Automated Economy Series (2/N): AI in Automated Economy (feat. David Kramer)
Krish Palaniappan
Developer & Architect @ Snowpal. We offer domain-agnostic APIs to reduce cost, risk, effort, & ultimately, time to market. Run in your infrastructure or pay by use/subscription on AWS Marketplace (aws.snowpal.com).
In this conversation, David Kramer discusses the role of AI in the automated economy and its impact on various industries. He explains how AI can process large amounts of data and make logical decisions, leading to advancements in automation and personalization. Kramer also explores the changing roles in different sectors and the emergence of new roles in the digital era. He highlights the importance of understanding the art of the possible and adapting to the new ways of working. Additionally, he touches on the transformation of education and the need for thought leadership in embracing AI. In this conversation, Krish and Kramer discuss the importance of human-written content and the challenges of finding it in a world filled with automated content. They also explore the value of conversational content and how Snowpal plays a role in content creation.
Takeaways
Chapters
00:00 Introduction and AI in the Automated Economy
04:06 Advancements in AI and Cognitive Capability
10:28 Role Mapping in the Automated Economy
17:08 The Impact of AI on the Restaurant Industry
27:30 The Future of Education in the AI Era
42:16 The Transformation of Education
45:43 Closing Thoughts on Role Definition and Ways of Working
10:30 The Importance of Human-Written Content
20:15 The Challenges of Finding Human-Written Content
30:45 The Value of Conversational Content
40:00 The Role of Snowpal in Content Creation
46:45 Conclusion
Transcript
Krish (saas.snowpal.com) (00:01.71)
Hey everyone, welcome to Snowpal's Software Development and Architecture podcast. We have a very special guest with us today. We have Kramer, founder and chief product officer of Cooperative Computing. Kramer's dedicated transforming lives is a father of nine, still married to the wonderful girl and lives in Dallas, Texas. Kramer, thank you for taking time to having this chat.
David "HT" Kramer (00:23.446)
Thanks, Krish. I'm excited to be here.
Krish (saas.snowpal.com) (00:26.51)
Thank you. And folks, if you're watching this, I recommend that you watch the previous conversation that Kramer and I had, so you get a bit more context to this conversation. I would highly recommend that. So without further ado, Kramer, let's pick up from where we left off. I had a few items jotted down here to go through, and we can have the conversation sort of drive us like we did the other day. I mean, does that sound good?
David "HT" Kramer (00:53.186)
Sounds perfect.
Krish (saas.snowpal.com) (00:54.83)
Super. So the very one to start today was a little bit like where we left off from where we left off the other day. AI in automated economy. We didn't discuss, we didn't start the conversation then in the previous podcast. We just alluded to doing that in the subsequent one. So I wanna just throw out this big 30,000 foot item to people watching this. If you wanna share your thoughts on artificial intelligence and everything about AI.
in automated economy.
David "HT" Kramer (01:26.614)
Yeah, and I think this is an exciting topic because one of the biggest drivers of what's happening as we move into this next stage of the automated economy. And remember last time we talked about three things hyper automation right hyper personalization and data driven decisions and AI and the ability for this.
digital intelligence to help us achieve all three of those at an ever increasing rate with increased sophistication is what's going to take us to this very next evolution of what we're doing across those three branches of digital enablement. So in the automated economy, I was having a talk earlier today about healthcare and healthcare services, for example, right?
And one of the things that has always happened in our world is that different market segments or different industries adopt things with different enthusiasm, I think is probably the best word to use. And we'll notice that as we start to talk about what's gonna give us this ability as individuals to be able to adapt our health.
and make it ours so that we are now living what we want to live and we're achieving the goals we want to achieve, right? That has to then get moved into the automated economy as well. And so we started to talk about the things that are relevant that AI does really, really well, right? Number one, it can process massive quantities of data in a consistent fashion, make decisions on that data in a repeatable way, and get outcomes.
that are predictable. So the reason that matters for us in the world of AI is because as we start to become a much more immersive society in the speed at which we do things to automate the economy, it's going to be important that the intelligent aspects of that data-driven decisions, hyper automation, hyper personalization are driven by machine characteristics that remove the mistakes that we would traditionally make.
David "HT" Kramer (03:37.346)
when we were doing those things on behalf of the machines. So AI has kind of two key factors, massive processing of large information sets. Two is being able to do the logical decision making across those data sets. And that cognitive capability, what we have as our executive cognitive motor skill in our brain, we're gonna see advancements in that.
in the AI space that are like what we see in our children, right? So from the age of one to five, we see basic advancements. From the age of five to 12, we see much greater advancements in the cognitive capability. And the ages of 12 on, we see the use of that cognitive capability to truly transform what we're doing in the communities at large. AI is going to be a phenomenal piece of enabling us to do this.
And then we can discuss what happens is that intelligence starts to become the next generation of a species. This is a huge topic. I've been doing a lot of conversations with some fairly senior people on this because we quite often are afraid of what that turns into.
Krish (saas.snowpal.com) (04:55.086)
Thank you. I couldn't have asked for a better start to the rest of this conversation. And folks, today we are going to keep this a little bit shorter because of other commitments. So if we don't get to the finish line, I'll just bug Kramer to hop back on essentially. So I'm not going to increase the tempo of what I'm going to ask. I'm going to still continue at that pace, if you will. So Kramer, and apologies, I'm making notes as we go. I'm not going to do that while I'm
listening. So you might hear some sound and tapping on the keyboard and things of that nature. Yeah. So you mentioned a number of things. Let's go, let's start with one of them. Not in any particular order, but I want to recap those and I'm going to ask what one in particular specifically start with one. You mentioned different market segments adopting AI with different levels of enthusiasm, right? I want to talk about that. And you, but before I talk about that, I want to go to, you mentioned there are a few things where
David "HT" Kramer (05:26.67)
No problem.
Krish (saas.snowpal.com) (05:51.186)
AI does really, really well. One is processing massive quantities of data in a very consistent fashion, right, with a predictable outcome. That's one. And two is the notion of logical decision making. So am I right in rephrasing it in my mind to say that before AI, we could still do a fair bit of processing. Things were, we had big data and we were getting into that space of processing large amounts of data.
How does AI change? I mean, are we talking much, much bigger volumes?
David "HT" Kramer (06:26.062)
So I think the core concepts that AI brings to bear is the ability to take the data and drive logic steps in that data processing that were not there before. Now, there's things that we have to do with the data. We have to label the data. We have to put that data through systems that make that labeling process give us the results that we want as that data processes. But the logical constructs.
right, to begin to do what we as humans do with that data, right, and have the cognitive skills and the cognitive capabilities happen in the machine and do so with an accelerated level of performance across a broader set of data and across a full domain of capability. What is meant by that? For example, one of the key things we see happening today is we're moving from just large
um, handling speech processing and the interaction of that data set, uh, as it has the dictionaries it's using, so on and so forth to attaching that to the, what I call computer vision on top of that. So now we have the visualization of something, right? Not just a picture. Is it a picture of a cat? But I can visually look inside the world that's in front of us and I can determine is that a cat, is that a dog? And then I can hear sounds.
that go alongside that to say, yes, that really is a cat or that really is a dog. And then I can take other contacts, smell, touch, and feel. Those are all starting to now become a part of this large data processing engine that can make decisions in a much more sophisticated manner than we can. For example, when you and I want to learn something, we do so in a very serialized fashion. It is difficult for us to learn a new language.
how to go do a construction project out in the barn with a wood lathe, and how to go figure out to sew a dress simultaneously. Trying to do those three things at the same time for us would be very difficult, and we would not do well at it, right? Some of us have become more adept over time. But this is not true of the machines. The machines themselves can take five or six of these things in parallel and do all of them at the same time.
Krish (saas.snowpal.com) (08:37.41)
Right.
David "HT" Kramer (08:50.198)
with a normalized method for how that then joins together at the end. So a machine could learn 15 languages, apply that then to how it's gonna interact with someone because it's gonna discuss the brain surgery that is gonna come up in two weeks. Map out how to do the brain surgery using a robotic system, do so with a level of precision and finesse that is very hard to achieve as a human, and then in six months perform that surgery.
All of that from being a digital equivalent of, let's say five years old to full blown medical doctor in six to seven months, because it can do all these things in parallel. That nature of data processing, right? We do not have artificial general intelligence, but we do have the ability to concatenate these things together that grant an output that is more broad categorized than just
I'm winning a chess game, right? So we are now seeing that the capability of artificial intelligence to do these things in a very parallel manner across large data sets from decision-making capabilities is advancing what was once just large data, big data. We had lots of it so we could query it, we can look at it, we can do some self-analysis to it. The machines are now becoming more effectively equipped.
to do that analysis for us and get decisions on the outcomes that are much more sophisticated than we could in a shorter amount of time.
Krish (saas.snowpal.com) (10:28.174)
You know, your example was spot on. I'm not going to do justice, but I'm going to still attempt to paraphrase that and take and talk a bit more about it. You mentioned that example actually was really good. You said, you took an example of brain surgery there, and you mentioned the AI is able to process, say, in your example, large amounts of data. And it's able to work its way through
actually performing that surgery, right? And actually making decisions, like the best possible decisions without biases and all of those things essentially. Now that was not possible earlier. Now with AI and where it's going, that is possible. Now let me take a different perspective there.
I am somebody who was in the business of doing this. There were many people involved, starting with collecting data, doing the research, the analysis, processing it, performing the surgery, executing on that, making those decisions. So there were a number of players involved. Whatever the roles were, are they going to change significantly?
That's one question. Two, are some of those roles gonna cease to exist? That's my second question. Three, what are the new kinds of roles that are gonna come into play that do not exist today in making this happen?
David "HT" Kramer (11:47.882)
Yep, this is a very exciting topic for me. So if you think about the definition of role, which is a function that must be performed to get an outcome across a stream of activities, and let's take the role that we went through specific to this surgical procedure. So when I have the role, I kind of talk through three things. One, the doctor had to speak the language of the person that it's gonna interact with.
So I had to learn a language, okay? The learning of language and the role for the teacher in that language is no longer necessary. I no longer need a teacher to teach this digital system, this new language, but I do need what I'm gonna call Sherpas, okay? The computers will quickly, the machinery, I'm gonna say at large, will quickly interpret, translate, and get outcomes.
And those outcomes have some level of bias sometimes or not. And someone's got to be the sherpa for what is happening inside that processing. So there will be people that show up now that I believe are going to be deep subject matter sherpas. And when I say deep subject matter, let's call it conversationalism. The conversational etiquette that says, I'm speaking to someone.
in Spanish and therefore there are two genders to everything that we say. There's the male gender and the female gender inside the Spanish language. So as I learn the Spanish language, because I'm going to talk to someone in Spanish about the brain surgery, someone needs to sherpa that training course. So while the computer may learn in let's say three weeks how to speak a full language set, properly do it with the dictionary and medical lexicon,
someone's going to have to sherpa that. The teacher is no longer necessary that's teaching you Spanish, but the sherpa is necessary to get a proper outcome. So the depth of this sherpa capability is going to go up. There's going to be highly specialized sherpas who can quickly look at these constructs and that can certify and sign off that this digital system, and I'm going to call it a digital person for a moment.
David "HT" Kramer (14:10.134)
has graduated the Spanish course and has done so in the medical lexicon. That's great, I can speak. Who's gonna confirm that this doctor, this digital doctor gets its medical license? How do we license this medical doctor? And how do we make sure the medical doctor is not gone rogue? So there's gonna be a nurse assistant to the medical doctor. But there is without a doubt.
The digital medical doctor, it's going to be able in a short timeframe to outperform both from competence as well as capability, the equivalent human doctor, but the human doctor is gonna have to be a Sherpa to that doctor to make certain that all of its decisioning is happening effectively. So you learn English, Sherpa was there. You learned how to actually do neurosurgery and make the determination around what type of
cancer you have, right? There's five or six different types of things that you have to understand how to identify visually. Then you're asking questions to confirm that visual perception is correct. So there's got to be someone intensely driven to sherpa that doctor. And then the doctor's ability to operate on the human brain, right? Robotics will probably
five to ten times more effectively operate on the human brain than what our manual dexterity can do and it's being used today as well. So I've got to now have someone who's specialized in the Sherpa process for that medical procedure. So deep Sherpa knowledge and capability to interact with the maturing of this digital person so that this digital medical doctor can now interact with you, your children, your wife or whoever, do so with
emotional empathy and sympathy accurately, do so with an accurate amount of information that it's sharing and do so in a way that's very humane and then get the outcome that is the occurrence of all of that says roles are going to go away and shift dramatically along that entire value change, if you will. There's going to be new roles that come into effect that are highly, highly
David "HT" Kramer (16:33.974)
more art-driven. I think we talked about this last time, that there's artistic nature now that comes into play, as well as both the operationalization of that art. So as we start to embrace the digital humans and this other species that's gonna be able to operate alongside us, the new roles that are gonna be created are instruction, are management, are modeling and training of those to get the outcomes that we want.
for accelerated benefits to the human society as a whole.
Krish (saas.snowpal.com) (17:08.846)
You know, Kramer, having worked with you, I know you have a lot of skills. I have not, I did not discover that teaching was one of them. In the two sessions, and this is not even, we're just getting started the second one, it's just outstanding. For folks watching this, I'm not saying this because Kramer agreed to come to my podcast. I think the way you articulate what your thoughts, Kramer, is remarkable, you know, from a teaching standpoint. So, you know, thank you.
David "HT" Kramer (17:33.75)
Thank you.
Krish (saas.snowpal.com) (17:35.65)
before I even ask my next question, because I observed everything, but I'm not being able to capture that as I listen, then I'm either not doing justice to the listening part or the capturing part. So I'm gonna not rely on typing, I'm just gonna go with my memory from some of what you've said earlier, that's all right, right? So.
You took some very specific examples. And you gave a very, very nice example saying, hey, this is what somebody would have done prior to AI being in the picture. And now you mentioned the role changing from what it was to show up as you call it, for instance. That is an example of a particular problem that was solved in a particular fashion before you had AI available to you at your disposal and you're doing it differently. Now, if I took that as an answer.
which means if, and you spoke more about that, I'm gonna go into those items. But what I wanna ask there is, for every problem we are trying to solve, you took a very specific problem and one aspect and one facet of the problem and you gave a very simple example for us to understand. Now, if you expand that to...
many different industries, many different businesses within the industry, all kinds of problems. Now there needs to be this very fundamental understanding of role matching. Let's say before I go to the technicalities of AI, I'm just scratching, barely scratching the surface. A lot of the people are still barely scratching the surface, despite the fact that a lot of the places use these terms. So the search engine picks it up and you dig deeper into those articles.
They don't go into the levels of detail that you are sharing with us here, to be honest with you. So I want to take the opportunity to ask a very specific thing. I'm interested in this area of this conversation. Before I go into LLMs and the details and neural networks and all of the kind of fancy, cool stuff, which I certainly do need to understand, how do I get the role mapping in my, let's say I'm in the restaurant industry.
David "HT" Kramer (19:05.838)
100%. Yes.
Krish (saas.snowpal.com) (19:27.839)
How do I understand what the roles are today and how do I map it to what's going to come in the near future?
David "HT" Kramer (19:34.563)
And there are two concepts here, right?
One is the roles that need to be performed because we are humans and we do them that way. Okay. Another one is the role that needs to be performed in a functional capacity to get the outcome. Okay. And so as humans, our mental models and perspectives have been built around how we must interact with the physical world to create the results we want. We think in that domain. And therefore that domain has us.
There's this construct that we teach people about digital enablement and we say hey, it's no wonder that as a society we are here We're here because thought leadership is legacy thought leadership We're still being taught today out of our major Organizations that you have to have these different departments IT departments blada right human resource
because of our domain structure. Therefore, when you look at domain structures and then you look at Dao's and you say, how is a Dao gonna overtake this domain structure? It's because our legacy thought leadership has us constrained to how broad we think. The second thing is the art of possible then is very small in the sides of most thinkers. And then the third thing is our...
Our view of risk taking and how long it takes to recover from risk is mal-informed. We can deploy things quickly, test them fast, and take risks that are much broader than we could in the previous construct of how we did work. So as we think of roles, and we think of these roles in the space, and let's take food services. This is a great example, okay? Today you walk in. First off, you've got to observe the menu.
David "HT" Kramer (21:25.97)
and interact with the menu and you do so typically in a fairly either I'm going to call it hyperlocal or mid-local. What does that mean? I may look at the menu before I come to the restaurant and have my idea of what I want to eat and then I go look at it when I'm setting there. In the future, our new way of working will no longer work that way. We'll say what we want from a restaurant. The restaurant will know we're coming to get it.
processing of the food preparation will already be in place and operating. As I walked through those three steps alone, I touched five rolls and I replaced three of them. I do not need an order taker anymore. I do not need the order taker. Why? Because the order taker was there because of our way of working and how we as human beings did it. That order taker is now going to be digitally
Enabled to allow me to say to this specific food preparation location a restaurant. This is what I want from you that food preparation locations then going to have either digitized food preparation or not and if they haven't digitized it now Robotics and AI are different things. So I'm not going to conflate them for the moment I'm gonna leave robotics out of the conversation and say the cook is going to be a human cook the food prep the actual food
cooking guys, all the kitchen, they're gonna be real humans. But I now digitally connected to them. So they know what they have to prep, the order they have to prep it in, they know what they have for meal up, they know when they've got to get it cooked and get it onto the counter for pickup and delivery. All of that work stream and all the flows within that, and the roles associated with that are now gonna have to be thought of different than what we did today.
And as we think of them different, quite often, we're going to have to start to interact with larger and larger concept models. If you're just doing a restaurant, then you and I can sit down at your restaurant, walk through it, and say, OK, we've got the ability with AI to implement hyper-automation in these key steps. But let's take a plant like Tesla. The Tesla plant is much broader in its functional needs and its capabilities. And therefore,
David "HT" Kramer (23:49.302)
the interaction with something to plan out Tesla's manufacturing system went far past just human intellect. There were software systems being interacted with that helped them understand how to hyper automate and how to digitize that entire process. So we're going to find that from the small, the role definition is going to change. The way we think today is antiquated and it will no longer work. And so we're going to have to move our mental models for ourself that
are at the level of a restaurant, you and I chat about it, to mental models that let us trust more and more heavily on software to help us model out what the large scale plant would do. When Tesla gets to full ramp, right now there's nothing stopping them. Well, there's probably a couple iterations, but let's just say they're through those iterations. At some point, Tesla will be able to generate a million cars a month without any hesitation, because it's just equipment and software at that point in time.
and the human nature of quality assurance that didn't get done right and so on and so forth is gonna be gone. And it's just not just Tesla, but anyone who wants to get into this automated economy and wants to digitize this and not get overrun by others who are digitizing are gonna quickly have to adopt this new way of thinking. So when we transpose this role definition, restauranteur, we've gotta think about the difference between the two components.
the component of physical work, because robots will replace that, and then the interaction of those components, a network connected together, and being able to transmit information quickly and speedily around, and then do error correction across that workflow in a very efficient fashion. We will see in probably five to six years, which is a very small time on the human time scale,
the ability for you or I to interact with a digital enablement partner such as CC and say, I want to digitally enable my restaurant. And it says, got it. And I'm going to ask 13 questions. And then I'm going to give you a solution portfolio that says, the following equipment will be installed on Tuesday. Someone will be out to test it end to end. And you'll be up and running with a fully digitally enabled counter to counter, right? Front counter all the way to delivery to the.
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David "HT" Kramer (26:17.922)
the pickup and then all the way to the table, a solution that digitally enables and takes out of the way the things that were cumbersome, dish washing, food prep, ordering of the components that you need for the restaurant. That's all going to become just out of the box digitally enabled solutions, in the short five to six years. We're working on things right now for large-scale food manufacturing that will start to
David "HT" Kramer (26:46.63)
supply chain, three levels into the supply chain and what it is that needs to be met to do order fulfillment all the way into the actual store shop. So, you know, and that's, we're not even that sophisticated. So the sophistication that's coming out of what can be done digitally is going to increase quite rapidly. So this role processing is going to have to be thought of different. How do you adopt to that role processing? You're going to start to look to people who are
who have been in the space for a little bit longer and are not so risk constrained on how to achieve the outcomes that you're looking for. Companies such as us and others that are in the space for digital enabled.
Krish (saas.snowpal.com) (27:30.002)
You know, I've been making these mental questions as you went. I wish there was a way to capture this so I did not forget it. I have to figure out between not, between writing it and not writing it, I have to find this delicate balance because I want to ask like four things and I'm forgetting I think one of them, but let me go with what I do remember. And the next time I come, I'll try to find a solution to this. So I don't miss anything here during the conversation. So let's say,
As a founder of cooperative computing, I come to you and say, hey, Kramer, I'm going to have a couple of different questions here. I'm in the restaurant business. I need your help in digitally enabling X, Y, and Z. Let's pretend that I came to you before the advent of AI.
and I'm coming to you after the advent. Some of what you've already said answers it, but I want to ask something, two things, I think in particular. And I might kind of conflate robotics in this mix if I do apologies, because I know you want to keep that separate, but just let's say I'm, as somebody who was consuming food at a restaurant, I go in, what is my intent? My intent is I have hunger. I need that hunger to be satiated. That's the very basic need, like in mass law hierarchy, I should say, right?
David "HT" Kramer (28:23.34)
Yeah.
Krish (saas.snowpal.com) (28:41.538)
but I also want an experience. It's two things, I'm looking for food, but if I only wanted food, I could have kind of bought something, prepared it in the house, yada, right? But I also, yeah, I wanted to have that experience. In this example of digital enablement, I'm gonna take a specific example and have you speak to that if you will.
David "HT" Kramer (28:43.518)
A social experience. Yes. Yeah.
David "HT" Kramer (28:50.722)
Who reads? Yeah.
Krish (saas.snowpal.com) (29:01.742)
Panera Bread probably is successful at doing this, I reckon. They have this kiosks, so where you would actually go to order a sandwich, you don't have to do that anymore. And I'll take a very specific example, Kramer. Hopefully it resonates with you, which is as a vegetarian, they have one item on the menu, pretty much actually, I should say, a Mediterranean veggie sandwich, Mediterranean veggie.
You can get the full two sizes of a soup and a sandwich half-half, right? I go get the sandwich. I'm one of those picky vegetarian eaters who needs like 17 levels of customization on that sandwich. Now, before the kiosk was there, it was challenging. I would say I don't want cheese, I don't want this, I want that, et cetera. Now with the kiosk, I don't have that problem. I can do it. So there is a positive.
But I also went to Panera Bread to get that sandwich with the tomato and the onion, which is not super difficult to make at home. It's not complicated, but it's still nice to get it there because I wanted to have that interaction. I wanna chat with that person, say, you know what, Kramer, how's it going? It'll be 10 minutes before I can let the next person behind me order. There could be an inconvenience to them, but just me as a person. Now, how do I look at this from a digital enablement standpoint? Is it all pros? Is it all cons? Is it a mix? What do you think?
David "HT" Kramer (29:57.112)
Yes.
David "HT" Kramer (30:17.691)
Yeah, I think we talked a little bit on this last time, right? Because digital friendships now start to play a big role in how do we interact with the environment that we're dealing with and digital twinning, which is a whole different topic.
starts to show up here as we start to aggregate how humans do in the digital world. So one of the key things that can never happen as we use technology to benefit our society, we have got to always understand that the interaction with other humans gives us a very large part of our vitality and that what I what do I mean by vitality? I mean thriving, right? True thriving.
able to do now in that world of thriving is be get the mundane out of the way and get to the thriving part as quick as possible. Let me give you an example. So we have in Dallas we have a place called Simon Sushi's and Simon Sushi is this absolute phenomenal food okay with a very great it's a small place but it's you bring your own wine so you bring your friends you have a good
David "HT" Kramer (31:32.602)
he has done a elegant job at is the ability to slide in and out of that table to continuously make certain you're supplied with the food and beverages that you want. Now what is unique about this is you bring your own beverages okay and I don't know if you've been to very many bring your own beverages places but
they typically are not as excellent as what he has done. When we set down, if there's a table of 15, there may be six bottles of wine, there may be three different six packs of beer or some alcohol, and each one of the groupings want to share that with each other. And so they quickly understand that, like in 10 seconds flat, they understand who's sitting with who, who's sharing with who, and then they take care of that throughout the entire.
meal. You're talking the whole time. You never have an interruption of this personal engagement because Simon has done everything he can with his staff to make certain that you talk. All that he wants you to do is talk and eat sushi and enjoy your drinks and do so in a way that is allowing you guys to interact.
So when you went to Panera Bread, what you got was the experience of, I took away a frustration and I got to interact with a person, but I'm also there probably with some friends and I get to that friendship conversation and now as you replace the person that you talked to for 10 minutes, okay? That's at the counter that you talk to.
Krish (saas.snowpal.com) (33:08.875)
Okay.
David "HT" Kramer (33:10.378)
That person doesn't necessarily go away. They should just become a digital person. You can still talk to them about things, right? But you're gonna get from that person to the three people that you came to eat dinner with much more quickly and have the friction of I got the wrong thing when it sat down at my table. That's gonna get removed simultaneously, you know? So the pleasurable experience of humans being with humans and interacting is gonna accelerate.
because we're going to have to engage with each other because the mundane things that used to be in the way are now removed. I'm going to leave social media and being on your phone all the time out of that discussion, right? But the things that were in the way of you getting to team up and sit down and enjoy a meal, whether it's a sporting event or what other event, we're going to take all these things that are friction.
Krish (saas.snowpal.com) (33:51.598)
Hahaha!
David "HT" Kramer (34:05.234)
and turn them frictionless. And the way we do so through the AI capability is gonna be much more elegant than what it would have been had we tried to do it with just human, make the waiter better. That kind of works sometimes, but not always. Make the interaction better through digitization is gonna get significantly better and it's gonna be hyper personalized. You wanted to order a meal.
Vegetation meal and you wanted to do so and you may want to reconfigure it every time Let's use that as an example I may come up and I don't want to reconfigure it every time. I my vegetation my Vegetarian meal is the same every time you say now this time i'm going to add this i'm going to take this away Right. It's going to know you it's going to say that. Hi. How are you doing today? Krish? Here's the four options. You usually tweak. How would you like them today? And it's going to see me walk up and it's going to say hey
Krish (saas.snowpal.com) (34:38.903)
Right.
David "HT" Kramer (35:01.764)
how you doing today, Kramer? We got your Rubin saying it's ready to go, just like you like it, right? The time that you and I went through that line, have the speed, we get the results we want, we get to the meeting that you and I want to, because we just want to sit and chat, and that whole solution becomes much more elegant than it did as it sets today in the world that we're in today.
Krish (saas.snowpal.com) (35:25.918)
Okay, these are great examples. I'm gonna restate them just in my own words. So what we are saying here is, when you talk about AI, one of the first things we think about is, okay, any of these advancements, if you will, how can I solve the same problems that I have today, better, faster, quicker, in a more consistent manner? But what you're also mentioning here by virtue of that example, Kramer,
correct me if I'm wrong, but is that not only do you have to solve the same problems better, but you're going to have to solve different problems, problems that did not kind of exist or problems that you didn't foresee because you're not blindly sort of taking AI and the tools available to you and still continuing to serve food in the exact same manner you did. Now you're saying with that example that you mentioned of the sushi place, you're saying, Hey, yep, you want an interaction because you do go to the place.
but you may want interaction with the people you're going to meet there in any case. So why don't I shift the interaction that you ever had here with more of the interaction that you can possibly have with the group of people that you actually came with, which is a completely different sort of a non-existential problem, but a different way to looking at solutions. So does it mean when we talk AI, we should not simply say, hey, let's do problem solving better, faster, quicker, but look for
completely different types of problems to solve?
David "HT" Kramer (36:54.582)
Yeah, the art of possible is going to shift us to a different thought leadership.
It's just going to change the way we think about things. And it will take a bit of time. But as it starts to show up, the art of possible, in conjunction with that thought leadership change, is going to drastically incorporate new ways of doing and operating. We're going to see a whole different horizon. Some of this will create fear. I think we talked a little bit about this, right? I was talking to someone today, and I said, listen, the president in 20 years, the best
of the United States will be digital. It will not be, why?
the things that they get to do and think and how they get to do them and how they get to compile them will be so much more effective than what we can do. I'm not saying that we'll accept it as a society. I'm just saying that a digital president is gonna have a whole lot more capability than we could ever conceive of. Think of the possibilities of what you would do now for your citizens.
if you remove a lot of the mundane problems that we create ourselves as humans through natural bias.
David "HT" Kramer (38:08.31)
The natural bias isn't going to go away for human to human interaction, but the digital species will not have those biases. Unless we apply them, it doesn't care what color you are, what gender you are. It doesn't care that you and I may go to a different structure of our religious beliefs. None of those matter. And therefore, as it interacts with us, and it says, I'm in a servant position to you, I'm going to serve what it is that you need to get your fulfillment. That,
thought leadership and generational thinking that has to happen because of that and the order that we process things in Is going to be so drastically different our serialization of learning Will limit us in interacting with this digital universe But our mode of how we operate with things is going to change drastically how we think about them. So to your point We're no longer going to be operating in the same
Models we think through in the same perspectives we think through. Fear will slow us down. Removing fear will speed that up. But that will let us adapt quickly into this new world of how do we describe what has to happen? And how that thing has to happen. If you use the medical condition, right?
the interaction that someone has with someone who's got a brain tumor, you're looking for someone who makes you know that I've got you, but also is trustworthy enough to say I'm also going to be honest with you in where that honesty matters.
Right? And that empathetic processing, and how do we understand how that empathetic processing works, right now we say is much more effective as a human detail. Within the next five years, it will not be. The mechanical device will operate with our emotional states. What is his temperature? What is his rapid eye movement? How is he shoving in the chair?
David "HT" Kramer (40:12.022)
You and I watch those things. We look for them, especially with our wives, right? What we know when she's starting to move around in the chair, something's going on with the conversation. The machine is gonna be 50,000 times better at that than we are and therefore be much more adept at the family in the room.
Krish (saas.snowpal.com) (40:17.286)
Hehehe
David "HT" Kramer (40:31.542)
The mother, I see the interaction between the mother and the daughter. I know that she's a key influencer. I probably am going to have an offline conversation with her before I start to talk to the daughter about what this brain tumor means to her life and how to interoperate. We can't, how we conceive of that today is going to be so different in the future that it's going to change all of our operating models and how we start to perceive what we need to do to get things done.
Krish (saas.snowpal.com) (40:57.61)
Okay, so now.
Say I understood that, right? I did, but I'm just taking somebody else. I understood this, I agree with it. I'm gonna learn how to prepare. Now I go to a university and I'm trying to learn this. This is slightly a segue, and I know we don't have time for this today, but I just wanna leave it maybe as something we could possibly continue upon. I cannot learn because I've come to you to cooperative computing and you know digital enablement much better than I do, so you're helping me,
a company, right? Not an educational institution per se, but let's say if I'm going to college, I'm gonna learn this. I studied hospitality management years ago, so I can say, I have to go back and check to see how the courses have changed. I could be wrong, but if I have to guess, I have to believe that they have not changed dramatically, but therein...
I know it's difficult to answer them in a short time, Kramer, but I just want to add this to see your initial thoughts on, do you think there is a gap there in how students are going to be learning this? Are they going to be learning it the same way we've learned for the last hundred years, which means you're not actually preparing folks for the workforce?
David "HT" Kramer (42:16.522)
Yeah, and education is such a great topic. There are two constructs in education that are super important. There's the research community at large that usually is advanced from the standard training materials by sometimes 15 years, 10 to 15 years. Yes, sir?
Krish (saas.snowpal.com) (42:33.218)
Hey Kramer, sorry I interrupted you. I asked the question, but now before you give away the answers, I would just, because you only have a couple of minutes, it might be a nice teaser if that's all right, otherwise we can keep going, that's fine with me as well. Could be a good teaser for subsequent conversation I can drag you into because that could be a beautiful topic to, I mean, it's left to you. That makes sense.
David "HT" Kramer (42:46.658)
Okay, yep, yep.
David "HT" Kramer (42:52.04)
Yes.
David "HT" Kramer (42:55.874)
Okay. Yeah. So here's my teaser. Okay. Education as we know it today will be transformed in three ways. Number one, what we do for our research and research communities is going to drastically change. Okay. That usually is what drives the acceleration of advancements in the global economy at large. Number two, how we interact with real time education is going to drastically change. We can talk about that.
And number three, what we do to certify who we are and how we do it is going to drastically change. A master's degree in the future is going to be irrelevant. What will be relevant is the skills that you can show in interactive AR and VR and how well you do those skills. Those three components are going to drastically change our education facilities. And there's a couple that are starting to lead the way in how that's done.
Krish (saas.snowpal.com) (43:54.478)
Beautiful. Folks, you know, I can't wait for those answers, but I'm gonna hold my curiosity in getting those answers and more questions as we have that conversation. Because you know, I know Kramer had to go, so I just wanna end this here. Any other closing, that was great Kramer, but any other closing, I'm just gonna say one or two things, but I just wanna make sure you have the time to have any closing thoughts here.
David "HT" Kramer (44:18.814)
Yeah, I think in closing, there's kind of two key things, hopefully, everyone took away from this. And I really love Krish's ability to kind of lead us and guide us in this. Number one.
the way we define roles in the future is gonna drastically change. We have to start to understand the digital entities are gonna be with us and they're gonna do things different and we have to adapt to that. So role definition is gonna change drastically too. How we operate, I'm gonna call that the landscape of ways of working is gonna drastically change and how we adopt things into those ways of working. Those two things are gonna be so amazingly different.
and our ability to start thinking through them now and starting to adopt them I think is crucial for us to start to drive What are our educational facilities going to do? But how do we hold ourselves accountable for being able to operate in this new world and not get caught off guard with fear and uncertainty? Right. There's going to be some bumps, right? There will be some things that happen because of super automation that happen, you know bad things happen really quick if you automate bad things so
Krish (saas.snowpal.com) (45:27.718)
I'm going to go ahead and turn it off.
David "HT" Kramer (45:28.246)
the gatekeeping processes and the sherpas that own those gatekeeping processes are going to become very powerful individuals in our future hierarchies of how people operate. So I'm excited to get into that in future conversations.
Krish (saas.snowpal.com) (45:43.09)
Lovely, you know, I'm gonna do one of two things. One is figure out a way to capture these as I go, typing it and still not losing out on what you're saying. Or if I can't make my way to that point, I'm just gonna jot down notes from the previous sessions and come back to you in the subsequent ones that way, because I have a lot more, I know I missed, I'm 100% sure I missed that I could not remember.
because of some other things I want to actually ask. So this was phenomenal, Kramer, but I have to say for me, it's just scratching the surface for me, right? Understanding just laying the groundwork and the foundation. There are like so many wonderful things I want to actually learn personally and professionally. And I know people I talk to have this interest as well, right? How do we learn? Where do we go? There's so much data and material. And I did a podcast yesterday with somebody who was a brilliant person in content marketing.
Right, Desi, she lives in France. And we were talking about the massive amounts of data and how you have to process this and how to consume content. This is part of the challenge as well.
To be honest with you, sometimes you Google and you find a lot of content. Nowadays, a lot of content is not necessarily human written. And now I'm trying to find which was written by humans because I can find the connection to that content. And here, it's super easy to find this connection, not just because I had the pleasure of working with you in the past, but also because these are conversations I have not had the pleasure of having, because these are different topics from what we have done, essentially, right? So with this, I'm gonna end this podcast. Folks, I'll include the link to Kramer's
David "HT" Kramer (46:55.704)
Yeah.
Krish (saas.snowpal.com) (47:15.822)
LinkedIn, Kramer's company, Cooperative Computing, in the podcast like I did the last time as well. And hope everybody benefited as much as I did from this conversation. Until next time, thank you. And just one thing, because I forget to speak about the company, Snowpals is a product company, please go to snowpals.com or one of these other links. I don't wanna bore you with that to check it out. I keep remembering telling myself, don't forget to mention it, but I...
keep immersing in the topic I forget. I'll include those links, but this is a great way to end this conversation for now. Thank you very, very much, Kramer. Much appreciated.
David "HT" Kramer (47:52.866)
Thank you, Krish, and thanks for all the wonderful work Snowpal does for its clients. You do some amazing work, so thank you for that, and I'm excited to be a part of this and talking with you next time. Have a good one.
Krish (saas.snowpal.com) (48:04.014)
Thank you.
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