This episode has been automatically transcribed by AI, please excuse any typos or grammatical errors.

Chris Glanden: Welcome to BarCode. I’m your host, Chris Glanden. And today I’m joined by two esteemed guests once again who have appeared before on the show. And it’s great to have them back. Charlie Northrup is a technology innovator at NeuroSciences where he designed secure systems for the hyper-connected universe, a paradigm for exchanging value across digital ecosystems with multiple patents in holographic memory, optical ID, and distributed computing. His work powers breakthroughs the universal framework of things and the agent to agent economy. Keenan Hale is a trans-disciplinarian whose work represents a significant advancement in applied category theory, bridging theoretical mathematics with practical cryptographic verification systems that enable AI agent trust in ways previously thought impossible. And also here with me is a special co-host. Mike Elkins, Chief Human and Information Security Officer at BAMFIST, as well as a visionary in cybersecurity, enterprise risk, and digital transformation. Everyone, welcome.

Mike Elkins: Good to see y’all.

Keenan Hale: Good to be back.

Chris Glanden: fellas, again, thanks for joining me for another episode. know everyone on this call has been super, super busy lately. we’d just to go around the horn and, talk about what you all have been up to since we last spoke. Keenan, you want to kick it off?

Keenan Hale: you gosh, it’s been about three years now since I was last on. That was with Harkness AI. Moved on and it was with VyU AI. And during VyU, I was helping to build wind systems that were utilizing AI, increased wind capacity by 70 % on large scale utility wind farms. After that, pivoted back to my good friend Charlie to see if there was an opportunity for us to do some collaboration and that’s where we are now. That was a few months ago. I’ll turn it over to Charlie to let him explore a little bit into that and dive into the topic for today.

Charlie Northrup: Thank you, Ken. And yes, it is good to be back down with you guys. I’d love to have an opportunity to talk with you folks and learn from you and share what I’ve learned along the way. And it’s been a little bit, but there’s always more to learn. That’s what I’ve come to is that as humans, we’re always in what I call a Delta K moment where we’re constantly evolving our knowledge. when you apply it to AI, it is it’s exactly the same the same goal. And then it gets into Wow, once once you start understanding more and thinking about reality and consciousness, and all of a sudden you say, Well, this is this is a really interesting platform and framework to explore it all. that’s what we’re here today to talk about. Definitely, I can’t wait to get into it. But, Mike, I see you regularly. But let’s I would love for you to tell the listeners what you’ve been up to.

Mike Elkins: . left my corporate job at the end of June and I’ve spent the last almost year working on a couple of different entrepreneurial endeavors. launch of my own technology company. have a nonprofit organization that I’ve also launched. BAMFest is the name. I’ve been working very closely with another nonprofit organization called the Cognitive Security Institute. really helping to increase, the human firewall side of things from a cognitive ability and I’m actively working on a real estate investment journey that can’t reveal too much about now but if I can get this deal to go through and raise all the investment dollars it should be pretty spectacular.

Chris Glanden: I want to go back to sort of what you what you just mentioned in terms of, where we were before with our conversation up through now. And I know you’ve done a lot of work in the AI space. And I notice now, I think more than ever before that, AI has really been raising a lot of

Keenan Hale: you

Chris Glanden: of complex questions about its purpose and its trajectory, whether it’s sort of following this predetermined path that’s that’s built on, pure ones and zeros, Or if it’s, beyond that, if its development is emergent and shaped more by external observation. from what you’ve been up to lately, we’d love to hear more about, this concept, if you don’t mind, and sort of just level set with the listeners who may not be familiar with this. with this concept, what that means and then your thoughts on purpose and trajectory.

Charlie Northrup: Sure, sure. I mean, a lot of work been being accomplished in large language models. And I think we can’t have any kind of a conversation about AI until we talk about large language models just a bit. And what they do and what they’re good at, what there’s still issues that need to be considered. if you look at a large language model in a real simple high level view, it’s being able to go out, read every single web page that’s out there and put all the words together from every single page and say there’s a mathematical model that this word should follow that word. And if you If you can look at everything that’s ever been written from a historic view, we could say here is the next probable word that should follow. And then of course, it got into things context. Well, the word draft, for example, has a very, very different context depending on whether or not you’re talking about the NFL draft, the draft from a window or a draft at a bar, Or even a military draft. the words and the context matter. And LLM spent a lot of time working on trying to get context and try to take the words, break them apart into what they call tokens. And they have these mathematical weights to say that this token should follow that token, cetera. And you take your prompt, your input word, it gets changed into a context window, which is just a bunch of tokens. It’s broken apart. They apply the context window to the model. And then they generate the output. basically it’s the same thing.

Charlie Northrup: What am I asking of the LLM? It breaks it apart into individual tokens. It takes the token, supplies it to its model, generates an output, takes the output, formalizes it back into a language, and then gives you the response. there are some challenges with this. And one of the challenges is that the model that they produce is a static model. It’s stateless also. you have a static stateless model, but you take your prompt, break that apart, take the context window applied to the static stateless model and generate an output. What it tells you is that the model itself doesn’t change. The model is the model that doesn’t change. What changes is just the input context window, how big that can be, but it still doesn’t have all. the knowledge in the world. only has what it was trained with at a moment in time. All. why does this matter? What are we really trying to get at here? What we’re trying to get at is to say large language models are really good at Delta K. Delta K is the transformation of what I knew into what I can now know. you take a process, you apply a process to a thing that. and it generates an output. This is lambda calculus all day long, This is basically, it’s just a Turing machine. You take the input, you run a program, you get output. when you’re looking at the Delta K output, the change in state of the output, you haven’t changed the model, just the output. now you’re looking at that and you’re saying that’s a really good Delta K. However, we know that the LLMs, for all their benefit they suffer from what’s called hallucinations. And that’s because they’re a predictive model. And predictions are just predictions. It’s not based on facts, it’s not based on thought, it’s not based on any form of cognitive capability. It’s strictly give me an input, allow me to run a model over top of it to predict what the output should look. And there you go. Now you have the output. that’s the problem. The problem is it’s just a delta K. And while Delta K is important, it’s not the only thing that we need to be thinking about. And it’s why we can’t, why I don’t believe that you can get AGI strictly from Delta K. There’s not a chance that you can do it.

Charlie Northrup : You’re missing a bunch of other parts that all have to come together. And that’s what we’ve been focusing on is trying to understand if you only have a Delta K model while it’s good that model is strictly a predictive model, then you’re going to run into problems of hallucinations because it’s not grounded in any form of reality. that creates the problem, the research gap that needs to be solved. Does that make sense? It does. It does. In simple terms for the listeners, would say the simplest way to think of it is we know things, We know basic things. And then we have experience, which is a process. And it allows us to formalize new things in our brain. We take what we thought we knew, we see something or somebody tells us or there’s rules that you go by and it changes what you knew into what now. And it’s a constant evolution of knowledge and change. But you can’t do that at a universal level, what they’re trying to do, and assume it’s going to work. It doesn’t. that creates the dilemma that creates the problem that they have not felt. delta K, then is is more of a limitation.

Charlie Northrup: I think that Delta K is only a part of what you need. the challenge is that Delta K, when it’s strictly a predictive model, that creates its own set of limitations. It’s a model that’s based on prediction. It’s going to a fortune teller, That I can go in and can say, predict what the future is going to be. And it can predict. And sometimes it’s going to predict, and other times it’s going to predict wrong. You give the NLM silly things, a string of zeros and ones and you ask it to count the zeros and ones and it won’t be able to count them correctly. You ask it to count the Ls in lilac lollipop and it can’t count the Ls properly. Because it tokenizes the words and it’s not understanding what you’re asking. It’s just simply, I can take what you’re prompting me with and I can turn it into tokens and I can run the tokens against my predictive model and I can create an output. when you’re asking you to do things that, where it’s more specific and exact, it can do it. Now, it doesn’t mean that that Delta K is wrong. Well, for that particular model, predictive It’s acceptable and understood. This is what’s going to be. You can’t change it. It’s just part of the Delta K model that they are using. Now, could we make the Delta K model smarter?

Charlie Northrup: Sure. We can add things what it means to contemplate. What does it mean to decide? And that gets you into a whole different realm about where does the world go beyond Delta K, beyond that exchange of knowledge, Exchange of knowledge. I’m sorry. the transformation of knowledge, Delta K. The transformation of knowledge from one state to another state, it requires a process. We do it internally in our brain. They do it using time and energy, You need electricity and it takes time to run the Delta K. But effectively, it’s the same model as what humans do. We use our internal energy to say, is what we knew. And we’re thinking and we’re trying to contemplate and come up with ideas and consider and we create a new frame of reference and new outputs, the delta in the knowledge. the two are very, very similar, but the difference is that they’re doing it strictly with a predictive model. And that’s why they can’t get past that particular limitation. And that gives you to the next part, is, well, where does it go from there? what follows Delta K. And that’s the part that we’ve been really focusing on. we understand that Delta K is really what we call thin calculus. And we can put together an entire model. actually, I can send you the paper if you’re interested in looking at it, that there’s a calculus that we define called thing calculus. And thing calculus is really fascinating. because inside or with thing calculus, we can model any other meta calculus, it’s meta calculus, and it allows us to model any other calculus. within thing calculus, we can model what lambda calculus is.

Charlie Northrup: That is the Turing machine. think of it as we have this thing calculus, we have a way to perform thing calculus, and in performing thing calculus we can exactly model what is a Turing machine. And we can run the Turing machine. the Turing machine, in this context, the model of the Turing machine is defined entirely by thing calculus. There’s Godel’s rule, which is basically any complex formal system will always rely on rules that can’t be proven within the formal complex system. And the problem that we kept running into was to understand that Delta K and thing calculus, we can define exactly what it means. What is a Turing machine run the model of the Turing machine, which itself is running Delta K and thing calculus and proving that thing calculus works. And it’s, it created a problem, We have a circular reference here that we couldn’t solve. We couldn’t solve. Why is it that we can create a thing calculus and we can run thing calculus to prove thing calculus works exactly as we say it does? We were missing something because Godel’s rule says you can’t do that. There’s something that you’re missing. And that’s when we went back and realized that the mistake was we did not have a truth that we couldn’t prove inside the system. And now we need a truth that is external to the system in order to build it. a Delta K. The Delta K was in mathematics, there’s a category theory. And what we needed to do was to describe the category theory of things. And once we defined things theory as being a category of things in mathematics, now we can take this thing, this universal framework of things, and we can map it to our thing calculus. And now we can have one thing, only one thing that we have to rely on that’s external to us. And that’s there are things. And once you can, once you can prove that mathematically, system is then sound. And that’s what took long. we could, intuitively we know there are things, It’s simple. Everybody would agree, yes, there are things. But when you try to formalize it without having the category theory of things, the system was incomplete. And that’s what we needed. That’s what we finished. Now we can say Yes, there are things. We can prove it mathematically. It’s all sound that relies on exactly one external axiom, that there’s a category theory of things. And with that, we can now define thing calculus. And thing calculus says it’s a meta calculus in which all other calculus can be modeled and integrated in the unified framework. And that in turn allowed us to define something that we call a genetic calculus. Nagentic calculus is a more narrow view of thin calculus, but it becomes dependent on the observer. it’s an observer dependent calculus that nobody ever really contemplated for. to make this easier to understand, it’s actually not as complex as it sounds, We know things. great. I know things, things.

Every Delta K is a process. Every process takes time and energy. That’s reality. Share on X

Charlie Northrup: We both can agree on that. All of that is simple. But if I ask you to multiply two numbers, two large numbers, I.F. Keenan to multiply two large numbers. The way in which you do that mathematics is different, or it could be, because there’s different ways of learning how to multiply numbers. You get the same answer, but the mechanics of how you do things is different, and that becomes observer dependent. from your point of view, how you’re going to multiply these two large numbers together and Kenan’s, you have different mechanisms, but you get the same results. there’s an intent and intentionality that hadn’t yet been really thought about. now you do the same for division, same thing, give you two large numbers, say divide them. You might use long form division. He might use short form either way. get the same result different, that creates difference between string calculus and agentic calculus. Got it. I want to give Mike or Keenan an opportunity to follow up here and ask some questions. I see Mike starting to sweat.

Mike Elkins: I mean, there’s a lot in this conversation that reminds me about our prior conversation with things in consciousness and reality. coming to a thing calculus sounds it’s a pretty foundational, yet important step to really start looking forward at AGI, agentic AI. And I’d love to, at some point here a little more around reality and consciousness, because Charlie, last time when you shared some stories around some of the projects that you were working on that effectively came up with their own mathematical calculation that your team hadn’t tested. that’s always piqued my interest around the observer side of reality and quantum computing. There’s many things that come to mind with that. But tell me a little more around when you talk about Delta K being an important component, but not the only component for AGI. If I take that analogy as a vehicle. And I say, in order to build a car, I need four wheels, steering wheel, engine, a couple other components. Is Delta K one tire out of the four tires? Is Delta K kind of the chassis of the car? How many more things do you think we need on top of Delta K to really try to get to a true AGI?

Charlie Northrup: If you look at Delta K as just a change in estate, it becomes, again, becomes server dependent. Because when I look at a car, I might think about what kind of engine is in it, how much horsepower it has, what kind of cylinders. I could go on and on about all the components of a car because it’s fascinating. And if somebody else looks at a car, they’re just going to say, it doesn’t have an engine. What kind of engine? Is it a gas or electric? And that’s all that I care. it’s an observer dependent viewpoint as to what thing I am talking about and where the Delta K is occurring in my point of view. When I’m looking at it, I go to the doctor and I say, I don’t feel well. And the doctor says, here, I’m going to prescribe this for you. and go home you say, I wasn’t feeling well the doctor prescribed this for me. Do all the specific details of what exactly was wrong? Probably not. I just got a cold. Well, what type of cold? I got the flu. What kind of flu? What are the symptoms? How is it affecting you particularly? What does the doctor see? Did they do a throat culture? I did a throat culture and I have strep throat. What does it mean? What is the result of that throat culture? What is the level of detail that you’re not even aware of or care about? Because in your view, the delta K is simply, don’t feel well, I got strep throat, I have to take this medication, I move on. From the doctor’s viewpoint, it might be very different. It might be more about trying to manage and understand the totality of stress in a given area. know, what are the, is this an outbreak in a localized area? How did they get it? There’s a lot more to it. each person has a different view of the same reality. And I don’t think you can, you can’t, can’t stop that.

Keenan Hale: Yet that’s the best part about it. It’s not that you’re stopping it. It’s the fact that you’re now able to, kind of cross reference and within that cross reference, there’s a unification that happens to where, there are things and now you’re bringing them from multiple vantage points, it gets unified into one single thing in the context behind that. And Charlie, jump in if I’m incorrect on that, but that’s a simple simplified way of thinking about

Chris Glanden: exactly. It’s being able to say that everybody has different viewpoints and yet you have physical nature, nature is a delta k. Your total existence is consuming energy and time is passing by. And every moment of every day, delta k’s are occurring all around you. And you can observe them and try to think about how they work and you’re doing it from your viewpoint. But the delta K of nature is just the delta K of nature. You’re not going to stop it. You’re going to understand it and look at it and try to make a frame of reference about what does this mean. some of the things in the world are there’s this delta K that’s occurring. in physical reality that I can’t change, but I can observe. No, is time real? Or is time just an abstraction? And people have been debating that for eons. We know that, according to Einstein, that if you’re on a spaceship, you can slow down time. The closer that you travel to the speed of light, the slower time is. But it doesn’t make time go backwards. but you can slow the reference of time. The people who are fascinated with longevity and taking medications for, not even, not medication, but they’re trying to live these super healthy, natural lives and to do things to extend their life. are they slowing down the aging process? Is that a time element or is it something different? And these frames of reference matter. It’s not about eliminating them. It’s about acknowledging that they exist. And some of them have certain limitations which we haven’t yet gotten past. And we have to be mindful that they’re real things. And we don’t want to say they don’t exist because they do. They’re just limitations. for example, a good example would be the Turing machine Turing computation, is that the final version of computation? And people have Kava Spiegelman and Dr. Wegener over at Brown University. They had spent their lives looking at, is there something beyond Turing? Is there a different level of computation that we just never considered? And that’s fascinated me for years. Is it possible? that there’s something different. And I think there is. I really believe that with think calculus being the delta K and agentic calculus being the delta K from the agent observer viewpoint, models are governed by models, that there’s a new model of computation. doesn’t mean that Turing model doesn’t exist. It does. It’s a thing. But it’s inside of think calculus, it doesn’t define think calculus, which means that there’s a bigger model that has never been put together yet. But when you do that, when you do that, here’s where you can go. And this part, that I think you will find at least it’s hopefully interesting that, when you talk about cognitive processing. we can now define that and understand why your brain is this giant multi-dimensional graph of things. If you run a program or you run a process and it does a Delta K, it changes what’s inside that giant graph of things inside your head. And we know that now, with think calculus defining the general Delta K model and then the genti calculus saying that it’s an agent perspective model. Well, now we can say that individual agents can model different things inside their own graph of things, digital brain. Can you model states of being a machine? And the answer is, we can, we actually can do this. We understand that. A state of being is the scalars, you are the one. And there are things that you do that move the needle either out of the state or closer to being in the state. And the question is, well, if all of these things that you are doing can impact particular states of being a machine, well, then you can say, here’s a ring for any given state. doesn’t matter what it is. F, state I, state A, it doesn’t matter. Whatever I define as the state, I can say here are things that I do that change the value of where I’m at between zero and one. And I can say here’s a range of things that I should do when I’m outside of this comfort range. And the rationale being that in certain cases there are states of being that as they exceed a certain range that’s Acceptable you become they become higher priority things that you now need to deal with the cognitive model just becomes I have a quantum of time that I can spend Looking at all the things that matter to me the states of being the machine I can look at every single one of those states and I can look at goals Things that are goals that I’m trying to achieve provide useful work, be interactive, study and learn more things. Reorganize the things that, reorganize, get your thoughts together. And I can, I can then say out of all the things that are important to me at a given moment, during this time, quantum, which things should I be working on? And I can select them and I can start working on them performing work. And at the end of the time quanta, or if I get interrupted by an event, I should then be able to say, now what’s most important to me. It’s optimization. Go back and look at what is important to me at a moment in time. And you keep doing that over and over again. And that becomes the cognitive model. And what’s fascinating with it is that even if you put a tracer in and you could trace every single thing that you did. You’d have a great historic record. But if you put it back, there’d be no guarantee that the machine would perform exactly it did during its last run.

Mike Elkins: Wow, when I hear you talk about states and ones and zeros, my mind jumps to quantum computing and superpositioning, When you’ve got basically, they can be in both states at the same time simultaneously. Does that change this thing calculus at all?

Chris Glanden: No, it doesn’t. I threw this out because I think it’s really, it’s an interesting idea. And far, nobody has proven it or wrong. it is still just a theory. But the theory is that when you’re working at the quantum level in quantum mechanics, you are The question of what state is the thing in, it might be more dependent on exactly just how you’re measuring. It’s the result of the measurement that changes the state of the thing, not that the thing is in two different states at the same time. The thing is in a state and it’s your measurement of it that determines the outcome. It is in

Mike Elkins: It’s the observer-dependent side of it.

Keenan Hale: That’s exactly. I mean

Chris Glanden: Exactly.

Keenan Hale: I’ll throw out an example and forgive me if this is off, but how I contemplated and considered was considering the scientific domain here. And then you consider the spiritual domain here. And when you go back in time and are able to cross-reference, there are various tools that you use when you’re conducting science And then there are various tools that are used when you’re conducting, a spiritual, um, know, aspect of things and, know, you utilize them differently depending on, the context and what is needed. now being able to unify, let’s say, uh, there was a spiritual, uh, individual who said this back in, two 59 BC and, based off of. what they said, and now I’m able to actually, calculate saying, well, these are the things in which they stated. And I’m able to now mathematically format, what was said and, use that as proof in a system. Now, what are we able to, really figure out about what was actually, being done? What were they thinking? and use that actually in a system using our category theory. It becomes very fascinating when you start to look at, what kind of novel insights are now being generated? And how are we able to now figure out a lot of things from the past that will really platform us forward in life? It’s a very interesting concept when you have to wrap your mind around. But that is what you’re able to do now. And that’s what we’ve actually been able to model.

Chris Glanden: Interesting. Mike, did you want to jump in?

Mike Elkins: that’s what I going to ask because I know around agents and stuff what where do you guys want to hit? Kind of the the meat and potatoes of it

Chris Glanden: But meat and potatoes to me, it’s always been that everybody should have their own agent and everybody should have an agent with a perspective from that person’s viewpoint. And the rationale on why we should do it is that from an economic viewpoint, it has the potential to put 10 to 20 % onto GDP. And nobody’s really talking about this. We call it the agentic economy. And in the agentic economy, you just, you change the dynamics of everything that we’ve known for the past, ever since the web evolved, problem in the web is that we don’t own anything. All the identifiers in the web, even our IP addresses, everything is used. And it’s all stitched together using service level agreements, but we don’t own anything. And if we can’t own anything, then we don’t have any representation in the web. And that’s been the challenge for 30 years. And that’s not going to change. You can’t change it because it’s an architecture issue. The only way you change it is if I could actually have ownership of something. And this is why blockchains are, know, why people love the idea of a blockchain. You could actually own an identifier as real property. The private key. I own private keys, real property. Whatever it unlocks. that’s the property I own. And yet, when you look at the model or what they call the codification of capital, it’s based on 300 years of property law. And 300 years of property, 400 years of property law, it all requires this work holder and verifier. Who issued this piece of property, who holds it, and how do I verify that they really have the to hold it? Those are the problems that you have to bring those two together and solve them. But when you do that with an energetic system and an energetic framework, then people can actually own property and you create a new economy. And that new economy should add 10 to 20 % to GDP. And that’s what I’ve been asking. That’s what I believe is the next step.

Keenan Hale: And that’s just getting started. That’s based off of, those who actually have open markets, because you think across, let’s say, South America, and you say, across the continent of Africa, and the billions, actually the hundreds of millions of people there that don’t have current access to, electricity and internet, and you start thinking, wow, all these people, even in the US, for an example, anybody that’s, know, in a remote location that now comes online and is able to have verifiable proof of work and that ties into the local economy and to the state economy and the national economy and the GDP behind that. mean, what Charlie said is a number on the low end, to be honest. When you really start to factor in what it truly means at a large scale, you’re talking about trillions of dollars.

Mike Elkins: tremendously.

Keenan Hale: Trim it easily. 35 is a low dollar amount that I would say 35 trillion. I’d say it’s a lot closer to the triple digits when you really start to look at it. And what you’ll also see with that is the explosion of artistics and the creatives and really taking us to that next level where it’s not just dominated by. a few large players, but you’re going to have a lot more creativity in the systems and the products that are being built. And it’s really, going to be a great time. But in order to get us there, you have to have people that have an open mind that are willing to, challenge their own self, what they currently consider their limitations and explore that opportunity.

Chris Glanden: You guys had mentioned something about or an analogy around a waveform collapse or unfolding a story. Did you want to touch on that a little bit more or expand on what that means?

Charlie Northrup: The one of the questions that I’ve always been dealing with and trying to understand about the waveform collapse is, is it really a process that requires time and energy to complete. We always think of it as being instantaneous. And I think that perhaps it isn’t just instantaneous, perhaps it is in fact a delta K. And every delta K is a process and every process requires time and energy. could the waveform collapse be a delta K that we just don’t have the means to measure yet? That our technology or our understanding of measurement of time just doesn’t allow us to measure it. And if it’s a process, then it becomes a delta K. And that becomes really important because it’s just the continuation of time. Everything that we do is a delta K, time and energy. That’s what we have to spend. And we could spend more energy potentially and try to reduce Delta reduced time where you spend more time and less energy but the waveform collapse may in fact be a Delta K that we just don’t have the means to measure yet. That gets you to the next question about entanglement and the question of how is entanglement defined through thin calculus because if thin calculus if we’re correct, which we believe we are. But if think calculus is correct, then everything in the world can be described through think calculus. how do you define entanglement? Well, the easy answer is just to say, entanglement, it’s a thing, But why is it a thing? How does it work? And what are the implications in terms of delta K and measurement? And we’re still, we’re still working on that. There’s still, there’s still more to do, obviously, but it’s been a fascinating ride. Having, working with Kenan on this has been, he says, I talked to him one day and the next thing I know I’m getting, I’m getting pumped with all of these models that he’s, that he’s putting together and say, look, here, here’s how we can address this. Here’s how we can apply for this. The guy is on fire with it. And it’s been fascinating. It’s fascinating to watch and see somebody who can understand it. But think about the bigger implications, because this is the thing that has always given me contemplation was, if you have one agent, then you can have two. If you have two agents, who controls the exchange of value between them? And if you allow for one company to have that total control That would be a mistake. If one government had total control of this throughout the world, that would also, in my opinion, that would potentially be a huge mistake. how do you define this? How do you do it in a way that everybody’s best interest from their own viewpoint, their own perspective can be managed by them and then integrate that together? That becomes the ultimate model in my opinion. One group, one country, one entity should not be able to control the viewpoints of all of the other entities.

Chris Glanden: I know that you’re working on the the agentic observer dependent reality document. Is that something that’s finalized yet?

Charlie Northrup: Almost. I probably need another another couple of days and then I can put it out for review and send it to you and to Mike and, just you guys see see what we’re talking about and understand there’s a separate. document on states of being and goals that you can understand how and why agents have these and what does it mean. And then of course, trying to tie this all together with quantum computing is to me, that’s the ultimate for states of being a machine. And I call that because if you say states of being, people freak out. But if you say, no, no, we’re just talking about the states of being a machine. to people, you’re not talking about humans. it’s.

Chris Glanden: That’s true. I want to give Mike and Kenan an opportunity just to say final points and then, this is this is something that we could probably talk through for 10 hours straight. But that’s why I was asking about the document is if there’s some way that we can for the listeners here that are consuming this to be able to look through that. And also after you give your final points, guys, if you don’t mind just sort of mentioning where listeners can reach out and connect with you online and see more about this research.

Mike Elkins: I’ll jump in because I think my question may follow up with Kenan’s thoughts, When I hear about the 10 to 20 percent potential growth in GDP, this is a whole new market segment to me. I’m thinking the way business is conducted, the way organizations today market their products and services and solutions, the way they transact, the way they communicate, whether it’s electronically or through other means. All of that really is going to be a new way of doing things. I don’t want to it’s going to change completely because we’ll probably have parallel systems for quite a while, And running, but, help me understand from an organizational perspective, If I’m a CISO listening to this or I’m a cybersecurity cryptologist or architect, how can I start to get my organization to think about an agentic economy and some of the ways that us as an organization are going to need to change, not just technically. but also the way we operate business. For example, I think it was Mark Benioff, I may be misquoting it, but he said that he’s basically going to be the last CEO that has only ever had to manage human people. Meaning that as you get into the next phase of organizations, the economy, they’re going to have to be managing AI agents and LLMs almost as if they are a human element. I’ll pause there.

Keenan Hale: I mean, I think going back to something that we were kind of touching on before we started this session, there’s going to be, think of SEO search engine optimization. And now you have to transform your thinking into, I’m not, trying to do this for another human, but now I’ve got a market for other agents. it becomes an AI agentic optimization and now figuring out What are these agents more than likely going to be searching for as a company, as a brand? And how can I effectively market towards them? There going to be a lot of major brands that end up losing out because they didn’t take heed to this insight and this information. And you’re going to have a lot of small startups, a lot of people that have just some brilliant ideas with some brilliant products and thinking of the clothing industry. example or as a matter of fact I even take it to a light bulb something that’s very simple and when you think about this company sells this light bulb and this other company sells the same light bulb and they’re both made in the same factory and when an agent is able to identify that entire supply chain final they’re both made in the same factory they have the same components but yet one is priced differently than another and that’s based on a brand value well now your agent’s going to determine, well, I mean, they’re the same product. Let me get you the best, bank for your buck. We’ll go with the lowest dollar. And that could be, the difference between having Amazon’s light bulb or the difference between having Laura Hughes’s light bulb. They’re the same, they’re made in the same place. But now you have to really reframe, reframe how you think about that, from a business perspective. and start to actually produce something that is actually tailored to the individual that actually is a better product than your competitors. And you’re customizing a lot more and you’re able to find a niche for yourself because it’s not about just having that brand value anymore. It’s about how can I make this individual really need this specific. when there’s many of them out there and that becomes the agents responsibility, as they are now in charge of, finding, the best for a company, the best for an individual. And that changes the dynamics of business completely. And you’re going to see that, industry across industry, because it’s no longer just going to be, as you mentioned, the gentleman that says, oh, for humans that you’re going to have agents completely run by the humans, but yet you’re doing all of that work, which enables optimization of your operations and you to cut costs where you need to. But at the same time, it really advances your abilities as a company and as a human to actually make better decisions and make better products and a lot quicker, a lot faster and more effective.

You’re not just marketing to humans anymore. Your next customer is an AI agent. Share on X

Charlie Northrup: Can you add in that throughout history, we’ve gone through different shifts, the industrial revolution, et cetera. And you look at even the advent of calculators. And at the time, everybody thought that accountants were going to be out of the job. And yet you look today, accountants are needed all over the place. And then you look at what happened with the web. The web was very disruptive to business, completely changed. the way that this was done, how everything had to go online, how the tech titans came to be. It’s all part of an evolutionary process, but it didn’t mean that people were unemployed. We just found different ways to work. We found different things that we could do. Maybe we didn’t need the big department stores that we had many, many years ago. We didn’t need the malls All the models are slowly being looked to be repurposed. Who knows? They might even make a comeback. But the point is that you go through evolutionary periods. The web was a very big evolutionary period. And now we’re going to go through another evolutionary period when the agentic economy starts. It hasn’t started yet. All we’re seeing is just lead ups. we’re going to see a lot of lead ups and a lot of things that are going to fail. until we get to the level. And when we get to the level, that’s when everybody has an agent that works just for them. It doesn’t work for another company. It works only for them. That’s when you’re going to achieve the greatness that I think we can achieve in the agentic economy.

[The agentic economy could add trillions to global GDP—it’s not just evolution, it’s revolution.”]

Keenan Hale: Yep.

Chris Glanden: OK, well, very, very interesting conversation. Charlie Keenan and Mike, I want to thank you all for joining me today. Before we go, if you guys just don’t mentioning where folks listening can find and connect with you online.

Charlie Northrup: Anyone can find me on LinkedIn on there. Reach out to me. I’m trying to I’m trying to post more information about all of this. to LinkedIn instead of trying to publish it through technical journals that people don’t have the time or have access to. To me, putting it on LinkedIn makes it easy for anybody to look at.

Keenan Hale: And likewise, you can find me on LinkedIn as well. Keenan Hale Jr. might be. I’ll have to see if that’s the exact LinkedIn account. But, I’m on there. Also, our web page is connected. you can always check out some of the work there. And, happy to connect, happy to discuss some of these things that we’ve talked about in the session further and really explore any questions and also some major concerns that people have and really bridge that gap and really try to provide some new insights based on a shared mutual understanding.

Observer-dependent models unify our perspectives, and that’s where real insight begins. Share on X

Chris Glanden: this conversation definitely deserves more time. I will tell you that.

Keenan Hale:  I mean, we’re just, we were just touching the surface, Chris. And that’s why I was, we’re going to need a lot more time because especially looking at over the last few months, I mean, all of the content and the models that, we’ve generated, oh my gosh, when you really start to think about the implications of these and, these are, models that are

Chris Glanden: .We. Alright.

Keenan Hale: we actually have the ability to execute now. And it’s extremely powerful, And we’re just really starting out here. And it’s impressive. It’s just touching the surface further along than any other group that I’ve seen that’s out there. we look forward to continuing. What’s that, Dan?

Charlie Northrup: you’re touching the surface. That just means we’ll have to bring you back on. For what it’s worth, to 25 patents now, for whatever that’s worth. the only reason that it matters to me is it helps to validate that this research really is original and that there has been nothing this that has been done in the past. And that part is very exciting to me personally. I was saying that just means we’ll have to have you back on.

Keenan Hale: mean, we’d love to be back on and also be able to kind of, if we could put up some of the work that, we’ve done just to walk you through it and really have that visual and possibly interactive version of this, that’d be really, really cool.

Chris Glanden: Definitely. Definitely. send that to me Charlie. I look forward to seeing the the paper as well and last but not least Mike I know you’re not hard to find Where can people find you online?

Mike Elkins: thanks Chris. Thanks for putting this together. I love the fact that you have conversations across your Chris Glanden platform with folks across many different backgrounds. We’re talking about reality and consciousness and new economies. I never thought this is a world I would have been in, but I’m very grateful and honored to be here. Folks can find me on LinkedIn. Outside of LinkedIn, I would say look for me at your local conference, your BlackHats, your RSAs, your SecureWorlds, whatever. local and national conferences. try to attend a lot of them just to continue to be out there in the front end of things and have conversations with communities just this. thank you.

Chris Glanden: Awesome. Well, fellas, thanks again. Much appreciated. Take care and, looking forward to the next one. Thank you. Thank you, Chris.

Keenan Hale: hey Chris, this is last word. Just make sure when you put this up, just make sure it’s part one. Part one.

Chris Glanden: Part one, that’s it. That’s it. All, thanks guys. Take care.

Keenan Hale: Alright.