Transcript: Deep Tech's Golden Age: Investing in Robotics, AI, and Quantum Computing
Full transcript: Deep Tech's Golden Age: Investing in Robotics, AI, and Quantum Computing.
2025-03-16
Host: I I want to talk about one of your uh portfolio companies which is PsyQuantum and recently you know Quantum had some breakthroughs from Google, but I think you invested in you know way early before anything about Quantum was in the narrative.
Guest: You're doing everybody was doing software right? like there was nothing else right?
Like you software was eating the world, you know we had the clean tech uh uh fast 2010 2011 so nobody wanted to touch anything that's like remotely hard technology part of it.
Most of the big VC firms were like basically retiring out their hard tech or their hardware VCs.
So it was very clear that if you talk to entrepreneurs in like core technology, new technology, new invention, they have like very few VCs to go talk to even in Silicon Valley right?
So they felt that there's a missing gap and then you know that was that was my DNA in terms of you know evaluating new technologies. That was what like interested me in technology to start off with.
So there was a we felt there's a gap when there in India the navigation is by more around you will find uh this particular place turn left on that. It's never about okay turn left on the street.
Turn left on this this point of interest and that point of interest again will have six different names. And uh you know so so you know it's a much much harder data problem that data is going to be very very valuable. So we invested in them like 2009.
So you know they went public three years ago now. Even today there's nobody who can come match up with the quality of data that they have built.
Host: Welcome to Startup Project. Uh my guest today is Karthi. He was previously managing director and vice president at Qualcomm ventures. Uh he led investments in companies like Waze, validity, MapmyIndia.
He currently runs MFA partners where he invests in deep tech companies across AI, robotics and quantum computing.
In this conversation we'll talk about uh how he became a full-time investor, his career in investing uh and investing experiences in deep tech and corporate venture capital. Uh what what does it take to become a great venture capital investor.
Um and what does it take to start and run your own VC fund and a lot more.
If this is the first time you're listening to Startup Project, don't forget to subscribe to us uh wherever you listen to the podcast uh because it helps us uh reach more audience.
Host: Karthi, welcome to the show.
Guest: Yeah thanks, glad to be here.
Host: Uh so I guess a good place to start is um you know how and how did you come into venture investing as a career.
Guest: I don't think it was a plan at all. In fact some of the people that knew me from very young age if they hadn't met me for 20 years they would have been surprised that I'm an investor or even a business person.
I think like when I was young I was going to do a PHD and then go develop new technologies.
That's what the plan was and that's what I pursued for the first maybe 10 years of my career, building new things semiconductor stuff wireless communication and new stuff.
I don't know I think is the circumstances where I kept getting pulled up and we're going in front of customers, uh leading people. So I basically learned that maybe maybe more than my technical capability to build things, maybe I have other skills.
So uh which is what I led to me to do a MBA and I had a I had a very clear thought I want to come back to technology I was not going to do anything else but uh maybe more as a business person.
I spent a summer doing venture capital until I was a startup guy, just really startups job and then suddenly I'm evaluating startups that was you know a lot more interesting where I can look at several various startups.
Um even then I hesitantly just jumped in saying that okay maybe I'll try this for a couple of years and I don't think there is there was any planned stuff to get into venture capital and it's also you've got to rewind it back would I just go do I don't know maybe I just done the product management and go go be an operator.
So uh I I don't think it's just a very you know uh defined uh path wherein I think all of that it's uh you know I still go back and forth thinking about okay if I had to rewind it would I have taken that.
I had two two job offers in product management in wireless communication at the time. Um anyway.
Host: How did that happen?
Guest: Actually wandering should I do this or not.
Host: So uh is the what is your first job in venture?
Guest: In venture. So the first job in venture I spent time at J&B capital in Chicago, that's where I was doing my business school. And um, in venture unlike other areas you don't really basically go fight for your job.
It's not like they are looking for some smart people.
So I basically reached out to them saying that hey look I I've been a lot of stuff in semiconductor stuff and wireless communication and it seems like you guys are starting to look at that and I can add all these things and then I basically suddenly all the stuff that I can look at reconfigurable stuff uh new architectures and what not.
So they like that's okay all right there's an area that they are spending time on that's the scenario they didn't have any expertise on and I had a deep expertise. So they felt that okay you could come and do that.
So so I I joined basically to just look at all this stuff new electronic stuff new microelectronics new semiconductor stuff. So so I was basically doing you know sector analysis reviewing companies in those areas, right?
So I think the the one key thing in venture capital is that compared to others like is that you have to basically define a job opportunity job description telling them that you need this and this is what I can come and help you out.
Even as an associate, even as an entry level person.
Whereas in every other job you say that okay all right I'm a smart guy, I can do software, okay where do you want software engineer, where do you need hardware engineer, do you need like an analyst where in venture it's not that right?
You you got to basically fill a hole or you got to basically create an opportunity and then say I can go uh fill that opportunity.
Host: One quick second um I think there's a bit of a lag. Are you experiencing any lag?
Guest: Yeah you went on uh we're like buffered are freezing for a while.
Host: If if you have lots of tabs open up like close Chrome tabs that takes a lot of memory.
Guest: I don't have a lot but let me close all.
Host: I was that do you want now?
Guest: This is the only.
Host: If it buffers you can just wait a couple of seconds it at the words will flow in. So that's what I do on my side.
Guest: Yeah.
Host: Um, All right so once you uh the join the firm yeah you start understanding you know venture capital what were some of the early deals that you worked on.
Guest: So one of the very early ones was like using wireless communication for tracking stuff. This was like in 2005, this is like you know 20 years ago now.
Um you know they were creating a proprietary you know wireless communication stack for tracking objects are tracking like assets going around across the country and stuff like that.
Uh you have to think about this is the day this was uh early days of 3G and then we are 5G means early days of 3Gs. Most of the data communication was like pretty low key.
And uh and anyway so uh think of it like what the tags that Apple tags but it's kind of a early incarnation of that but more for like big assets and uh it was interesting because I had wireless background and I had some of the microelectronics background as well.
So we didn't end up investing but uh it was it was interesting to understand you know how you know there are problem statements that need to be solved using technology and uh the the key learning there was that you know you always try to solve a business problem and then you solve it through underlying technology but you have to be aware of how underlying technology is getting commoditized so fast.
Uh so so one of the early ones at a Qualcomm I evaluated was around navigation app and I think this is like 20 years ago people don't realize that you know this is before Google Maps, you paid $10 a month to arise an app for you'll give you a phone based turn by turn navigation stuff.
Right so um it was a business need because you know you're driving and uh you know you you you don't want to buy a separate uh navigation device so you want to use your phone uh so for turn returns so $10 a month because there's a good business opportunity so because it's actually some of the companies are doing good revenues but the underlying technology was getting commoditized.
People are basically MapQuest was saying oh I can do it for $4 a month and then Google said you know what it's free. So suddenly these companies business model just went away in one day.
So so uh so it's good to understand both the you know business problem but also what the underlying technologies and that's the thing about deep tech uh our new technologies now we defined it as deep tech but new technologies they get commoditized very quickly as well.
Whatever I built 25 years ago as a startup today it's like a five cents chip right? Nobody cares it's like it's still a commodity right?
So so understanding the technology curve technology progress is extremely important to figure out you know which is going to last longer and which is going to just go up and flames out.
Host: Is there any pattern that you've identified in terms of figuring out if some new technology is getting commoditized then should we invest or not? Like how how do you make that decision?
Guest: It's uh it's a lot of lot of um I'll say a lot of uh heuristics right? Like you know you kind of try to see you know what is the path of commoditization here and then who could actually retain still value.
So one of the things like in this whole stack when we looked at this turn return navigation at the time is that yes the turn return navigation part of it was $10 it went to zero when Google came up but we felt the underlying data the actual map data, the actual data of where all the points of interest are and stuff like that.
That is not easy for anybody to just go building right?
Like you know so at that time nav check and chili Atlas were spending the hundreds of millions of dollars, maybe not quite that but maybe 50 to 100 million dollars every year to just make sure they have the updated stuff right?
So that I didn't ever feel that hey that is just going to get commoditized just based on technology to all the way to zero. That's still going to have value so that's what led to actually two investments.
One in US called ways Israel in US called ways and the other one is in India called MapmyIndia. So uh so in some sense you've got to figure out what could get commoditized and what could not.
So for example what Nvidia's GPU could get commoditized down to nothing I think there are a lot more barriers there right?
First of all to get to that uh that level of performance but more importantly they have built the stack on top which is the middleware the software application layer so they have basically created a mode around everybody using the CUDA everybody using their software middleware to build their application.
So it's so they'll be able to preserve quite a bit of that right? So so you look at all all possible stuff you know do they have any other protection here are they just in the whim of the technology curve.
If it's just that they're probably going to get commoditized but there are cases where there are physical needs there are things that have a regulatory stuff and things like that where there's going to be enough of uh resistance to completely get commoditized and then you kind of look look through a combination of that.
So yeah.
Host: So what was the lens behind investing?
Host: India what was the thesis there?
Guest: Can you repeat that? I think we you broke for a little bit. Uh the lens behind?
Host: Yeah uh what was the lens behind investing in MapmyIndia?
Guest: So um like I said we we looked at this whole stack of you know navigation um turn return navigation or uh GPS or location based services in broader terms and we felt that the biggest value there is in the underlying data y the points of interest data.
That's the one that's not easy to get it and then that's the one that requires a lot more time. And um Waze was doing in a different way, they are basically doing in uh you know uh sourced way interesting tricks.
MapmyIndia is more more uh uh traditional way but they're going after the market which is very very very unstructured right?
Like whereas we are in US it's very structured most of the western markets we went through this in the you know 1900s you know mid 1900s to to standardize the addresses to standardize things on a grid and stuff like that. even Europe Western Europe even though they are older civilizations but you on a lot of effort was been made to standardize that so there was it was easier to maybe go collect data in some sense but India still is like a completely uh you know unstructured and then more importantly uh there was no one standard norm.
You know people you know just your you know just to give you a quick context when a parcel of land is you know assigned or divided there is a plot number or parcel number and then then there is the next step which is okay I'm now going to do something which is like um so there there is a numbering and then maybe a a road and then finally when everything is built up there is an official street name assigned and stuff like that.
So you have like three at least three different versions of things but most of the time you'll find five or six versions of it and then different people use different different ones.
So there's in the same street, same place you'll have one which is based on the parcel number or land number and then something the early uh street number and then finally something which is the you know supposedly the standard one.
And there is no you know horizontal vertical lines and what not. So which basically means everything like from not just the uh mapping this data but also how do you route this data is like very very unstructured.
Um and then people who have done anything in Asia especially in India the navigation is by more around you will find this particular place turn left on that.
It's never about okay turn left on the street to turn left on this this point of interest and that point of interest again will have six different names. And um uh you know so you know it's a much much harder data problem.
Uh so which is why we felt that uh you know that data is going to be very very valuable. So we invested in them like 2009. So you know they went public three years ago now.
Even today there's nobody who can come match up with the quality of data that they have built.
So that was the thesis that with the quality of data to for you to get in a country like India unstructured data like Indian addresses it's going to be very hard and it has grown up.
Host: And even I think couple of years back Google Google was still trying to like introduce a standard format of you know creating addresses in India which is a which I think is a whole another talk about the differences in incentives like when you work at a fund at an early stage you know in some firms you get some carry some equity in the fund versus your salary um and in you know corporate venture firm there's again the component of um you know your W2 salary plus some equity component talk to me about the nuances of those incentives.
I think that's a little bit of undertalked um part of this industry is like especially for people who want to think about coming into working in venture who are at early stages of career uh what should they maximize for here.
Guest: Early on there's not as much difference actually.
Um because if you think about it early on you're learning to get your own network of entrepreneurs where you're trying to connect with them build a relationship with them and then figure out which are good investable startups, you know curating that, figuring out your in your own way, you know what is what is a good startup to invest in what is not and what's something that maybe a good startup but you need to probably wait a little bit for them to figure out.
And then making sure when a round does happen you get allocation to it. Those are all the first you know when you're we start as an venture capital those are the first things that you focus on right?
Frankly there's not as much difference between a corporate venture versus a financial venture because you know the early years you're learning the craft you're learning to learning to learning to figure out how to get into these companies and then figuring out uh do you have your own expertise in terms of what you like, where you have the edge and then what.
Right? Um some of the bigger financial VC firms will have a brand in terms of you know attracting uh inward.
Corporate also have that right? you know corporates will have you know Qualcomm so if you are anything to do with semiconductors or wireless communication or communication in general Qualcomm is well known name.
So you know so if you have somebody from Qualcomm validate your technology and already a company or potential partner is very interesting as well from the startup. So there is interest from that.
So so uh so so early on it's actually there's not as much difference what I have seen is that at Qualcomm Ventures especially um maybe start true for all corporate venture firms but at Qualcomm ventures we were given reasonable autonomy to actually go chase investments and then if you do end up making investments we be on the board or be responsible for the of the investments.
Host: But do you also get the compensation along with your W2?
Guest: So on the compensation side again early on it doesn't matter us so much right? because if you're thinking about it you get some carrot interest plus bonuses plus uh plus uh salary in in a financial VC firm.
You know court perform some of them are now instituting some carry but let's say even if we didn't have carry you probably have bonuses stocks from the corporate and and and and uh you know salary.
You are still okay, you're still the same almost the same. There's not as much difference right?
Where the difference coming is in the later years right? where okay now you made these investments one of them like for example you know was a good outcome for for us for for me for for the for for the for Qualcomm.
I don't think you know you if you have been similar in a financial VC firm the compensation you have got would have been different right?
So so you start to matter once you go like five six years in because an investment takes six seven years for it to get exists and things like that right? So once you go six seven years in then that's when you start to see.
So there's a the corporate VC you want to think about it is that you live within a band.
Your downside is protected right? whereas in a financial VC maybe if you're not as good you probably get cut very fast whereas maybe in a corporate you have a little because there's a strategic value and all those things.
So in a corporate your downside is maybe a little more limited but your upside is kept right? you are not you know you you're never going to have this unlimited upside on a large upside right?
So you live within a band which is okay in early part of your career but later part of the career it's not right?
So so if you think about corporate we see as a as a career early in the thing a good corporate we see probably offers you almost the same amount of you know incentives opportunity to learn and things like that.
But once you have five six years in that's when you start to figure man I'm not I'm a good rec I'm not seeing the same upside right? so that's when you feel we start to right? so anyway.
So so so to summarize that would be the thing like you know few years in the starts to make a difference especially if if you believe you're a very good VC making very good investments.
Host: But so you're saying the even though like even though you make good bets early on in the farm, your upside is also capped because of the way the compensation is structured.
Guest: You could get promoted you could get like a bonus uh you get a so there might be a salary around that. But you're still going to be along with the corporate arms of other people that are working in the company.
So you cannot completely you know so you you get a 100 x or 30 x or 50 x outcome doesn't mean that you suddenly make a couple of million dollars they still have to be within the corporate role right?
Host: Yeah.
Host: So you made couple of interesting you know bets or was part of it like Waze and MapmyIndia um and then you decided this wasn't enough compensation and started your own firm. So what was that journey like and how challenging was it to raise your first fund?
Guest: Yeah it was not the compensation alone it just felt that you know you you're you're I'm becoming a corporate BP and then you you probably kind of feel that okay this is probably where you you're you're hitting the roof here like in terms of where you go from there, both in terms of learning in terms of also in terms of a I make I'm making good bets then I should be compensated accordingly right?
Um I think the the transition to starting a firm um probably the hardest uh uh experience of my career that I had to go through.
I've been an entrepreneur before that and things like that and you know but this one um uh raising money from LPs is very different from raising money as a startup.
You know I think we we went through quite a bit of thoughts around why am I doing this?
I think the main reason we felt I felt was that look uh this was in 2017 18 I was thinking to 2016 17 18 when I decided leave software is ruling the roost right? like you you you're doing everybody was doing software right?
Like there was nothing else right? Like you software was eating the world you know uh we had the clean tech uh uh vast 20 2010 2011. So nobody wanted to touch anything that's like remotely hard technology part of it.
Most of the big VC firms are like basically retiring out their hard tech or their hardware VCs.
So we it was very clear that if you talk to entrepreneurs in like core technology, new technology new invention they have like a very few VCs to go talk to even in Silicon Valley right?
So we felt that there's like a missing gap then you know that was that was my DNA in terms of you know evaluating new technologies that was what like interested me in technology to start up.
So there was we felt there's a gap and that we could go fill it um and then we felt that there's a new like you know we used to be selling technology to technology companies that's what it used to be mostly uh other industries other verticals typically adopt a technology very slowly very late.
But that was changing. We invested in a Qualcomm company called Cruise which was doing autonomous vehicle and then GM bought for a billion dollars very very quickly almost like a technology company buying a early early startup.
Um so we could start to see that this verticals now worrying that they're going to get disrupted. So so that that was the reason to start this firm.
We felt that first of all deep tech companies don't get enough attention and then there is new opportunities of verticals uh adopting new technologies as well. So there's a big opportunity and a gap.
So that's what that's what that was the thesis to start this thing.
But the fundraising and then building the firm you know suddenly you go from you know okay I'm part of Qualcomm even though Qualcomm didn't have as big a name in Silicon Valley's circles when we started but still it's still a big company um to going and basically building the the entire stack.
Um and the but the biggest thing was fundraising. That was the biggest thing because as a corporate visa I had to go fundraise you know we invest using our balance sheet.
Um so there's a lot of learning there and then when it comes to investing as an LP uh it's really a trust based thing right? Like people people are going to invest based on trust.
Which basically means you could raise money really from people that know you or at least know of you. You couldn't go beyond those circles right? or or it takes much harder to go beyond the circles.
So some people may have a big big circle of this thing who who you know who have a lot of capital and they know you are know of you but sometimes they don't.
So if you're coming from a financial VC sometime sometimes you have enough of his relationship with the established allocations of capital so it may be easier to raise capital whereas coming from a corporate we had I had to go basically learn that from scratch right?
So learn that from scratch go build a relationship, go build that um you know network of folks that trust us and what not.
So it was much slower harder So but you know you know it's not a it's not a short-term thing number one number two you don't do it for other things like oh I want to do it because that's what everybody does.
I want to do it because it's just a status symbol if you do any of those reasons you probably want to quit at some point. Uh I think we I we we felt that we thought this through. We felt that there is a need to do this for a very long term.
So we're able to go through all the ups and downs and then you know now we are you know investing out of a fund too.
I don't think we have still arrived yet in terms of a fundraising is like uh is like cruise control thing but it's it's been much better than how it was when we started.
Host: So um are there any sort of one or two quick lessons that you can share or learnings that you'd change on how you did your fund one first raising like based on now that you've done you know two rounds of this.
Guest: Uh You know there's a statement that you know everybody has a plan until you get knocked in the face right?
So so I don't know whether there is any plans that I could have done you know in fact we are all like very rational people like I'm an electrical engineer so more analysis and stuff like that so I don't think I would have done any more analysis and stuff like that right?
I think the only thing I would ever said is that um you know uh there are so many people that we I felt could could have invested in the fund didn't end up investing.
So separating people from their capital even as an investment right? is the hardest thing to do right? So so which basically means that yeah the you know we start off thinking that we're going to raise a bigger fund.
Uh maybe we should have started off thinking that we're going to raise a very small fund right?
So maybe that's the only thing that okay uh assume that everybody is uh who who who you potentially think could invest is not going to invest and then maybe start up that way.
But I don't think the end goal end game would have been any different would probably end up being a similar sized fund and what not. Maybe you would have had less disappointments less uh you know heart aches but uh uh anyway.
Host: Let's talk a little bit about you know deep tech itself.
I mean now the term became pretty you know common I think that's to do with you know some of these companies like Andruil and uh I think A16C sort of pioneering something called American dynamism.
Um I think there's a lot of like a narrative um I think maybe SpaceX and Nvidia sort of also you know are you know hard deep tech companies and it in in my view VC has always been for like really hard problems like you know when first Intel or you know Intel was coming upon a scene when you know government was actually you know Intel Q uh you know that fund was investing in these uh ideas which your original internet and silicon uh chips it was really to solve really hard unknown risky problems.
And then I think you know we sort of went hey where in between and then we started finding everything and call it everything as we see now.
Um so you I think probably found the right time where as you said was everyone was focused on software and in a way sort of like an easier problem and as if you've solved all the hard problems and only these problems are left.
I think that sort of happened in I would say in 2027 to 2020 where people were not looking at hard problems really closely at least the narrative was not there will always firms that were focused on such stuff clearly.
Um so right now what do you think um where the deep tech is um and which areas you're interested in trying to find companies in.
Guest: Yeah so you're right VC is used to fund hard problems right?
Like not necessarily R&D science research stuff that that labs used to do but like when when something is proven out and ready for hey now this could be now go built but it's still hard you know it's not like obvious that you know you don't have customers you don't have obvious stuff right?
That's what it used to be and then uh internet first started this now you have lot of internet companies right? then mobile and then the the whole concept of elastic compute virtualization started the cloud compute as well.
So you put all those three things come together you have internet, you have mobile, you have cloud compute it it all coincided with the with with two things as well. So one of the reasons why this happened right?
I usually think why this is happening because this concept of deep tech stuff right? or deep tech or core infrastructure core infrastructure it's been there for the last 50 60 years of our technology evolution right? you come with the microprocessors oh and now which which which initially we started this you know personal computing era now you had a bunch of applications there layers stuff right?
Then you started the basics of internet infrastructure internet technologies oh you start all this right? So so you this is this is how this is how uh that the technologies work.
You you build the hardest stuff then you can build applications and uh you know utilities on top of it and then in the next generation of stuff happens and things like that right?
So in that cycle nothing has changed. you know we went through these things now the next one was robotics automation AI so it has been moving in those things right?
So uh but think what happened was um you know both the internet mobile and cloud compute all of them now provided this era of cloud applications internet applications web mobile applications and then it coincided with the time of like a hard landing of clean techs where people were doing all sorts of crazy things just crashed landed and then the uh you know the the evolution or the emergence of China as a semiconductor hub.
You can say that the silicon valley used to build bunch of semiconductor stuff right? Like you know it's a video processes it was a it was a it was an application chip for this and things like that.
Those type of slightly easier to do maybe not the hardest one type of a thing just moved to China. just China could basically do all those things. You couldn't do like a I'm doing this microcontroller video application stuff.
This is not that deep an IP but you still need to go build this that used to be one of the biggest areas of venture investing venture for funding went away because China just took it. China and Taiwan just took it.
You could just build those things. you can never compete with that. The only place you could compete was that you have a fundamentally new technology where you are right?
So you combine all of them boom like suddenly the the the the people are investing in semiconductors and hard technologies finding that there's not enough opportunities or the the opportunity threshold is much much higher now and then this emergence of this mobile compute mobile apps internet apps, cloud apps.
And then so so why would you do that? So you did that for 10 years institutional knowledge is completely gone.
So initially start up was a big area coming up and then all the you know the hard tech folks just kind of retired all the new VCs were hired into these firms as associates and principals, they were all doing software. 10 years later now I I've seen some of these panels or dinners and then they asked me how come in deep tech like you know you you you get this revenues of 1 million one year and then in the next year it's 30 million how do you account for that?
So it's like yeah you get one design and yeah you can just say you can do 30 million units and then yeah that's where it goes.
It's not like software where you're going from you know even 5X growth right? whereas like suddenly you get one design win you go 30 years or even 100x is like it's possible.
Um so the institutional knowledge is completely forgotten amongst the newer younger circles as well right? the associates and principals as well. So we need to start building that right?
So which what's happened in the last five six years like now more firms coming up and what not. But anyway so so that's the that's the history of what happened and what's the missing gap and what not.
But I'm very bullish because like I said the technology used to buy technology now verticals are buying technology other verticals are getting disrupted.
So we are in a kind of a golden age of score deep tech hard tech because you know we're seeing things around robotics and automation, we're seeing things around synthetic biology affecting a lot of the industrial applications.
We're seeing the new generation of computing in quantum computing, we're seeing the core AI stuff that's solving a lot of the classification generation problems that is now possible.
So we are kind of in the golden age of new technologies solving problems now and I'm sure once some of these infrastructure gets built you're going to see a variety of application leader companies but uh anyway so I think I think we'll just keep doing this cycle of new technology which provides the infrastructure and then application companies that come and take use of the infrastructure and then build the interesting applications for consumer.
Host: I I want to talk about one of your uh portfolio companies which is PsyQuantum and recently you know Quantum had some breakthroughs from Google, but I think you invested in you know way early before anything about Quantum was in the narrative.
Uh talk a little bit about what was the impact on uh PsyQuantum, what is the company really doing because I think they'll say uh practical applications that they're trying to do.
I think that's sort of like undercovered right now is like there's a lot of narrative around Quantum but no one really knows what the outcome or the use cases that are going to come about. So if you can shine light on that that would be good.
Guest: Broadly like we are basically hitting the limits of computing because we already gone to like 1 nanometer 2 nanometer type of a semiconductor chips there's no more things to really act right?
So and then all computing needs are not going away. We are we need more computing and things like that right? Um so we definitely need to figure out different form of computing architecture which doesn't have the same stuff.
So Quantum provides the best alternative to this one right? So even even though it may seem like hey you know are you going to put a quantum computing chip in your desktop? Maybe not right?
But you know so so if you look through only that lens yeah you might seem like oh it's not there there's no practical application but that's not really true.
Today if you really want to push the edge of computing beyond what we can do through using our current standard technologies is the best option for us right now is Quantum right?
But the basics of Quantum is that you know it draws insights from quantum mechanics where anything can be in two states at one time right? Multiple states at one time.
Um so but if you go back to any of our computing current computing digital computing right? It goes through the same thing.
It's either a zero or a one right? like we might do it in different forms but it it goes back to the same thing it's zero or one. With Quantum mechanics now we can be zero under one. You can same bit can take multiple states right?
That suddenly it has exponential qualities to it especially if you put bunch of them together right? Like if you think about you know eight bits now suddenly it can stay in two to the power eight states at one time right?
So and then if you just if you're able to then provide use that ability to actually store that kind of data or use that kind of exponential thing and then you can actually do some arithmetic on top of that now you can solve some complex exponential problems which are taking much much harder to solve us using conventional computing.
That's what quantum computing is do right? It's not for control logic right? yeah if you want to do if then else stuff yeah your current digital chip can do that right?
It's not for even some of the very regular computing yeah our matrix multiplication yeah maybe some of our current computing will do but there are a bunch of problems that are exponential in nature which which even today the best computers cannot solve over thousand years right?
So that a quantum computer could solve very quickly right? So the first applications are all things that require these things.
So you think about drug discovery the reason we have to do actual trials is that we cannot simulate the whole interactions of in your body even