars in value over the course of this decade.

Artificial intelligence, I think everybody can see that the pace of change is accelerating

with artificial intelligence. This innovation platform has the steepest cost decline of any of

our innovation platforms and it's most critical to catalyzing other innovations, as I'll talk about.

The AI software is going to become the dominant form by which software advance is delivered over

the course of this decade and command more than $10 trillion a year in revenue, in our view.

Multiomic sequencing is the idea, multiomics is actually a term that some of you might not

be familiar with, but it's not just the DNA, which is the recipe for your body that's important to

developing biological understanding of what's going on. It's also the

things that are on top of the DNA that control which genes are expressed, as well as all of the

data that's feeding from digital health platforms that help us to tie kind of what's going on at

the molecular biological level to the actual diseases that people have. And it's going to,

we believe, change cancer care with multiomic technologies that precision therapies will

be developed that will be worth trillions of dollars and that emerging capabilities in

programmable biology will change the way that food is produced and how expensive it is. On the

energy storage side, this for us comprises both electric vehicles as well as kind of

autonomous mobility solutions. And in over the course of this business cycle, this may have

the most tangible impact to people's day-to-day lives as owning a car, for example, could become

really optional for people even in the Western world, since it'll be cheaper to ride around in

the robot taxi that delivers you from place to place for tens of cents per mile is safer,

more convenient, and allows you to sit in the backseat and watch Netflix while you're

getting to or from work. And then the earliest and most emerging, I think, of these innovation

platforms is in the robotic space. Robots have been around for a long time in the industrial

automation setting. The advance here is robots that can operate alongside humans, reusable

rockets that can deliver low-earth orbit communications constellations that radically

reduce the cost of connectivity on a global basis and 3D printing that can allow manufacturing to

happen closer to the end user with an infinite variety of parts available to the manufacturing

entity regardless of supply chain vulnerability. And so these five innovation platforms are both

self-reinforcing in all the critical stages of inflection. And so one of the things, one of the

activities we've undertaken is to score these innovation platforms on the degree to which

they're converging is how does an advance in neural networks impact the rate of change for

next-gen cloud or the capabilities of multi-ohmic technologies or autonomous mobility? And so

this visualization on the right is actually a mapping of that convergence scoring. A few things

here. You can see that each color represents one of the innovation platforms I was talking about

in the previous slide and that this network graph actually emerges from that convergence mapping

to reveal the 14 underlying technologies that we focus on from digital wallops to advanced

batteries from adaptive robotics to precision therapies and that the five innovation platform

buckets we talk about emerge organically from this mapping of convergence. So you can see that

our kind of the definition of five innovation platforms maps to these 14 technologies and

these 14 technologies themselves are more tightly interwoven at that innovation platform level.

So the next-gen cloud is going to be required to allow neural networks to be trained and to operate

at scale. The capabilities of neural networks are going to reinforce and in fact be critical to

whether or not augmented reality glasses actually become viable devices for people to buy

and they'll feed straight into the capabilities of the smartphones that you have today.

So I talked about convergence. If you unpack this graph to measure the

relative convergence importance of each technology, you can see that neural networks are artificial

intelligence deep learning systems are by far the most important in terms of their ability to

catalyze other technologies. On the left is a quantification of that. Roughly the way in which

the convergence scoring is quantified is that an order of magnitude increase in the address of

the market for another technology is essentially a score one and everything is scaled on that

basis. So you can say that in advance in neural nets, if AI accelerates, we believe that corresponds

to basically four other technologies, roughly increasing their addressable market by an order

of magnitude. And so you can see it's tied into, advances in neural nets are tied into almost every

other technology that we focus on, but it's critically tied into, for example, autonomous

mobility and the ability of adaptive robotics. And so one of the advances that we've seen over

the last year that really gives us confidence across all of the technologies is that neural nets,

AI capability is happening faster than even experts in the field anticipated. Inverting it,

this is a measure of how sensitive the technologies we focus on are to other catalysts. So autonomous

mobility systems are the most sensitive to accelerations in other areas. This makes a

conceptual sense that a reduction in the cost or an increase in the energy density of battery systems

means that you can have more form factors that the aerial drones will become more capable and

have longer range at lower costs. And in advance in neural networks allows and in fact is required

for autonomous mobility to operate in really challenging open space driving situations,

for example. And so essentially accelerations in other technology lead to a potential to order

of magnitude increase in the addressable market for autonomous mobility in our view.

And so just to focus again on artificial intelligence and how that acceleration is

feeding into these other technologies, it's clear that advances in AI are driving, so to speak,

robotaxies. So in Tesla's AI day, they talked about how the system can approach an intersection

that it's never seen before and understand essentially the taxonomy of how you can drive

through that intersection. Where are the likely pathways of travel? How do lanes merge or unmerge

in, if you have two left turn lanes, how does that feed into the four lane road on the other side,

for example. And the way they solve this problem is using what's called the transformer architecture,

which was originally introduced in 2017 as a way to make AI systems better at translating language.

So in advance in an AI language translation system, which became the advance that has driven all of

the innovation you've seen in large language models and AI natural language processing fed

directly through into the ability of a robotaxie to understand an intersection. And so this is,

and it's not just robotaxies. If you look at long read sequencing within the multiomics

technology space, this is a long read sequencer, something that can read your genome. And instead

of cutting up the genome into really little bits, it constructs the genome out of longer

clumps of DNA and stitches them together to understand the genome and actually to more

completely understand the genome than is possible with small bits. One of the drawbacks that that

approaches is those DNA chunks had had a higher error rate. Well, by using that same transformer

technology and applying it to a long read sequencer, there is a 59% reduction in that error rate

realized over a couple of years of just deploying that transformer architecture against long

read sequencing. And so again, it's an advance that happened because we were trying to translate

language better with AI. And lo and behold, it turns out it makes a long read sequencing box

reduces its error rate by almost 60% without any change to the box itself. It's just an AI

software upgrade to the box. And it's not just long read sequencing. If you go over to the

robotic space, that same transformers advance from 2017, you would think, well, it makes sense

that it would be useful to be able to talk to a robot in natural language and say, hey, go pick

up that object over there. But this is actually at a more profound architectural level, where by

using that transformer architecture for neural nets and applying it to a robot, you can see that

the robot gets much more performant on tasks it's seen before. The error rate, it was only

completing it 70% of the time roughly without transformer architecture, without this AI language

architecture, helping it to understand the underlying tasks that it's doing. And then that

completion rate improved to 97%. And then on tasks that robots have never seen before, these papers

were robots that were in a kitchen being asked to lift up a spatula, for example. The completion rate

improved from 19%. So only one out of five times would get the thing right to more than seven out

of 10 times was the robot successfully completing this task. And so, you know, net of all of the

technologies we look at, the rate change in AI accelerating is the one that feeds most importantly

through to the other technologies. And so we see all of the innovation happening in the AI space

today and say, hey, this means that everything is going to go faster, not just AI, that multiomics

and energy storage, that robotics and public blockchains are all going to be driven forward

by advances in AI. And it's not just AI that's feeding through to other technologies here,

we're showing how kind of our convergence form indicates that, you know, advances in batteries

feed through to advances in intelligent devices. The iPhone that you buy today has three times as

much battery as the iPhone that you bought in 2008. And if you look at the critical path for

developing augmented reality goggles or VR headsets that are performative and can last long

enough to be interesting and not too heavy, well, it runs through the quality of the battery and the

density, energy density of the battery you can put into that headset. So an advance in electric

vehicles can actually accelerate the adoption and the performance of the intelligent devices

that we're going to buy and use to access the AI, advanced AI systems that we think are going to

be deployed over the next few years. Similarly, an advance in robotics also feeds into a more

capable set of intelligent devices. That we can launch a lower-thorbit satellite constellation

inexpensively allows that telecom satellite constellation to provide our smartphones with

capabilities that they simply didn't have a few years ago. T-Mobile is going to allow you,

a user of an iPhone, to access satellite connectivity from, you know, anywhere in the world where they

can strike an agreement. And conceptually, it could be anywhere in the world. It could be in

the middle of the ocean. And your iPhone will suddenly have a signal via a lower-thorbit

satellite constellation made possible by SpaceX's rockets. And if you look, you could have done this

in 1998, there was actually a lower-thorbit constellation lofted, but you would have required

a specialized headset that would have cost 15 times more than an iPhone. And your cost per minute

at the time would have been roughly 40 times more than what T-Mobile is going to monetize

of that. Whereas today, it's going to be the headset or the handset that you have, the smartphone

that you have. And T-Mobile is just going to bundle it in the plant. It'll become, you know,

part of this is what we expect cell phones to be able to do. And on the cryptocurrency side,

I think many don't appreciate that public blockchains and cryptocurrencies in particular

are not just a potential alternative currency that offers user self-sovereignty.

They're also an important energy tool. And so we have previously demonstrated that

if you deploy a solar system, you can provide somebody with 40% of their electricity needs.

But if you start making the system bigger than that, then the electricity you generate begins

to get more costly because you're generating too much electricity during very sunny parts of the

day. And the person can't use it at that time, so it's just spill over its waste effectively.

And so you can attach a battery system to that solar installation. And that helps somewhat,

but also you run up against the limit of the economic size of the battery system you can install.

If you attach Bitcoin mining as well, then you can make both the solar system and the

battery system larger. And anytime there's excess energy coming off the large solar system,

the Bitcoin miner can mine and compete in the economic game to produce Bitcoin.

And this allows a solar system to scale from 40% of the end-use needs of the user

all the way to 99 plus percent, effectively make the system grid independent. And it can do so

because you can build larger solar and a larger battery system since you have essentially an

outlet mechanism for the excess energy that the system produces into Bitcoin mining.

And so the size of the battery, which is represented on the y-axis here, gets larger,

the economic size of the battery, you can build gets larger as you attach Bitcoin mining.

And so a more valuable Bitcoin network actually feeds into more demand for battery systems.

So what is all of this add up to? These converging technologies are, we think,

going to lead to remarkable macroeconomic growth. And here we're presenting a long history of

macroeconomic growth and demonstrating in the purple bars that actually

discontinuous changes in the annual rate of real economic growth are the norm, not the exception.

Driven by technology over the course of distinct time periods defined by technological transitions,

we have gone from doubling and 10xing kind of the rate of macroeconomic growth per year.

Now, in the red bar here, you can see that the consensus forecast is this long technological

economic history is over. The assumption is that the advances that we've had all the way from

year one to year 2021, that that technological advance, that the march of technological history

is ending, we're at the end of technological history. We think that's actually wrong.

And the data would suggest, and you can see as we've transformed the x-axis here,

that a forecast consistent with technological economic history would suggest that we are moving

from a 3% real growth rate per year into a domain of 8% plus real growth per year.

What does this mean tangibly? It means by 2030 we could have 20,000 real GDP per capita,

as opposed to the consensus, which is 15,000 real GDP per capita. And the growth rate would not

slow down. We think it would accelerate from there. Now, this is a very crude forecast necessarily

because we're taking a very long data series and we're saying, well, this is what it looks like

could happen. So I would not be confident in this forecast if we weren't able to point to the

technologies that are going to deliver that result. A note of caution. Macro economic statistics

have a hard time taking on and embedding disruptive technologies. So somebody buying an

electric vehicle today, as you can see on the left, they're paying maybe one and a half times

the purchase price. This is comparing a Tesla Model 3 to a Toyota Camry. But they are reducing

their ongoing operating costs of that vehicle over time. So it looks like they're spending a lot of

money. In actuality, the total cost of ownership for that vehicle is lower. And the future expenses

are diminished. The amount of oil demand clearly falls off cliff. And even if you accommodate

the need for electricity or the need for repairs, you end up with a bringing forward of demand

and then a reduction of demand in future years. On the right was showing if somebody cuts the

cable cord, if they stop paying for pay TV and switch to streaming services, that looks like

degrowth from a macroeconomics perspective. They're spending less for TV. So it's a lower GDP.

But clearly from the consumer perspective, this is more valuable, entertaining. A consumer can

switch over to streaming for a lower aggregate cost and get the same number of entertainment hours

on demand without commercials. And so the disruptive technologies are often mismeasured

and can found the measurement of macroeconomic statistics. So with that as a caveat, we can

point to the technologies that we've modeled, the five innovation platforms, and say with some

degree of confidence that we think that that macroeconomic forecast of accelerating growth,

the one that's consistent with technological history, is likely to come true. If you look at

energy storage largely driven by robo taxis, we think that robo taxis are going to deliver

$26 trillion in real GDP incremental to consensus by 2030. And then robotics as well as they

infiltrate people's homes, and they allow manufacturing processes to accelerate, will

deliver an excess of $10 trillion in real GDP growth in incremental GDP relative to consensus

by 2030. Adding up those two innovation platforms alone, and those are ones that are

more likely to be captured in the macroeconomic measurements, would suggest that we are on the

green trajectory here rather than the red trajectory. So while I would be cautious about

this particular forecast, if I were using these data alone, the fact that our modeling of individual

technologies platforms that are at a critical stage of inflection suggest that the green

trajectory is right and going to be realized over the course of this decade suggests to me

that we are in a state of discontinuous change in macroeconomic growth. On the right side of

this forecast, I'd say there's less certainty about how kind of these technologies will be

measured in the macroeconomic statistics, but it's very clear given the cost decline in AI,

given the human health impact of multi-ohmic technologies, and given the efficiencies that

were likely to ring out of having truly digitized finance, that there could be tens or even hundreds

of trillions of dollars of additional macroeconomic product delivered by these technologies.

The largest bucket here, of course, is AI software, where we think the best way to think of AI

software is it is a knowledge worker force multiplier. So an analyst becomes that much

better. A CFO becomes that much more powerful and precise. An administrator can actually

manage more assets, and that would feed back into the real growth that we see in the economy

and the actual produced items that we get. So, of course, large macroeconomic growth,

large value add to the economy, we think will lead to large market value accrual.

On the left, we have roughly the state of the equity markets at the end of 2022.

You had $84 trillion in non-disruptive innovation-exposed market capitalization,

and roughly $13 trillion in disruptive innovation-exposed market value, inclusive of

public blockchain protocols. By 2030, we think that those legacy businesses, they might approve

value, but it won't be at any kind of extranormal rate. It will basically be on a real basis of

2% compounding, and that the innovation platforms, as they infiltrate every sector in the economy,

as they deliver profound productivity advances, are going to approve profound value. We think

more than $200 trillion in value will accrue to these innovation platforms, and all with

growth rates in the excess of 25%, and that the shape of exposure to equity markets will change,

that more than half of equity markets will be disruptive innovation-exposed, that public

blockchain protocols will be seen truly as a new financial category that

allocators will need to be exposed to, and that if you're not aggressively exposing yourself to

innovation now, you'll be left behind by the growth. Just like we think that the macroeconomic

growth is going to surprise to the upside driven by innovation, we actually think equity market

appreciation is going to surprise to the upside driven by innovation, and accruing to the benefit

of the companies that are enabling these innovation platforms that have ownership of the innovation

platforms themselves, or aggressively using these tools to enable them to deliver better cash flow

to their shareholders and better end product to their customers. That's how we think technologies

are converging, and what we think is in store for both the market and the economy over the next

decade. I appreciate the time and attention, and look forward to seeing how this technological boom

plays out. ARC believes that the information presented is accurate and was obtained from

sources that ARC believes to be reliable. However, ARC does not guarantee the accuracy or completeness

of any information, and such information may be subject to change without notice from ARC.

Historical results are not indications of future results.

Certain of the statements contained in this podcast may be statements of future expectations

and other forward looking statements that are based on ARC's current views and assumptions,

and involve known and unknown risks and uncertainties that could cause actual results,

performance, or events that differ materially from those expressed or implied in such statements.

Machine-generated transcript that may contain inaccuracies.