The Human Code
The Human Code" podcast unravels the intricate blend of technology, leadership, and personal growth, featuring insights from visionary leaders and innovators shaping the future. Host Don Finley dives deep into the human stories behind technological advancements, inspiring listeners at the crossroads of humanity and tech.
The Human Code
Future-Proofing with Sachin Keswani: AI, Quantum Computing, and Human Ingenuity
AI Revolution and Human Creativity: Insights from Sachin Keswani
In this episode of The Human Code, host Don Finley converses with Sachin Keswani, a technical program manager and software performance optimization engineer, about the rapid evolution of AI and its societal impacts. They discuss the importance of balancing human and machine collaboration, how AI can release human creative potential, and the promising future of quantum computing. They also explore the challenges in AI adoption, ethical considerations, and the potential socio-economic divides that emerging technologies might create. Sachin offers valuable advice on adapting to the AI-driven world, emphasizing the importance of mastering mathematics, communication skills, and leveraging technology for creative expression.
00:00 Introduction to The Human Code
01:11 Meet Sachin Keswani: Tech Visionary
01:35 The Evolution and Impact of AI
05:30 Balancing Technology and Society
11:04 The Future of AI and Quantum Computing
15:50 The Role of Hardware in AI
27:09 Quantum Computing: The Next Frontier
34:01 Advice for the Next Generation
35:11 Conclusion and Sponsor Message
Sponsored by FINdustries
Hosted by Don Finley
Welcome to The Human Code, the podcast where technology meets humanity, and the future is shaped by the leaders and innovators of today. I'm your host, Don Finley, inviting you on a journey through the fascinating world of tech, leadership, and personal growth. Here, we delve into the stories of visionary minds, Who are not only driving technological advancement, but also embodying the personal journeys and insights that inspire us all. Each episode, we explore the intersections where human ingenuity meets the cutting edge of technology, unpacking the experiences, challenges, and triumphs that define our era. So, whether you are a tech enthusiast, an inspiring entrepreneur, or simply curious about the human narratives behind the digital revolution, you're in the right place. Welcome to The Human Code. In this episode, we're thrilled to welcome. Satchin Keswani. a seasoned expert in the technology field. Where he has worked as a technical program manager and software performance optimization engineer. Sachin is deeply involved in AI machine learning and cutting edge technological advancements. And he's passionate about exploring the boundaries between humans and technology. Today, Sachin, and I will share insights into the rapid evolution of AI and how it transforms industries while exploring how society can keep pace with these changes. The importance of balancing human and machine collaboration with AI, helping humans unlock their creative potential and exciting future of quantum computing and how it could revolutionize the way we solve complex problems. Join us as we. dive into these fascinating topics with Sachin. This episode is packed with insights that will inspire you to think creatively about how technology can shape the future of work, creativity and society. You won't want to miss it. got another great episode here today. I have Sachin Sach Kasani sitting with me here, and it's going to be awesome. he's a man who has many talents and who we have constantly just had a nice back and forth, but Sach, I really want to hear your story. around how you got interested in the intersection between humanity and technology.
Sachin Keswani:Pleasure to be here, Don. Thank you for inviting me. as a child, I always had this dream of working in the technology space. And as I grew, I got to fulfill some of those dreams, came from a higher education in the U. S., studied and worked in some of the leading companies at the intersection of software and hardware. But as I grew and I realized what technology can do, I realized it can solve a lot and lots of problems in society. and and it's a great game changer, a great leveling player. some of it, it comes when technology shows its promise in the sectors, which you had never seen. It could be doing when I was growing up, I was sending letters, handwritten letters, and today we are talking where we can literally see people in front of each other, all within the span of 20, 30 years.
Don Finley:that is an amazing thing. Like we were watching the Jetsons when I was younger and you would see they would have video conferencing and we just had landlines, connected to the wall, couldn't travel too far, but then you also had like wireless phones and you're right. it's absolutely fascinating how we got this far over the course of our lifetimes. And it seems like we're in A place where we're accelerating into a new transformation. what are your initial thoughts on like the excitement that we have around AI today and where you see this going in the next 10, 20, 40 years?
Sachin Keswani:Yeah, that's a very interesting question. So AI, the concept was already there since 40s and 50s in terms of the neural networks, the mathematics, but the necessary compute was never there. And as the game started shifting in the last decade or so with the GPUs being able to process many of those instructions parallelly, suddenly we had an opportunity where there was finally a solution to solve this problem. Now, AI is taking over those things where it can potentially replace a lot of mundane things where you don't need to do a human level intelligence or human level decision making. I see all getting done through AI. The bigger problem is when it comes to ethics. When as a human takes decisions and humans make society, they have certain value system and ethics based on their culture, on this, on the basis of which it takes decisions. And we are coding all these things in an AI, it becomes a little tricky. It's not a problem of technology. It's a problem of how we want to Solve a society's problem using that AI technology. But as far as software and hardware is concerned, we are right on track. This is a decade of a great transformation and not just for this. technology curve, but also in terms of human evolution. I think it's a great paradigm shift that's happening in this decade.
Don Finley:a little taken back because you've hit on something that is really, Close to home and passionate about, Like we have a technology that can aid in that transformation for humans. where are the areas that you would love to see that technology being applied?
Sachin Keswani:A lot of places where I would say, in traditional computers, we had problems which we used to call NP complete problems, which were hard to solve. Some of them are like route planning, operations, transportation, a lot of traffic diversion, which can happen, which can make people's lives easier. Yeah, I can take over those things. It is monitoring it day out. but the bigger solver of, intricate problems that society is facing right now is how do you bring a balance between haves and have nots? It's people who have access to technology or who are determining what technology algorithms are looks like at the forefront of AI, whereas there are people in the world out there who do not have any idea on what AI's impact is going to be on their lives. And these two sections are diverging more and more. So at some point we had to bring them to converge, otherwise one section of society will immensely with AI's growth, whereas another section of society will lose immensely because of AI's growth.
Don Finley:Yeah, we've seen this with the internet, Like having access reliably to the internet is a growth factor for communities, for children, Like it helps educationally. It helps just advancement and jobs and everything. And it seems like we're going to be down the same path with AI as well. I think that
Sachin Keswani:I would say that the pace at which those changes happened were much slower, like it took internet almost 10 years, mobiles maybe five to seven years, and now the pace at which it's happening is two to three years. My only worry is that the pace at which technology is changing is faster than what humans can adapt to. Humans need at least one to two years to adapt to any new technological shift and right now the changes are happening moving forward at a faster pace.
Don Finley:I think that the technology will end up being hamstrung by the human's ability to actually adapt it. And that essentially our hype cycle that we're currently in, there's a lot of emphasis on AI and can it actually deliver upon that hype? Might actually just be slowed down by our ability to actually deploy it. cause even if you look at like how capital is flowing in the United States, it's gonna take capital investments by companies to be utilizing the current level of AI that we have. There's two sides of this. One, you may have corporations that are very adapt and have the resources to go after and fight through this change and they'll end up capturing additional market share. But then the other idea is what happens to the, the 90 percent of the economy that isn't tied up in those. big four companies that can really deploy those resources at the, scale that's necessary. what do you think on that as far as is a limiting factor in our adoption of the technology, the actual human, and will that slow down?
Sachin Keswani:You are right Don, in some cases AI will show immediate improvement in productivity and efficiency but in other areas it might be just incremental improvement. like right now, if I have to go from my home to office, but there's a reminder, oh, now you have to go do this. It's an incremental value, but would I pay additional money for that feature? it's something those companies are investing, have to think about. So I see some of the monetization models, the business models. will also shift. if in the previous cycle, we had software as a subscription model, the subscription model, or average number of users, the impressions that you make, even if they were not paying users, determine the valuation of a company. But with AI, it may again shift on how we want to monetize those AI features. I don't know what it will look like, but it will be interesting to see. Coming back to your question on the benefits, in some sectors, which I see traditionally, like manufacturing or industrials, the benefits might be slower and might be observed over a period of five to 10 years. And it also might be not directly as in software, but maybe humanoid robotics. which are leveraging the AI, so making things more efficient and more productive, but those gains will be more visible over a period of time.
Don Finley:And I think that makes a lot of sense. You have capital intensive industries that may not be able to adapt as quickly. As some of them that have less restrictions, on how they can pivot or move into new paradigms.
Sachin Keswani:And also to add, not every industry may need to pivot that fast. It's right now, I think we are in a stage, we are building a solution, looking for a problem. Yes, we have generative AI tools and content, but that's more like the most common use case, the ultimate use case. We still have to have those breakthrough use cases. I see it as like you build the roads before you can drive your Hondas and Porsches and Ferraris. So right now we are in that stage where we're building those highways of AI where the cars can literally fly. But we still haven't found those cars yet because the infrastructure of the highways is still not complete. And right now we are building those building blocks.
Don Finley:it's a good analogy and I think to take it one step farther, we're basically at the Model T. and Gen AI has leapfrogged that I know three years ago, I was out there saying it's most likely blue collar jobs that we're going to end up looking at automation or like low, lower. entry level jobs that would have been gone. And then you see some of the early like reasoning capabilities that are coming from Gen AI and LLMs to actually create something that is more on that creative side and the like white collar perspective. And over the last two years, we've seen that the demand for content generation is both. probably even higher than what it was before, but also copywriters and content generators are struggling to find, work in some capacity.
Sachin Keswani:Yeah. And if you see the evolution in the last 200 years, whether it was from agricultural to industrial, from industrial to software and whatnot, it's always been a little bit of paradigm shift and hits hardest that generation, which sees that shift, but then we adapt and the next generation is more efficient. So in these cases, the lower end jobs, what people will end up doing is they start using these AI tools as collaborative tools. To help do their work faster and by the next generation, these jobs will disappear. but that's how human society has always evolved. And this change overall would be good for human society. What I see personally would be the biggest benefit. It will unlock the potential for human creativity. And now you have all the tools. You don't have to get bugged down by the mundane things because AI is helping you take care of those mundane things. Now you can really exploit your creative potential.
Don Finley:I think that's, I love the picture you're painting because it is a way for technology to help free up us from doing the things that we, don't really either enjoy or, are just, necessary for the job, but not our greatest skill. And so you're saying we can utilize this AI and like the next door, either this generation, next generation, but continuing to move down that path of utilizing AI to help the human fulfill their purpose and their joy that they can bring to this world. I think that's a wonderful state. What do you think are some of the challenges of us getting to that state?
Sachin Keswani:Our inertia that we have learned over the last 20 30 years, that a 9 5 job only can make a productive output. No, that's just a perception because that were the constraints we had in the last 20 30 years. But you remove those constraints, artists and creative people, they are equally or even more productive no matter from where they work, whether in the confines of their home or from the beach or anywhere, and they produce remarkable outputs. So that means if you remove the constraints, people feel that freedom, they will produce great results. You don't need to put them under the confines of a building and say, oh, that's how only the productivity can happen. But there's one estimate which says by 2034, majority of 95 jobs will be gone. This culture might get a reset. But right now we are coming from that culture. So there's a reset happening and companies are not really prepared for it. And that inertia is what is holding us back. We are in that stage where we are experimenting two steps forward, one step backward, but eventually it will evolve into a new shift. And who knows, in a decade's time, we may have a new definition of what culture, what it feels like working.
Don Finley:Oh, that's fantastic. how can we as individuals or how can we as part of this society, what do you think our role is in helping to move forward towards this, this world that you're painting?
Sachin Keswani:We have to shed the traditional definition of work. We always have been quantifying in terms of hours that one puts in.
Don Finley:Yeah.
Sachin Keswani:The way we measure and the way we quantify has to differ and we have to start measuring how much is the output of that one individual based on that individual's capability. Right now it's one size fits all and all are being measured equally and some of that will start shifting. When you see people performing in a movie, Like film stars, everyone brings a different value and everyone is measured for what value that person, that actor or actress brings, the director brings, and it can't be quantified all being at the same, still all are very important. So that is more like a free flowing model of creativity, which I see extending to all the software, at least if not hardware, it might be more difficult,
Don Finley:I love that we're bringing in the hardware to this conversation because it is one of those, areas that, A, you have much more expertise in, then I do than I ever honestly want to have, but what do you see as being hardware's play in this? and I know in the pre show we were talking about the distributed computing versus centralized computing and how we ride those cycles. And I'd love to explore that paradigm with you of what we could expect from the next future, the future vision of what AI is in the cloud today versus what AI will be, in distributed devices in the future as well.
Sachin Keswani:great question. So if you remember the sine wave or the cost wave, in basic physics that we used to have, that's the kind of cycles we follow. And if you, remember out of phase cycles, you plot it, which is like 180 That's the kind of correlation I see between software and hardware. In software, it's at its peak, hardware is has saturated. But when you need a gain of 10x or even 100x, then software can't take you to that level. Then you need to reinvent the hardware. So if you trace back our history as we were talking pre show from 17s, when IBM said mainframes is going to be future, PCs won't have any future. Someone like Apple and Intel and Microsoft came and invented PC. They said it goes back, the compute goes to the individual. It unlocks their creativity and productivity. And then again, moving forward, we put a mobile phone, even smaller individual unit of compute in everyone's individual, it unlocked even more creativity. And now we are hitting some of the saturation points in those devices compute. So I said, okay, the workloads are becoming interesting. Let's move them to cloud, which is. What 70s mainframes were, an analogy. And now when the cloud would become saturated, again, we will move back where we will come with inventing a new smaller devices, like you've already seen IOT, but there will be also a plethora of AI devices, probably which don't even, which I can only imagine. But I don't know how they will really look like, but maybe they would be smaller to the size of watch or even smaller, but they will just have that necessary compute and everything else is either on the edge or on the cloud. So you're building different layers of infrastructure. how I call, the bank headquarters, then the branches and then the ATMs. Similar analogy, you have a big cloud, the central place where all the powerful hardware would be sitting, the branches, slightly less powerful or medium powerful compute will be sitting and then the individual compute which would be the weakest. But the levels of compute will keep going up at all these layers. Now coming back to the software and hardware, we are in the tremendous cycle or super cycle of semiconductors in this decade, where we are reinventing the hardware because we need to build these new highways and we need 100x gain from the previous hardware that we had. But once that market saturates, software will again catch up. So right now, a lot of this AI cannot be done in software. That's why we had to reinvent this AI. Hardware, which is gives us 100x gain. So every 10 to 15 years, there's this cycle, which, again, the out of phase, 18 years to be precise, the out of phase cycle where software leads, then hardware leads, then software leads, then hardware leads.
Don Finley:Which is amazing because they both, it's a symbiotic relationship and I can't tell which one drives the other, but I also know that whenever you give me more hardware to play with, the software guy in me will just figure out a Like use it all. And there's new things that you can go after. And I think what we're seeing from the space that we're at with GPUs is we, so my background CS with a focus in AI, right? so I've been, I was educated in the winter of AI, right? We didn't have compute, we didn't have the data, but we knew the math and we were like, all right, this makes sense. And we wonder what we could actually do. And early on, you could only just tell, handwritten digits. So still a very narrow solution and somewhat helpful, but also trivial in regards to like where we're at today. But the idea of putting, image classifications and running neural nets on GPU back in like the 2012 2013 timeframe. Was groundbreaking to open that up and you then saw the explosion of the focus on gpus the focus on parallel processing of like matrix multiplication, like all these things Led into this and I do think you're right that we're now seeing that hardware will take you to the next phase, we're seeing innovation around that's both you know more efficient and incredibly more powerful as well.
Sachin Keswani:And two points
Don Finley:yeah, go for it.
Sachin Keswani:Two points. One is that, then it becomes a point of diminishing returns because cost is so high for GPUs and then companies start to optimize. Can I do this in software? Can I do this with maybe CPUs, like new friends probably? So cost becomes the block which then starts, putting the roadblocks. The second thing is, if you remember back in 90s, we used to have ASICs and FPGAs.
Don Finley:Yeah.
Sachin Keswani:And that's an era we'll pick up again, because after CPUs era, we had GPUs, but GPUs were not the most efficient solution for AI. It's just that they happened to be there and they were the most available solution. But there are like, if you want to do some fixed functions or specific problems, you build a custom chip around that. And that is. And, a thing which is happening, a lot of companies, startups, especially in this space, which are building custom chips to solve custom problems, let's say finance or oil and gas. And that will again diverge this industry from GPUs to those specific chips for specific functions. So you will see both of a centralized and a distributed way of solving this problem.
Don Finley:It is really exciting to see all this. was talking with one of my partners, last week just about the idea of, using AI or using an LLM for everything around Hey, sentiment analysis is overkill. we had sentiment analysis 10 years ago. That is like 95 percent of what you could get with an LLM, but cost. A tenth of a percent? we're talking, a thousand times cheaper to be able to do sentiment analysis at scale. Compared to what we can do it today. And I think we're going to see that same sort of paradigm play out where, compute on a, per query basis, we'll get more efficient, but additionally, you're saying like custom chips, we're also at the edge of quantum computing as well. do you see that playing into the world that we have today? Cause it does, it breaks our current understanding of. Of a lot of things.
Sachin Keswani:Sure. Before I jump to the quantum, I'll ask the previous question. So you already seen like individual PCs being released, which have CPU and a GPU, and then a neural processing unit. A chip for the AI workload. So we will see this, we already started to see this happening,
Don Finley:Now, we're going to jump to quantum, but I want to understand what is the difference between a GPU and an NPU?
Sachin Keswani:so,
Don Finley:unit.
Sachin Keswani:so if you see the history of GPUs, it started when the graphics and gaming were the most prominent use cases beyond the central processing unit. And their CPU was limited. now GPUs happened to be there already a solution. But for ai, you actually ideally needed a specific chip built, let's say for Transformers, the models. And there was no such solution. So GPUs continue to be there, but last three to four years, if you see, there's a lot of startups which started to have these neural processing units. Some call it IPU, some call it VPUs, like vision processing units, some call it data processing. So basically to solve their specific problem, they had custom chip, let's say for a data intensive financial workload,
Don Finley:Yeah.
Sachin Keswani:a car, which is using vision cameras, a vision processing unit. So for solving specific problems, you have an architecture, which is customized to solve exactly that problem, and you build a chip around that. And that took off to some extent, but then everyone's needs are different, so it won't scale that everyone needs it. Like Google wanted to build a tensor processing unit, for example, because they are using the tensors as the basic unit of computes, everyone solved the problem in their own way.
Don Finley:Ah, okay. How many different variants do you think we're going to end up getting? Or are we going to continue down the industry specific? But then we also could go back to the, why am I going to blank on The chip that basically configures itself.
Sachin Keswani:Is it self configurable?
Don Finley:Self configurable. Isn't it ASICs or, FPGAs? Don't they have a bit of configurational?
Sachin Keswani:Correct. So to answer that question, it is, again, I will go back to that sine wave. It's my favorite. You have centralized, so CPUs dominate, then you have this other cycle with all these custom chips. And again, you think, Oh, that's too much of fragmentation in the market. You need something more general. And then again, you move, start moving back towards a centralized chip. So right now we had CPU cycle, the GPU cycle, we are going in a phase of this fragmentation of the market, where each one is trying to build their own chip and this is better. Eventually there will be two or three players, which will be like the leaders, market leaders, and by end of this decade, there'll be like, One more, maybe a leading player who would, master the AIs thing. I would call it AI general chip for lack of a better name. AIPU, you
Don Finley:I think it just, you're highlighting, the world that we live in today is very much trying to figure out the best way to get advantage. in the AI space. And we're figuring out like creating the experiments, understanding that Hey, we may need this as specific, but then we'll start to find out that this specific piece over here and that specific piece are actually solved in the same way so they can be merged. And We'll see that kind of like culmination of that consolidation of chipsets.
Sachin Keswani:that's the,
Don Finley:next phase. Yeah.
Sachin Keswani:you said the perfect word consolidation. So eventually a consolidation will happen, but what happens like most of the other industries, you have giants and. who are like CPU giant, the GPU giant. So now if a newcomer wants to enter this market, he has no way other than bringing, solving a specific problem with a specific chip and hoping it becomes a big enough problem so that it becomes a big player. And out of this always there are five to seven different players in this market. Trying to capture this, non CPU, non GPU market. Eventually, there will be another couple of players by the end of this decade, which will become giants. That's my projection.
Don Finley:percent sit there with you. I would love to jump into the quantum conversation for a bit because how do you see that influencing, the world that we're talking about today?
Sachin Keswani:classical computing, if you see, to put it secondly, is you qualify something as a 0 or a 1. And in quantum, we are saying it's a probability of being in a zero or a one. If you see like sunrise and sunset, there's no discrete point which you say it's a sunrise really happened. It's like an analog process. It gradually happens.
Don Finley:Yeah.
Sachin Keswani:There's similarly a sunset happens. to make it simpler, we said, okay, there's a zero and a one. Just to make the computation more realistic, but at the best, it was an approximation. Now that served us good, the classical computing for the last 50 years, but quantum computing is more closer to how the nature operates, that at any point we cannot say with certainty if it's a zero or a one, and now we are building that with all the hardware, a realistic way that we can maintain those states, it's still very hard. It's expensive hardware. It's a costly affair, but the kind of gains it gives in terms of compute, imagine thousand X gain. You can never achieve that with classical computing. And that's the paradigm shift we are looking at when we look at those things. It may be still a decade away or five years away for some good quality, commercially quantum computers to be available. see 2025 to 2027, in this time frame, there will be a chart GPT moment of quantum computing. And then suddenly everyone will be interested.
Don Finley:Oh, that's amazing. So you think that we're actually going to see a somewhat of that killer app type of use case that would come out in the next couple of years?
Sachin Keswani:I think there's some apps already which could benefit from it. It's more about the technological breakthrough. We are at 128 qubits, aiming for around 256 qubits. And for anything to be commercially viable, we need to be around 10, 000 qubits. so once that breakthrough and all the companies which are involved in this. They're trying like five to six different approaches of ion stabilization, the ionization, and one or two of them will win this game, again, like other things. And that will determine which way it pivots. And you won't have just Right now what we're doing AI is on classical computing. You will have a quantum AI, for example, quantum machine learning. And there are already compilers which are trying to do this on quantum machines. It's still early stages, but you can see It's happening.
Don Finley:Oh, that's absolutely fascinating.
Sachin Keswani:And the reason this is going to happen is because we are hitting the limits in silicon. If you see, we're already down to 5 nanometer, 3 nanometer, 2, 1 nanometer, and then we are at angstrom level. That's 10 to the power minus 10. so it brings in that quantum effects because that electrons and all the subatomic particles are so unstable
Don Finley:that love this cause like you're, I think you're going to walk down the space of we're getting to a point where basically the electron could hop the gate through a quantum tunneling because of the sizes that we're talking about where you can't reliably stop an electron from going down the path. In the classical computing standpoint,
Sachin Keswani:And if you remember that Heisenberg's Uncertainty Principle, you cannot never tell with certainty the position or, velocity of, the subatomic particle, if I correctly remember it. And you're actually dealing with quantum effects even in classical computing when you go to sub 2 nanometers, sub 1 nanometer. So there is one projection which says by 27, 28, we might be reaching the limits of the classical computing, in terms of if you can still continue to go with silicon, either we may have to find a new material or there might be some innovation happening with till we have exhausted all the periodic table elements. But again, it's a very research intensive, capital intensive thing to find if you want to move to the next level of compute.
Don Finley:That is incredible. I like absolutely love this. It's so amazing how we are up against the edge of what we know as humanity around Hey, the science tells us that Hey, here's a limit. And we're just going to bump up against it, but we'll learn more about the universe and how it plays out through that. I was reading a paper that came out probably a couple months ago. are you familiar with Penrose, Roger
Sachin Keswani:Roger Penrose? yes.
Don Finley:Yeah, so he has this idea and like you win enough Nobel prizes and you're allowed to have a crazy thought every once in a while and his is that consciousness is actually a quantum function in the brain.
Sachin Keswani:That's true.
Don Finley:Yeah. And the one crazy thing and the immediate was our quantum computers have to be like cold, Like almost absolute zero. They're basically like the coldest environment that we have in the universe on this planet is quantum computing today. And so how in something that is a wet, warm environment of your brain or of your body, could we actually maintain quantum effects? And this paper had. Reliably produced quantum effects in a petri dish. I'm using formaldehyde. I think it was formaldehyde was the chemical in microtubules and microtubules are all out your body. Every cell has microtubules are basically like the building block for it.
Sachin Keswani:Very interesting.
Don Finley:have you been involved in any of that research or do you think we're going to see quantum computing in a, room temperature type application?
Sachin Keswani:that's what everyone is hoping for, but what I see more realistically would be happening is like today you have cloud. like Azure, Google Cloud, and you just operate it remotely. So you have a thin client, like your computer doesn't need to be very powerful, as long as it can access the mainframe or cloud, whatever you may say. Similarly, you will have a quantum cloud or a very powerful quantum machines through which you can access through your regular client. You submit your workload and it gets done. eventually, would you will have this powerful laptops and stuff? I don't know how that will be realistically possible, at least as of today. But I think the next 10 to 15 years, if quantum computers take off, you will have access to them through quantum cloud. In fact, some of these cloud companies are already providing the access to them. It's very just like 32 cubits or 64 I guess Microsoft and Amazon is probably,
Don Finley:exactly. It'll be interesting to see how this all plays out. I'm really looking forward to the 2027 timeframe that we're talking about to see what that like next innovation can be to get us to those 10, 000 qubits. So we can have the cloud computing model. as well, but to come back to today, right? Like we have a number of listeners that are both young or looking through transitions, and seeing what their, both companies and their lives will be. What recommendations would you have for people, about adapting the AI lifestyle?
Sachin Keswani:uh, I would say don't, there's many things that are striking my mind right now, but one thing I wanted to say is get good in mathematics and get good in your communications because these two are your life skills. Don't assume just that because AI is there, you will never need to learn mathematics. That is a fundamental thing. And second thing is your creativity. You have now got actual tools to unlock your creativity. anything that you wanted to do, you're not limited by technology. It's a great time to utilize technology, but don't spend all your time watching, spending on social media. They can suck all your time. time in learning the basics, the mathematics. unlock your creativity and your communication. Even a hundred years from now, these will be still very valuable skills, human skills.
Don Finley:Such that's fantastic, advice for everybody. And I really appreciate you coming on the show today.
Sachin Keswani:a pleasure talking to you, John.
Don Finley:Thank you for tuning into The Human Code, sponsored by FINdustries, where we harness AI to elevate your business. By improving operational efficiency and accelerating growth, we turn opportunities into reality. Let FINdustries be your guide to AI mastery, making success inevitable. Explore how at FINdustries. co.
