Cathie Wood and Brett Winton Discuss Big Ideas 2021

[Music] 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 arcs current views and assumptions and involve known unknown risks and uncertainties that could cause actual results performance or events to differ materially from those expressed or applied in such statements welcome to fyi the for your innovation podcast i'm brett winton i direct research for arc invest and here we have our fearless leader our ceo and cio kathy wood hi kathy how are you hey brett i'm fine how are you doing i'm doing great so what should we talk about today in today's podcast well i think we should talk about big ideas i mean really big ideas what do you think sounds good every every year we put out a big ideas presentation it's kind of a compendium of some of the research insights we've both had over the course of the year and what we think is going to be important this year and so we just put it out it's called big ideas 2021. kathy why don't you set it up a little bit what should we think about here we are are getting all kinds of questions about how sustainable what we're experiencing in the equity markets in the global economy how how sustainable are these new mega trends that seem to be evolving and i think what's great about big ideas is even we who we spend all of our time in the research and investing around these ideas and even we at the end of the year as we're summarizing how big these ideas are we are sometimes stunned when we pull away at how quickly this is happening so quickly and the other question we face here is is this a bubble now there's a lot of muscle memory around the tech and telecom bubble and bust and uh obviously that was not a good experience for a lot of people it was their first experience with this notion that the internet in that case was going to set off years and years of exponential growth opportunities and that did happen it actually happened but it did take for some of the technologies it took 15 to 25 years for the seeds that were planted during the tekken telecom bubble to bear fruit now we're ready for prime time they're bearing fruit now that we're here investors are skeptical they're afraid that uh there are going to be disappointments like we experienced back then well back then what happened is too much capital chased too few opportunities too soon the costs were way too high and the technologies were not ready in fact some of them didn't even exist and so of course that did end badly now the costs and the technology are at the right point so that we are experiencing exponential growth trends in five technology platforms involving 14 different technologies individual technologies and what we're seeing which this is what is astonishing to us i think as we talk about this we're seeing more and more convergence uh between and among the technologies and so we're seeing one s curve feeding into and accelerating another s curve upon another s curve and i think that's why this is happening so quickly so maybe i can uh lob it to you uh brett to riff a bit on that and and maybe we can start with deep learning which i think has been the big surprise sure and and i think deep learning is actually a nice almost continuation of the internet but even reflecting back on on dot com at least in our view there really was a single technology platform that a lot of hopes were being put on top of and and one of the i think you're absolutely right that convergence like the the capabilities of an individual technology that's itself building atop other technology platforms they get multiplicative as they converge and you just didn't have as many opportunities for almost like puzzle piecing together different technologies back then uh that you seem to now like you mentioned deep learning really that that's an area where the pace of change has surprised us if you were around the clock three years and asked whether or not deep learning was applicable to natural language processing and really smart experts you know experts in the field would say no language is too discontinuous and this innovation of kind of using neural networks to solve complex um problems and pattern recognition tasks is just not going to apply there well it turns out just over the past year we've gotten a proof point that actually it absolutely will and you will be able to do it in a way that it seems to like produce almost like high school english level compositions generated from scratch by a computer and so think about like all of the ways in which the world relies upon language to to make business processes work and and what a powerful tool automated generation of language could be and and so that's one area where deep learning is clearly having an impact and then also within the past i think it's within the past three months deep mind uh one of google's subsidiaries or alphabet subsidiaries announced alpha fold which is the ability to take the genetic information in dna and and predict what proteins would be generated from that genetic information the structure of the protein that would be generated which would give you an understanding of like what the actual disease or disorder coming off a mutated stretch of dna how it will manifest in the body and so give you a much better chance of targeting a drug against that disease this is something that has been it's an unsolved problem for the past 50 years of how to do that and and that deep mind seems to be able to use the same technology that we're using to solve language generation by computers to also figure out protein folding is indicative of both how dramatically large and interesting the opportunity set up applicable to deep learning is and also within the health space like you're only able to do that protein folding problem because you have the gene sequencers to sequence the genome to get yourself the information to train the algorithms and so kind of like gene sequencing in the cost decline there is enabling kind of the deep learning to build on top of it which will then enable the next you know maybe it's gene editing to design the treatment that plugs into that particular protein so yeah i i think uh in mentioning each of those technologies we can talk about how quickly costs are falling so in the case of ai training costs are dropping about 37 per year when costs drop that quickly really there's a it unleashes a lot of creativity and problem solving that couldn't have taken place uh at any other time and we're seeing uh a.i models growing tenfold per year i think it's something like that brett yeah these are astonishing rates of change and uh it seems it seems like they are unstoppable except when you think about that wait a minute 10 times per year these models are growing what does that mean how are we going to get through all of this but maybe that's a topic for another day it's just the change is happening very rapidly well it's it's indicative of how much investment needs to go into the hardware to support these ai models right like if you if you imagine if you can create an ai model that replaces a lot of the sales function of a salesperson or makes that person's job that much easier makes them that much more productive then you would pay a lot of money for that if your sales force or or another software as a service provider because the in business will pay a lot of money for that and since the investment in the model that enables you to do that is a one-time cost it's basically you're investing in compute you're investing in collecting all the data you are training the model um and then you demonstrate kind of the capability and the productivity uplift you get you'd happily you know you think think about that as a an asset that you have invested in that actually will yield a very high return uh and so within the deck you can see our forecast that you know we think the cost to train an individual model could grow to a billion dollars or a hundred million dollars however right like there's also all of this innovation on the cost decline side so so it's hard to say exactly what each unit model will look like but i think it's clear that people are doing a lot more investment in the underlying compute capability to deliver these models and thinking strategically about business model setup to make sure they're scraping in proprietary data to then give them an advantageous position relative to um kind of the new capabilities being unveiled by deep learning we think that arm and risk five are going to displace intel in in the data center uh mostly because they can accomplish what we're talking about so much more easily and less expensively than intel so intel's market share in the data center which is the most profitable part of their business i believe uh we think we'll go from 92 share to 27 share by 2030. that's uh that's kind of a wake-up call for those index funds who have intel as the largest semiconductor position and there it's like i can a few factors that are kind of headwinds apple going direct to its own arm chips on its laptops will port over the developer base you can also argue that the within the data center the cpu which is where intel is dominant that becomes less critical to performance as more applications moving to a deep learning environment where they're using accelerators either fpgas or gpus by the likes of nvidia and so then because you're not performance differentiating on your cpu you're not willing to pay some extra normal premium to to kind of buy the best in class cpu and at the same time at least in custom chips developed by amazon and other cloud providers we anticipate in the future you can already get better bang for your buck on arm and likely on risk five in the future and so there's a lot of choppy water in the traditional cpu space to come in our view yeah and actually just highlighting that one point uh you know the the traditional benchmarks out there if what we're saying about disruptive innovation is correct and these s curves are feeding one another so that these trends are accelerating it probably means the amount of creative destruction out there is going to be pretty serious and and i think it will hit again the traditional world order which today is represented by the broad-based benchmarks so again it's really important to be on the right side of change when it comes to innovation i'd like to bring us into maybe a little more quickly we spent some deep learning because it is really critical to everything that's happening but from virtual worlds and digital wallets bitcoin electric vehicles autonomous automation uh drones orbital aerospace as you mentioned the dna sequencing uh ai is going to touch every one of these but maybe why don't you select the next one we we talk about brett well sure i think a topic that i return to again and again uh is is kind of autonomous taxis and people are skeptical that we are actually going to be able to deliver an autonomous taxi service a taxi where you don't have a driver in the driver's seat and the the pace of change in ai informs our view that that is more likely you have at least from my perspective you have to believe it's more likely at the you know this point in 2021 than you did 12 months ago because of so many basically critical thresholds neural nets have crossed in domains that you didn't necessarily expect them to be able to perform at the level that they're able to and what's interesting about to me the autonomous taxi space is if you can deliver autonomous taxis you should unlock a huge market opportunity measured in the trillions of dollars you know in in the deck we talk about more than a trillion dollars in operating profits accruing to the platform operators uh by 2030 uh and and so like just within equity markets if somebody's doing a trillion plus dollars in operating profits you should be willing to pay you know well north of 10 trillion dollars for that collective set of entities right and i'll just uh uh maybe provide a little more perspective there so the ride hailing uh companies globally today are are valued in the marketplace at roughly 200 billion dollars and what what brett is saying is this multi-trillion dollar opportunity has just started and the the ride-hailing players in the market today are not in the poll position to enjoy this ride so to speak uh it is the companies that have been collecting data data is the new oil i know that's trite but it really is in this case because it is the companies with the largest pools of data and the highest quality data with the the best ai expertise that are going to win this game and of course uh tesla is our largest position for a reason we've studied how much it has done to make autonomous happen and i think the miles of data collected are now north of 30 billion miles 30 billion miles just tesla and i believe google is 30 million miles now google is probably the finest ai company in the world i think that a lot of people agree with that but in terms of making this to executing on this challenge it seems like tesla has pulled together the right people with the right data with the right vision just because a company is expert in ai does not mean they're going to win the spoils it's really the applications yeah and and actually if you look across the platforms one of the i think interesting byproducts of the modeling that we do is ai is um the most equity market cap is captured by ai of our platforms in the equity space today you can easily imagine why right like a lot of we attribute a lot of the value of alphabet to its ai capability for example and that's a you know more than trillion dollar market cap company but if you look across our technologies kind of our expectation for rate of um market cap accrual or incremental market cap creation is actually much more rapid in the other technology platforms in some of these vertical applications where ai actually plays an important role but there are other um technologies that it's converging with to to kind of enable these applications and so autonomous taxis is a great example where it's not really you know there's a value attributed to cruz automation within gm there's whatever the value of tesla that's attributable to autonomous taxi expectations there's kind of about some value in attributed to baidu but it's not that much it's actually substantially less than is we think currently attributed to the electric vehicle space but we think that part of the value chain assuming you can deliver on a robo taxi becomes the most valuable position to be in yeah and uh tasha and sam have have done incredible work all of what we're talking about now are based on the research insights of our analysts guided by brett our our fearless director of research and sam has also done work this is new to big ideas this this year on automation generally and uh taking what we learned from how the manufacturing sector automated it took i believe was it 15 years no 25 years 25 years so from 1990 to 2015 for the manufacturing sector to go from uh 20 robots per 10 000 uh employees to i think that was 200 and our work now suggests that this is going to happen economy-wide around the world depending on the development of the country in just five years the same level of automation again ai plays importantly into this as as does battery technology both of them foundational to to what is going on yeah and and if you think about that like in some ways that sounds like oh my gosh that's really quickly you know manufacturing which is very well suited to robotization or mechanization took 25 years to take up kind of these systems these automation systems and how is the whole economy going to do the same thing well for one thing 250 robots per 10 000 employees is that could be a quite low number right like there's no reason why you wouldn't have an employee who is essentially using multiple robots or tools to produce the things that the employee is producing and what the advance in robots that's interesting to us is the combination of ai and more advanced and inexpensive sensors and even just like programming interfaces allows them to to work right alongside a human and you should also think about robots broadly in that getting a drone delivery that's a robot potentially the fry cook in a restaurant is a robot arm it will enable adaptive robots to to backwards integrate with current processes in in a much easier way which allows the uptake rate to be faster with with manufacturing you kind of have to build a giant cage and then you have to hire specialized engineers to to determine exactly how about that robot's going to move and it will move very quickly kind of the the next generation of robots is going to be something that you can oh it's not doing it quite right the person that's working on the line can help it to work more effectively and it's not going to accidentally knock that person's head off because it's going to have sensors to prevent it from doing so and so it allows it to be more completely integrated in processes that are already working for businesses today yeah and i know that as our listeners hear about this they say oh my goodness this is going to happen so quickly when we started the company the headline i think this was for from oxford university a study the headline of the study was 47 of all people in the united states it was a u.s study are going to lose their uh jobs to automation artificial intelligence in the next 20 to 25 years and they left it there hair on fire you know it was it caused a ruckus and we did the rest of that study actually brett leading our analysts there were 700 occupations that they believed are going to be mechanized and when we did that study the right answer and the full story was yes and because of this gdp in the united states in 2035 will be not 28 trillion but 40 trillion and our job is to find that where that 12 trillion dollars is new jobs completely new jobs we cannot even imagine today who could have imagined the gig economy back in the early 90s uber drivers and so forth the same thing's going to happen except much more rapidly but i'd love to get more into drones because here's a provocative way to understand what's going on and how quickly it might happen according to tasha's work the cost uh to for a drone to take a parcel over a 10 mile area will be roughly 25 cents today even if we introduced drones that were remotely piloted again introducing human beings into it the cost would be for the same thing would be seven dollars and eighty cents now that 25 cents is at scale whereas when we really have a sky full of drones uh delivering packages but you can see the the remote the human part of it is not going down in terms of wages or salaries but taking the human out of the equation and by the way i do believe we're going to end up in a labor shortage again so this will be a very useful thing for us the more repetitive jobs are going to succumb to mechanization and the more interesting jobs uh will go to human beings who will be helped by by robots right in in in that underwriting of 25 cents per partial delivery there's an expectation that there's going to be a human monitoring all of those drones that are operating autonomously they just won't be responsible for the single one just like with autonomous taxis there's still going to be a human in the system and and what autonomous taxis are really going to displace is all of the amateur drivers you and me we're amateur drivers and then in fact if we calculated the cost of driving to us personally over the course of our lives it would drive us nuts it's like you don't get paid for sitting in the car driving but it's clearly economic activity one of the things that innovation does is it takes non-market activity and it turns it into a market service and so autonomous taxi platforms are the big labor force they're going to displace is an unpaid labor force and i'm looking forward to watching you know netflix in the backseat of an autonomous taxi someday because driving is it's boring and it's laborious uh it's it's not like using your human creative potential to its maximum degree yeah i was i was going to say uh brett has two young children and i remember around the time his uh son was born he was uh thinking about we were thinking about autonomous very carefully and he basically said you know what i am so happy my son is never going to drive because we're going to look back and say that human driven cars were weapons of mass destruction you know 35 000 deaths a year in the u.s i think it's 1.25 million globally will save a lot of lives so a win-win there right yeah i think the age of 16 was probably the most dangerous year of my life just because suddenly i was given you know 3 000 pound missile to to pilot around it it seems crazy in retrospect yes it does i guess another new area that we've introduced this year is orbital aerospace and what strikes me about orbital aerospace is talk about convergences almost everything we've worked on except for maybe genomics and and we'll end on on that is involved in orbital airspace so deep learning mobile connectivity 3d printing robotics i mean you know the gamut uh so and again we're we're looking it's the same story over and over again why is this happening now this has been a dream in the eye for years right and the reason is costs have collapsed and the technologies are ready yeah i mean i i think that the collapse and the price to loft something into the orbit into the sub two thousand dollars per kilogram range is notable even more notable is is as you are doing reusable rockets we think you're gonna get to uh on the order of a hundred dollars a kilogram so another 20 x decline versus you know in 2016 you're at 14 000 a kilogram and so then the question becomes what are you going to do with all this capability it's one thing it's very clear you're going to throw a lot of satellites up into space and so if you've you know ever been agonizingly duped into paying for uh satellite internet while on an airplane that experience should get better once you once you have the satellites a lot closer to connect to and even like i know at my house sometimes my internet cuts out you can imagine that some people like have a backup satellite internet that actually works and is quick so that in the case of kathy her video just cut out because she doesn't have satellite internet and and that's going to be possible because of the the declining cost for orbital aerospace yeah and i i think people are surprised to hear us talk about 3d printing in the context of orbital aerospace but it's applicable widely to aerospace right now the killer app for 3d printing is aerospace you've got boeing and airbus and others in such trouble and during times of trouble when businesses fear for their survival they're willing to change the way they're doing things and so the the costs associated with printing some of the parts on airplanes even within engine engines the the costs are 75 to 95 percent to 90 percent cheaper and form factors are smaller weight is lighter and so forth so 3d printing we believe is starting to emerge from uh it's been a very long valley of despair valley of despair usually happens after there's been a hype cycle as there was with 3d printing we were all going to have a 3d printer on on our desks what i don't know for maybe jewelry or parts whatever that was not going to happen the killer apps are right today are medical so hearing aids and aerospace and space itself maybe you want to talk a little bit about that sure i mean any performance critical application where you have like a low volume part that's where it makes sense we have an example in the deck where i believe it's airbus uh reduce the part count for a single um it's called a satellite bus from 125 parts to one part which is stiffer and lighter and so you can imagine the kind of manufacturing efficiencies you even get out of that if i can just print one i don't have to do deal with error tolerance between all the parts i'm putting together uh and uh and our belief is that particularly as the number of aerospace both orbital and suborbital so think of drones and parcel drones as the number uh enabled by energy storage by lithium-ion batteries as the number of form factors proliferate you're actually going to see more in-use parts appear in these devices that are 3d printed and that's where the market gets really interesting and large right now 3d printing is mostly in prototyping and kind of the tooling that you use to manufacture things at volume well if you have all kinds of custom aircraft basically uh that are all very weight sensitive then you're going to end up with a lot more 3d printed parts in those aircraft and so and and our view is you basically increase the size of the addressable market by more than an order of magnitude as you get into that in use part space yeah and i guess uh maybe we'll end on genomics and you'll notice if you read our big ideas 2021 that genomics is indeed at the back of the bus and our genomics analysts were wondering well why is that well there was a certain progression that marketing thought was um important starting with deep learning but ending with genomics probably one of the most provocative applications as as brett mentioned a little earlier is of ai is to the genomic space and there are breakthroughs for example long read sequencing that are taking place again it's all about cost coming down to a low enough level and the technology being ready well long read sequencing is getting ready for prime time it's more accurate more reliable than short read sequencing which has been the traditional sequencing we believe that the costs are coming down fast enough now i believe this is a 28 for every cumulative doubling in the number of human genomes uh sequences read with long read that we believe the next during the next five years the compound annual rate of growth in the long-range sequencing space is going to be 82 percent per year uh so think about that compa think about that 82 year after year i mean that that that space is going to get very big in a very short period of time much like electric vehicles which are also ironically not ironically coincidentally going to we believe show an 82 compound annual rate of growth over the next five years so so we're we're moving into very big numbers in a in a much shorter period of time than has happened historically and i and before we we end or brett ends on this topic i think that the reason many investors are having trouble visualizing how quickly things are going to happen here how how quickly the changes are going to take place is because we've never been in this uh kind of period before you have to go back to the early 1900s to three to see three platforms evolving at the same time technologically enabled platforms telephone electricity and automobile those three went into exponential growth trajectories at the same time today five platforms 14 underlying uh technologies or supporting technologies converging converging you know there are going to be explosive changes and we're probably as excited we love all of our children but we're you know the results we're going to see coming out of the genomic revolution we're going to see cures to disease we're already seeing it but maybe brett why don't you take it from there yeah i think one of the things you mentioned with these cost declines what's interesting is like some cost price points matter more than others in terms of the demand that you unveil right and and so it's actually the cost of sequence of genome has been declining you know dramatically since the first human genome project in 2003 and and the costa long read sequence of genome where you can understand more of the structural variants so not just is a letter wrong in the genome but is is a part of it inverted are there a number of repeats in a row and those structural variants are actually really important in some critical situations like cancer being able to get that information it can simply be too expensive for a while for anybody to do it in a scalable way and then you hit a critical cost threshold and suddenly it makes sense within the economic decision-making processes of all of the purchasers of that technology and so the the same is true we believe with um liquid biopsy the idea that you'll be able to take a blood test and detect whether or not you have early stage cancer well in 2015 with our understanding of the basically the genome and and the information that was being sloughed off into the blood by cancers it would have cost roughly thirty thousand dollars to try to detect cancer from a blood draw and we believe that's gonna be i think it's 250 dollars by by 2025 right and so between 30 000 and 250 30 000 to 10 000 actually doesn't matter because it's still way too expensive for it to make sense for an individual patient to be tested on that basis because you're going to catch so few cancers that you the economics don't work but as you cross the thousand dollar price threshold suddenly the whole market gets unlocked and it's it's a market that we believe is going to be worth more than 100 billion dollars a year in spend because you're going to be able to dramatically reduce cancer mortality so then to electric vehicles the same is true it an electric vehicle that costs 200 000 doesn't make sense and never would have made sense to drive volume scaling of the pricing but once tesla delivered the model s price point and got people to buy that drove battery production and and started that flywheel moving such that in our view you're going to cross all of the price segment price points in in the whole automotive space where by 2025 an electric vehicle will be cheaper substantially cheaper than like a toyota camry the median car sold in the u.s a 350 mile electric vehicle will be more performative cost you less money over time and cost you less money out of pocket and so that's an important price threshold to cross and the whole market will invert on that basis and on that price point uh already it's that the total cost of ownership of an electric vehicle is lower than that for a like for like gas powered vehicle that's already true we're talking about the sticker price now so that by i think in five years we believe that a toyota camry will still cost somewhere in the 25 to 26 000 range by that time a like for like electric vehicle will be down to eighteen thousand dollars no-brainer no-brainer it would be a huge surprise if people decide to stick with old technology so if you stack all of these opportunities up and this goes back to your original point kathy if you look at all the 14 technologies and the modeling we've done on those 14 technologies and the attributable market cap right now we think there's roughly 14 trillion dollars in attributable public equity market cap to those technologies by 2030 we think there's going to be on the order of 75 trillion dollars in attributable market cap uh that's roughly the size of the total global equity market today so so that gives you a sense of like how much value creation we think is going to happen in the technology platforms that we're focused on and by the way we could be wrong and it's not going to be a straight line from here to there but that's how much value creation we think there's going to be and it could result in a lot of destruction to the rest of the kind of public equity space along the way yes and uh we would be remiss if we did not talk about one more topic because today actually and i realized this will be a bit dated when anyone's hearing it but uh the market cap or the equivalent of market cap for bitcoin uh just crossed one trillion dollars and uh yasino mandra has written a number of white papers more on their way and you know he points out that every use case case is just additive to bitcoin but just think about this one trillion dollars is you know half of what apple's worth and yet this is a global digital monetary ecosystem uh and bitcoin is the reserve currency of that ecosystem we believe uh that is a very big idea and so you'll see two sections in big ideas about bitcoin as well so with that uh brett do you want to close this out sure thanks kathy i as always enjoyed the conversation i'll probably talk to you in another half hour or so about these exact same topics this is like a taste of kathy in my life like research meetings basically this over and over and over but every one of them is fun we're trying to we're trying to figure out the way the world's going to work putting the pieces of the puzzle puzzles together especially especially with all of these convergences going on creating 3d puzzles so it's a it's actually quite fun i agree 100 okay well thank you so much for joining us kathy and thank you listeners for joining us uh to fyi the four-year innovation podcast we will talk to you soon and thank you brett for guiding us through this exciting time of research 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 arcs current views and assumptions and involve known unknown risks and uncertainties that could cause actual results performance or events to differ materially from those expressed or implied in such statements

Channel: ARK Invest

Views: 235,265

Rating: 4.930222 out of 5

Keywords: ARK Invest, Cathie Wood, Cathy Wood, Cathy Woods, Cathie Woods, Kathy Wood, Kathie Wood, Kathie Woods, Kathy Woods, ARK, investment, investing, innovation, tech, technology, research, economics, macroeconomics, stock picks

Id: qKAVn58q5bA

Length: 38min 10sec (2290 seconds)

Published: Mon Apr 05 2021

Reddit Comments

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Data is the weightless gold of the future.

[u/XpensivPasta]

Love it, cant wait for ivy to get rolling

[u/zibdabo]

I think Cathie Wood will look at BB when we get more details on how IVY will be monetized.

[u/I_Am_The_Turkey]

Only a matter of time before ark takes a BB position

[u/No-Satisfaction1395]

Guys, remember BB gives the data to the OEMs. The OEMs will subscribe to IVY and in return they will own the data.